ServerRun 46349
Creatorsphinx
Programsvmlight-linear
Datasetmytest
Task typeBinaryClassification
Created1y29d ago
Done! Flag_green
4m35s
52M
BinaryClassification
4m6s
0.477
12s
0.480
8s

Log file

===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
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..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..43400..43500..43600..43700..43800..43900..44000..44100..44200..44300..44400..44500..44600..44700..44800..44900..45000..45100..45200..45300..45400..45500..45600..45700..45800..45900..46000..46100..46200..46300..46400..46500..46600..46700..46800..46900..47000..47100..47200..47300..47400..47500..47600..47700..47800..47900..48000..48100..48200..48300..48400..48500..48600..48700..48800..48900..49000..49100..49200..49300..49400..49500..49600..49700..49800..49900..50000..50100..50200..50300..50400..50500..50600..50700..50800..50900..51000..51100..51200..51300..51400..51500..51600..51700..51800..51900..52000..52100..52200..52300..52400..52500..52600..52700..52800..52900..53000..53100..53200..53300..53400..53500..53600..53700..53800..53900..54000..54100..54200..54300..54400..54500..54600..54700..54800..54900..55000..55100..55200..55300..55400..55500..55600..55700..55800..55900..56000..56100..56200..56300..56400..56500..56600..56700..56800..56900..57000..57100..57200..57300..57400..57500..57600..57700..57800..57900..58000..58100..58200..58300..58400..58500..58600..58700..58800..58900..59000..59100..59200..59300..59400..59500..59600..59700..59800..59900..60000..60100..60200..60300..60400..60500..60600..60700..60800..60900..61000..61100..61200..61300..61400..61500..61600..61700..61800..61900..62000..62100..62200..62300..62400..62500..62600..62700..62800..62900..63000..63100..63200..63300..63400..63500..63600..63700..63800..63900..64000..64100..64200..64300..64400..64500..64600..64700..64800..64900..65000..65100..65200..65300..65400..65500..65600..65700..65800..65900..66000..66100..66200..66300..66400..66500..66600..66700..66800..66900..67000..67100..67200..67300..67400..OK. (67424 examples read)
Setting default regularization parameter C=0.1446
Optimizing..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................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................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................done. (20823 iterations)
Optimization finished (32142 misclassified, maxdiff=0.00087).
Runtime in cpu-seconds: 232.88
Number of SV: 65778 (including 65751 at upper bound)
L1 loss: loss=65730.53341
Norm of weight vector: |w|=2.19936
Norm of longest example vector: |x|=4.11316
Estimated VCdim of classifier: VCdim<=82.83616
Computing XiAlpha-estimates...done
Runtime for XiAlpha-estimates in cpu-seconds: 0.02
XiAlpha-estimate of the error: error<=97.54% (rho=1.00,depth=0)
XiAlpha-estimate of the recall: recall=>3.28% (rho=1.00,depth=0)
XiAlpha-estimate of the precision: precision=>3.28% (rho=1.00,depth=0)
Number of kernel evaluations: 2016784
Writing model file...done
=== END program1: ./run learn ../dataset2/train --- OK [246s]

===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Reading model...OK. (65778 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..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..28900..29000..29100..29200..29300..29400..29500..29600..29700..29800..29900..30000..30100..30200..30300..30400..30500..30600..30700..30800..30900..31000..31100..31200..31300..31400..31500..31600..31700..31800..31900..32000..32100..32200..32300..32400..32500..32600..32700..32800..32900..33000..33100..33200..33300..33400..33500..33600..33700..33800..33900..34000..34100..34200..34300..34400..34500..34600..34700..34800..34900..35000..35100..35200..35300..35400..35500..35600..35700..35800..35900..36000..36100..36200..36300..36400..36500..36600..36700..36800..36900..37000..37100..37200..37300..37400..37500..37600..37700..37800..37900..38000..38100..38200..38300..38400..38500..38600..38700..38800..38900..39000..39100..39200..39300..39400..39500..39600..39700..39800..39900..40000..40100..40200..40300..40400..40500..40600..40700..40800..40900..41000..41100..41200..41300..41400..41500..41600..41700..41800..41900..42000..42100..42200..42300..42400..42500..42600..42700..42800..42900..43000..43100..43200..43300..43400..43500..43600..43700..43800..43900..44000..44100..44200..44300..44400..44500..44600..44700..44800..44900..45000..45100..45200..45300..45400..45500..45600..45700..45800..45900..46000..46100..46200..46300..46400..46500..46600..46700..46800..46900..47000..47100..47200..47300..47400..47500..47600..47700..47800..47900..48000..48100..48200..48300..48400..48500..48600..48700..48800..48900..49000..49100..49200..49300..49400..49500..49600..49700..49800..49900..50000..50100..50200..50300..50400..50500..50600..50700..50800..50900..51000..51100..51200..51300..51400..51500..51600..51700..51800..51900..52000..52100..52200..52300..52400..52500..52600..52700..52800..52900..53000..53100..53200..53300..53400..53500..53600..53700..53800..53900..54000..54100..54200..54300..54400..54500..54600..54700..54800..54900..55000..55100..55200..55300..55400..55500..55600..55700..55800..55900..56000..56100..56200..56300..56400..56500..56600..56700..56800..56900..57000..57100..57200..57300..57400..57500..57600..57700..57800..57900..58000..58100..58200..58300..58400..58500..58600..58700..58800..58900..59000..59100..59200..59300..59400..59500..59600..59700..59800..59900..60000..60100..60200..60300..60400..60500..60600..60700..60800..60900..61000..61100..61200..61300..61400..61500..61600..61700..61800..61900..62000..62100..62200..62300..62400..62500..62600..62700..62800..62900..63000..63100..63200..63300..63400..63500..63600..63700..63800..63900..64000..64100..64200..64300..64400..64500..64600..64700..64800..64900..65000..65100..65200..65300..65400..65500..65600..65700..65800..65900..66000..66100..66200..66300..66400..66500..66600..66700..66800..66900..67000..67100..67200..67300..67400..done
Runtime (without IO) in cpu-seconds: 0.10
Accuracy on test set: 61.18% (41250 correct, 26174 incorrect, 67424 total)
Precision/recall on test set: 100.00%/61.18%
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [12s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [5s]

===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [1s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
Reading model...OK. (65778 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..7400..7500..7600..7700..7800..7900..8000..8100..8200..8300..8400..8500..8600..8700..8800..8900..9000..9100..9200..9300..9400..9500..9600..9700..9800..9900..10000..10100..10200..10300..10400..10500..10600..10700..10800..10900..11000..11100..11200..11300..11400..11500..11600..11700..11800..11900..12000..12100..12200..12300..12400..12500..12600..12700..12800..12900..13000..13100..13200..13300..13400..13500..13600..13700..13800..13900..14000..14100..14200..14300..14400..14500..14600..14700..14800..14900..15000..15100..15200..15300..15400..15500..15600..15700..15800..15900..16000..16100..16200..16300..16400..16500..16600..16700..16800..16900..17000..17100..17200..17300..17400..17500..17600..17700..17800..17900..18000..18100..18200..18300..18400..18500..18600..18700..18800..18900..19000..19100..19200..19300..19400..19500..19600..19700..19800..19900..20000..20100..20200..20300..20400..20500..20600..20700..20800..20900..21000..21100..21200..21300..21400..21500..21600..21700..21800..21900..22000..22100..22200..22300..22400..22500..22600..22700..22800..22900..23000..23100..23200..23300..23400..23500..23600..23700..23800..23900..24000..24100..24200..24300..24400..24500..24600..24700..24800..24900..25000..25100..25200..25300..25400..25500..25600..25700..25800..25900..26000..26100..26200..26300..26400..26500..26600..26700..26800..26900..27000..27100..27200..27300..27400..27500..27600..27700..27800..27900..28000..28100..28200..28300..28400..28500..28600..28700..28800..done
Runtime (without IO) in cpu-seconds: 0.03
Accuracy on test set: 60.97% (17619 correct, 11277 incorrect, 28896 total)
Precision/recall on test set: 100.00%/60.97%
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [8s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [2s]


real	4m35.652s
user	4m27.093s
sys	0m4.076s

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