 | Run 11014 |
| Creator | lavergne |
| Program | wapiti |
| Dataset | jdpa-bnp-pos BIO |
| Task type | SequenceTagging |
| Created | 2y177d ago |
Done! 
| 3m52s | |
| 119M | |
| SequenceTagging | |
| 0.985 | |
| 0.866 | |
| 0.928 | |
| 0.289 | |
Log file
RM: wapiti
CC: wapiti.c --> wapiti
src/rprop.c: In function ‘trn_rprop’:
src/rprop.c:155: warning: dereferencing type-punned pointer will break strict-aliasing rules
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
4
* Load patterns
* Load training data
* Initialize the model
* Summary
nb train: 361
nb labels: 3
nb blocks: 325599
nb features: 976806
* Train the model with rprop
[ 1] obj=254913.21 act=546064 err= 7.77%/99.17% time=0.92s/0.92s
[ 2] obj=147180.48 act=51346 err=92.11%/100.00% time=0.92s/1.84s
[ 3] obj=249969.27 act=548983 err= 7.77%/99.17% time=0.94s/2.78s
[ 4] obj=79135.40 act=73649 err= 9.29%/100.00% time=0.92s/3.70s
[ 5] obj=92243.84 act=285808 err= 7.46%/99.17% time=0.92s/4.62s
[ 6] obj=51598.53 act=99052 err= 4.51%/99.72% time=0.94s/5.56s
[ 7] obj=53049.76 act=167138 err= 6.66%/99.17% time=0.94s/6.50s
[ 8] obj=42134.86 act=107226 err= 3.52%/100.00% time=0.94s/7.44s
[ 9] obj=42360.02 act=134522 err= 5.73%/98.89% time=0.92s/8.36s
[ 10] obj=36715.24 act=109298 err= 4.71%/99.17% time=0.92s/9.28s
[ 11] obj=34185.69 act=114116 err= 3.76%/98.34% time=0.94s/10.22s
[ 12] obj=32515.89 act=112112 err= 4.69%/99.17% time=0.93s/11.15s
[ 13] obj=31787.44 act=122855 err= 2.37%/96.95% time=0.93s/12.08s
[ 14] obj=31379.16 act=115349 err= 3.78%/98.89% time=0.93s/13.01s
[ 15] obj=30475.85 act=117315 err= 2.66%/97.23% time=0.93s/13.94s
[ 16] obj=29965.51 act=114042 err= 3.15%/98.61% time=0.94s/14.88s
[ 17] obj=29573.13 act=115896 err= 2.14%/96.95% time=0.92s/15.80s
[ 18] obj=29337.31 act=114887 err= 2.90%/97.23% time=0.92s/16.72s
[ 19] obj=29115.36 act=116089 err= 1.95%/97.23% time=0.91s/17.63s
[ 20] obj=28965.81 act=114977 err= 2.53%/97.51% time=0.93s/18.56s
[ 21] obj=28793.41 act=115551 err= 2.31%/96.68% time=0.92s/19.48s
[ 22] obj=28632.63 act=115377 err= 2.37%/97.23% time=0.91s/20.39s
[ 23] obj=28512.68 act=115988 err= 1.85%/96.68% time=0.92s/21.31s
[ 24] obj=28423.85 act=116033 err= 2.42%/97.23% time=0.93s/22.24s
[ 25] obj=28376.31 act=116934 err= 1.64%/95.01% time=0.93s/23.17s
[ 26] obj=28365.82 act=116668 err= 2.13%/96.68% time=0.92s/24.09s
[ 27] obj=28297.25 act=116068 err= 1.92%/96.12% time=0.92s/25.01s
[ 28] obj=28244.05 act=115929 err= 2.00%/95.57% time=0.93s/25.94s
[ 29] obj=28194.16 act=116059 err= 1.96%/96.40% time=0.91s/26.85s
[ 30] obj=28146.94 act=116158 err= 1.91%/96.40% time=0.92s/27.77s
[ 31] obj=28108.59 act=116293 err= 1.86%/96.40% time=0.93s/28.70s
[ 32] obj=28076.51 act=116421 err= 1.83%/96.12% time=0.92s/29.62s
[ 33] obj=28053.06 act=116593 err= 1.90%/96.12% time=0.92s/30.54s
[ 34] obj=28032.02 act=116722 err= 1.53%/93.63% time=0.91s/31.45s
[ 35] obj=28029.97 act=116801 err= 1.88%/96.68% time=0.92s/32.37s
[ 36] obj=28007.36 act=116559 err= 1.58%/93.91% time=0.92s/33.29s
[ 37] obj=27993.61 act=116497 err= 1.71%/95.57% time=0.91s/34.20s
[ 38] obj=27975.95 act=116471 err= 1.70%/95.01% time=0.93s/35.13s
[ 39] obj=27958.52 act=116482 err= 1.71%/95.84% time=0.92s/36.05s
[ 40] obj=27941.93 act=116542 err= 1.68%/95.01% time=0.92s/36.97s
[ 41] obj=27925.74 act=116590 err= 1.67%/95.29% time=0.92s/37.89s
[ 42] obj=27910.33 act=116636 err= 1.67%/95.57% time=0.92s/38.81s
[ 43] obj=27895.36 act=116683 err= 1.59%/95.57% time=0.93s/39.74s
[ 44] obj=27883.09 act=116733 err= 1.75%/96.40% time=0.93s/40.67s
[ 45] obj=27877.35 act=116943 err= 1.45%/93.91% time=0.92s/41.59s
[ 46] obj=27874.95 act=116900 err= 1.62%/95.57% time=0.92s/42.51s
[ 47] obj=27863.05 act=116735 err= 1.53%/94.18% time=0.94s/43.45s
[ 48] obj=27855.64 act=116742 err= 1.56%/94.46% time=0.92s/44.37s
[ 49] obj=27846.44 act=116732 err= 1.56%/94.74% time=0.93s/45.30s
[ 50] obj=27838.29 act=116772 err= 1.57%/95.01% time=0.92s/46.22s
[ 51] obj=27829.73 act=116786 err= 1.55%/95.01% time=0.93s/47.15s
[ 52] obj=27822.39 act=116805 err= 1.61%/95.57% time=0.92s/48.07s
[ 53] obj=27815.95 act=116853 err= 1.44%/93.91% time=0.92s/48.99s
[ 54] obj=27812.30 act=116874 err= 1.55%/94.74% time=0.92s/49.91s
[ 55] obj=27806.73 act=116857 err= 1.48%/94.18% time=0.92s/50.83s
[ 56] obj=27802.26 act=116811 err= 1.52%/93.91% time=0.92s/51.75s
[ 57] obj=27796.62 act=116813 err= 1.52%/94.46% time=0.92s/52.67s
[ 58] obj=27791.10 act=116831 err= 1.51%/94.46% time=0.92s/53.59s
[ 59] obj=27785.43 act=116838 err= 1.50%/94.74% time=0.92s/54.51s
[ 60] obj=27780.29 act=116864 err= 1.49%/95.01% time=0.92s/55.43s
[ 61] obj=27774.83 act=116851 err= 1.53%/95.29% time=0.92s/56.35s
[ 62] obj=27769.48 act=116917 err= 1.45%/95.01% time=0.92s/57.27s
[ 63] obj=27766.95 act=116913 err= 1.54%/95.57% time=0.92s/58.19s
[ 64] obj=27764.16 act=116974 err= 1.42%/95.01% time=0.92s/59.11s
[ 65] obj=27761.56 act=116897 err= 1.48%/95.01% time=0.91s/60.02s
[ 66] obj=27758.12 act=116890 err= 1.48%/95.01% time=0.92s/60.94s
[ 67] obj=27755.17 act=116888 err= 1.48%/95.29% time=0.94s/61.88s
[ 68] obj=27751.68 act=116905 err= 1.47%/95.29% time=0.92s/62.80s
[ 69] obj=27748.39 act=116902 err= 1.49%/95.01% time=0.91s/63.71s
* Save the model
* Done
=== END program1: ./run learn ../dataset2/train --- OK [165s]
===== 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 [5s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
* Load model
* Label sequences
* Done
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [19s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [18s]
===== 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 [2s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
* Load model
* Label sequences
* Done
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [10s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [8s]
real 3m55.846s
user 1m38.454s
sys 0m0.908s
Run specification 
Results
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