Instructions sur http://researchers.lille.inria.fr/~pdenis/hw-online.txt
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u perc -i 1
Binary Classification model: perc
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 0.357143 time = 0:00:00
done in 0:00:00
done.
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Testing... done in 0.001 sec.
====== ACC: 0.62 (8/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.875 0.636 0.737 | 7 11 8
-1 | 0.2 0.5 0.286 | 1 2 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u perc -i 4
Binary Classification model: perc
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 0.357143 time = 0:00:00
it. 1 14 avg loss = 0.5 time = 0:00:00
it. 2 14 avg loss = 0.5 time = 0:00:00
it. 3 14 avg loss = 0.285714 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.85 (11/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.875 0.875 0.875 | 7 8 8
-1 | 0.8 0.8 0.8 | 4 5 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u perc -i 10
Binary Classification model: perc
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 0.357143 time = 0:00:00
it. 1 14 avg loss = 0.5 time = 0:00:00
it. 2 14 avg loss = 0.5 time = 0:00:00
it. 3 14 avg loss = 0.285714 time = 0:00:00
it. 4 14 avg loss = 0.142857 time = 0:00:00
it. 5 14 avg loss = 0.285714 time = 0:00:00
it. 6 14 avg loss = 0.0 time = 0:00:00
it. 7 14 avg loss = 0.0 time = 0:00:00
it. 8 14 avg loss = 0.0 time = 0:00:00
it. 9 14 avg loss = 0.0 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.69 (9/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.75 0.75 | 6 8 8
-1 | 0.6 0.6 0.6 | 3 5 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u perc -i 100
Binary Classification model: perc
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 0.357143 time = 0:00:00
it. 1 14 avg loss = 0.5 time = 0:00:00
it. 2 14 avg loss = 0.5 time = 0:00:00
it. 3 14 avg loss = 0.285714 time = 0:00:00
it. 4 14 avg loss = 0.142857 time = 0:00:00
it. 5 14 avg loss = 0.285714 time = 0:00:00
it. 6 14 avg loss = 0.0 time = 0:00:00
it. 7 14 avg loss = 0.0 time = 0:00:00
it. 8 14 avg loss = 0.0 time = 0:00:00
it. 9 14 avg loss = 0.0 time = 0:00:00
it. 10 14 avg loss = 0.0 time = 0:00:00
it. 11 14 avg loss = 0.0 time = 0:00:00
it. 12 14 avg loss = 0.0 time = 0:00:00
...
it. 90 14 avg loss = 0.0 time = 0:00:00
it. 91 14 avg loss = 0.0 time = 0:00:00
it. 92 14 avg loss = 0.0 time = 0:00:00
it. 93 14 avg loss = 0.0 time = 0:00:00
it. 94 14 avg loss = 0.0 time = 0:00:00
it. 95 14 avg loss = 0.0 time = 0:00:00
it. 96 14 avg loss = 0.0 time = 0:00:00
it. 97 14 avg loss = 0.0 time = 0:00:00
it. 98 14 avg loss = 0.0 time = 0:00:00
it. 99 14 avg loss = 0.0 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.69 (9/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.75 0.75 | 6 8 8
-1 | 0.6 0.6 0.6 | 3 5 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u perc -i 5000
Binary Classification model: perc
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 0.357143 time = 0:00:00
it. 1 14 avg loss = 0.5 time = 0:00:00
it. 2 14 avg loss = 0.5 time = 0:00:00
it. 3 14 avg loss = 0.285714 time = 0:00:00
it. 4 14 avg loss = 0.142857 time = 0:00:00
it. 5 14 avg loss = 0.285714 time = 0:00:00
it. 6 14 avg loss = 0.0 time = 0:00:00
it. 7 14 avg loss = 0.0 time = 0:00:00
it. 8 14 avg loss = 0.0 time = 0:00:00
it. 9 14 avg loss = 0.0 time = 0:00:00
...
it. 4990 14 avg loss = 0.0 time = 0:00:00
it. 4991 14 avg loss = 0.0 time = 0:00:00
it. 4992 14 avg loss = 0.0 time = 0:00:00
it. 4993 14 avg loss = 0.0 time = 0:00:00
it. 4994 14 avg loss = 0.0 time = 0:00:00
it. 4995 14 avg loss = 0.0 time = 0:00:00
it. 4996 14 avg loss = 0.0 time = 0:00:00
it. 4997 14 avg loss = 0.0 time = 0:00:00
it. 4998 14 avg loss = 0.0 time = 0:00:00
it. 4999 14 avg loss = 0.0 time = 0:00:00
done in 0:00:01
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.69 (9/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.75 0.75 | 6 8 8
-1 | 0.6 0.6 0.6 | 3 5 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 5
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 0.915963 time = 0:00:00
it. 1 14 avg loss = 0.799224 time = 0:00:00
it. 2 14 avg loss = 0.65894 time = 0:00:00
it. 3 14 avg loss = 0.551289 time = 0:00:00
it. 4 14 avg loss = 0.48065 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.92 (12/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.875 1.0 0.933 | 7 7 8
-1 | 1.0 0.833 0.909 | 5 6 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 10
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 0.915963 time = 0:00:00
it. 1 14 avg loss = 0.799224 time = 0:00:00
it. 2 14 avg loss = 0.65894 time = 0:00:00
it. 3 14 avg loss = 0.551289 time = 0:00:00
it. 4 14 avg loss = 0.48065 time = 0:00:00
it. 5 14 avg loss = 0.446881 time = 0:00:00
it. 6 14 avg loss = 0.428057 time = 0:00:00
it. 7 14 avg loss = 0.408499 time = 0:00:00
it. 8 14 avg loss = 0.389525 time = 0:00:00
it. 9 14 avg loss = 0.371429 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.85 (11/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 1.0 0.857 | 6 6 8
-1 | 1.0 0.714 0.833 | 5 7 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 100
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
----------------------------------------------------------------------------------------------------
Training...
it. 0 14 avg loss = 0.915963 time = 0:00:00
it. 1 14 avg loss = 0.799224 time = 0:00:00
it. 2 14 avg loss = 0.65894 time = 0:00:00
it. 3 14 avg loss = 0.551289 time = 0:00:00
it. 4 14 avg loss = 0.48065 time = 0:00:00
it. 5 14 avg loss = 0.446881 time = 0:00:00
it. 6 14 avg loss = 0.428057 time = 0:00:00
it. 7 14 avg loss = 0.408499 time = 0:00:00
it. 8 14 avg loss = 0.389525 time = 0:00:00
it. 9 14 avg loss = 0.371429 time = 0:00:00
it. 10 14 avg loss = 0.354182 time = 0:00:00
...
it. 90 14 avg loss = 0.007753 time = 0:00:00
it. 91 14 avg loss = 0.007391 time = 0:00:00
it. 92 14 avg loss = 0.007046 time = 0:00:00
it. 93 14 avg loss = 0.006718 time = 0:00:00
it. 94 14 avg loss = 0.006404 time = 0:00:00
it. 95 14 avg loss = 0.006105 time = 0:00:00
it. 96 14 avg loss = 0.005821 time = 0:00:00
it. 97 14 avg loss = 0.005549 time = 0:00:00
it. 98 14 avg loss = 0.00529 time = 0:00:00
it. 99 14 avg loss = 0.005043 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.77 (10/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.857 0.8 | 6 7 8
-1 | 0.8 0.667 0.727 | 4 6 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 5000
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
----------------------------------------------------------------------------------------------------
Training...
it. 0 14 avg loss = 0.915963 time = 0:00:00
it. 1 14 avg loss = 0.799224 time = 0:00:00
it. 2 14 avg loss = 0.65894 time = 0:00:00
it. 3 14 avg loss = 0.551289 time = 0:00:00
it. 4 14 avg loss = 0.48065 time = 0:00:00
it. 5 14 avg loss = 0.446881 time = 0:00:00
it. 6 14 avg loss = 0.428057 time = 0:00:00
it. 7 14 avg loss = 0.408499 time = 0:00:00
it. 8 14 avg loss = 0.389525 time = 0:00:00
it. 9 14 avg loss = 0.371429 time = 0:00:00
it. 10 14 avg loss = 0.354182 time = 0:00:00
...
it. 286 14 avg loss = 1e-06 time = 0:00:00
it. 287 14 avg loss = 1e-06 time = 0:00:00
it. 288 14 avg loss = 1e-06 time = 0:00:00
it. 289 14 avg loss = 1e-06 time = 0:00:00
it. 290 14 avg loss = 1e-06 time = 0:00:00
it. 291 14 avg loss = 1e-06 time = 0:00:00
it. 292 14 avg loss = 0.0 time = 0:00:00
it. 293 14 avg loss = 0.0 time = 0:00:00
it. 294 14 avg loss = 0.0 time = 0:00:00
it. 295 14 avg loss = 0.0 time = 0:00:00
it. 296 14 avg loss = 0.0 time = 0:00:00
...
it. 4990 14 avg loss = 0.0 time = 0:00:00
it. 4991 14 avg loss = 0.0 time = 0:00:00
it. 4992 14 avg loss = 0.0 time = 0:00:00
it. 4993 14 avg loss = 0.0 time = 0:00:00
it. 4994 14 avg loss = 0.0 time = 0:00:00
it. 4995 14 avg loss = 0.0 time = 0:00:00
it. 4996 14 avg loss = 0.0 time = 0:00:00
it. 4997 14 avg loss = 0.0 time = 0:00:00
it. 4998 14 avg loss = 0.0 time = 0:00:00
it. 4999 14 avg loss = 0.0 time = 0:00:00
done in 0:00:03
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.77 (10/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.857 0.8 | 6 7 8
-1 | 0.8 0.667 0.727 | 4 6 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 10 -C 0.35
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
----------------------------------------------------------------------------------------------------
Training...
it. 0 14 avg loss = 0.930657 time = 0:00:00
it. 1 14 avg loss = 0.754507 time = 0:00:00
it. 2 14 avg loss = 0.642208 time = 0:00:00
it. 3 14 avg loss = 0.537676 time = 0:00:00
it. 4 14 avg loss = 0.473679 time = 0:00:00
it. 5 14 avg loss = 0.418825 time = 0:00:00
it. 6 14 avg loss = 0.376875 time = 0:00:00
it. 7 14 avg loss = 0.359364 time = 0:00:00
it. 8 14 avg loss = 0.35169 time = 0:00:00
it. 9 14 avg loss = 0.346008 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.92 (12/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.875 1.0 0.933 | 7 7 8
-1 | 1.0 0.833 0.909 | 5 6 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 100 -C 0
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
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Training...
it. 0 14 avg loss = 1.0 time = 0:00:00
it. 1 14 avg loss = 1.0 time = 0:00:00
it. 2 14 avg loss = 1.0 time = 0:00:00
it. 3 14 avg loss = 1.0 time = 0:00:00
it. 4 14 avg loss = 1.0 time = 0:00:00
it. 5 14 avg loss = 1.0 time = 0:00:00
it. 6 14 avg loss = 1.0 time = 0:00:00
it. 7 14 avg loss = 1.0 time = 0:00:00
it. 8 14 avg loss = 1.0 time = 0:00:00
it. 9 14 avg loss = 1.0 time = 0:00:00
...
it. 90 14 avg loss = 1.0 time = 0:00:00
it. 91 14 avg loss = 1.0 time = 0:00:00
it. 92 14 avg loss = 1.0 time = 0:00:00
it. 93 14 avg loss = 1.0 time = 0:00:00
it. 94 14 avg loss = 1.0 time = 0:00:00
it. 95 14 avg loss = 1.0 time = 0:00:00
it. 96 14 avg loss = 1.0 time = 0:00:00
it. 97 14 avg loss = 1.0 time = 0:00:00
it. 98 14 avg loss = 1.0 time = 0:00:00
it. 99 14 avg loss = 1.0 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.38 (5/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.0 0.0 0.0 | 0 0 8
-1 | 1.0 0.385 0.556 | 5 13 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 100 -C 10
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
----------------------------------------------------------------------------------------------------
Training...
it. 0 14 avg loss = 0.915963 time = 0:00:00
it. 1 14 avg loss = 0.799224 time = 0:00:00
it. 2 14 avg loss = 0.65894 time = 0:00:00
it. 3 14 avg loss = 0.551289 time = 0:00:00
it. 4 14 avg loss = 0.48065 time = 0:00:00
it. 5 14 avg loss = 0.446881 time = 0:00:00
it. 6 14 avg loss = 0.428057 time = 0:00:00
it. 7 14 avg loss = 0.408499 time = 0:00:00
it. 8 14 avg loss = 0.389525 time = 0:00:00
it. 9 14 avg loss = 0.371429 time = 0:00:00
...
it. 90 14 avg loss = 0.007753 time = 0:00:00
it. 91 14 avg loss = 0.007391 time = 0:00:00
it. 92 14 avg loss = 0.007046 time = 0:00:00
it. 93 14 avg loss = 0.006718 time = 0:00:00
it. 94 14 avg loss = 0.006404 time = 0:00:00
it. 95 14 avg loss = 0.006105 time = 0:00:00
it. 96 14 avg loss = 0.005821 time = 0:00:00
it. 97 14 avg loss = 0.005549 time = 0:00:00
it. 98 14 avg loss = 0.00529 time = 0:00:00
it. 99 14 avg loss = 0.005043 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.77 (10/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.857 0.8 | 6 7 8
-1 | 0.8 0.667 0.727 | 4 6 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 300 -C 1000
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
----------------------------------------------------------------------------------------------------
Training...
it. 0 14 avg loss = 0.915963 time = 0:00:00
it. 1 14 avg loss = 0.799224 time = 0:00:00
it. 2 14 avg loss = 0.65894 time = 0:00:00
it. 3 14 avg loss = 0.551289 time = 0:00:00
it. 4 14 avg loss = 0.48065 time = 0:00:00
it. 5 14 avg loss = 0.446881 time = 0:00:00
it. 6 14 avg loss = 0.428057 time = 0:00:00
it. 7 14 avg loss = 0.408499 time = 0:00:00
it. 8 14 avg loss = 0.389525 time = 0:00:00
it. 9 14 avg loss = 0.371429 time = 0:00:00
...
it. 287 14 avg loss = 1e-06 time = 0:00:00
it. 288 14 avg loss = 1e-06 time = 0:00:00
it. 289 14 avg loss = 1e-06 time = 0:00:00
it. 290 14 avg loss = 1e-06 time = 0:00:00
it. 291 14 avg loss = 1e-06 time = 0:00:00
it. 292 14 avg loss = 0.0 time = 0:00:00
it. 293 14 avg loss = 0.0 time = 0:00:00
it. 294 14 avg loss = 0.0 time = 0:00:00
it. 295 14 avg loss = 0.0 time = 0:00:00
it. 296 14 avg loss = 0.0 time = 0:00:00
it. 297 14 avg loss = 0.0 time = 0:00:00
it. 298 14 avg loss = 0.0 time = 0:00:00
it. 299 14 avg loss = 0.0 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.77 (10/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.857 0.8 | 6 7 8
-1 | 0.8 0.667 0.727 | 4 6 5
--------------------------------------------------------------------------------
carette@b04p15:~/Documents/A2DI_PDenis_6$ python code-stub/py/polka/classification/binary.py -d code-stub/data/tennis/tennis.train -t code-stub/data/tennis/tennis.test -u pa -i 300 -C 0.25
Binary Classification model: pa
Training on data in 'code-stub/data/tennis/tennis.train'.
----------------------------------------------------------------------------------------------------
Training...
it. 0 14 avg loss = 0.933036 time = 0:00:00
it. 1 14 avg loss = 0.699847 time = 0:00:00
it. 2 14 avg loss = 0.618705 time = 0:00:00
it. 3 14 avg loss = 0.53243 time = 0:00:00
it. 4 14 avg loss = 0.46511 time = 0:00:00
it. 5 14 avg loss = 0.415527 time = 0:00:00
it. 6 14 avg loss = 0.382198 time = 0:00:00
it. 7 14 avg loss = 0.348718 time = 0:00:00
it. 8 14 avg loss = 0.317005 time = 0:00:00
it. 9 14 avg loss = 0.299559 time = 0:00:00
...
it. 287 14 avg loss = 1e-06 time = 0:00:00
it. 288 14 avg loss = 1e-06 time = 0:00:00
it. 289 14 avg loss = 1e-06 time = 0:00:00
it. 290 14 avg loss = 1e-06 time = 0:00:00
it. 291 14 avg loss = 1e-06 time = 0:00:00
it. 292 14 avg loss = 1e-06 time = 0:00:00
it. 293 14 avg loss = 1e-06 time = 0:00:00
it. 294 14 avg loss = 1e-06 time = 0:00:00
it. 295 14 avg loss = 1e-06 time = 0:00:00
it. 296 14 avg loss = 1e-06 time = 0:00:00
it. 297 14 avg loss = 1e-06 time = 0:00:00
it. 298 14 avg loss = 1e-06 time = 0:00:00
it. 299 14 avg loss = 1e-06 time = 0:00:00
done in 0:00:00
done.
----------------------------------------------------------------------------------------------------
Testing... done in 0.001 sec.
====== ACC: 0.77 (10/13) ======
====== Recall/Precision/F1 by class labels ======
--------------------------------------------------------------------------------
Label | Recall Precison F1 | Correct Predicted Gold
--------------------------------------------------------------------------------
1 | 0.75 0.857 0.8 | 6 7 8
-1 | 0.8 0.667 0.727 | 4 6 5
--------------------------------------------------------------------------------