Use scipy.special.xlogy to avoid indefinite limit in 0 for x*log(y) #12915
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rth
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Jan 3, 2019
| @@ -297,7 +297,7 @@ def test_multilabel_classification(): | |||
| max_iter=150, random_state=0, activation='logistic', | |||
| learning_rate_init=0.2) | |||
| mlp.fit(X, y) | |||
| assert_equal(mlp.score(X, y), 1) | |||
| assert_greater(mlp.score(X, y), 0.97) | |||
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This fails otherwise, as the score is 0.98 in this case.
Testing that mean test accuracy is exactly 1 on this random dataset with those parameters doesn't sound too robust, and the change of the numerical accuracy in log loss might have been enough to break this condition.
TomDLT
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Jan 3, 2019
qinhanmin2014
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Jan 3, 2019
|
I guess we don't need tests and what's new here. |
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…r x*log(y) (scikit-learn#12915)" This reverts commit 6b476ca.
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…r x*log(y) (scikit-learn#12915)" This reverts commit 6b476ca.
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x*log(y)is undefined when bothxandyare zero,currently we avoid this by clipping
xandyto a very small value (e.g. 1e-10) instead of 0.A cleaner solution is to use
scipy.special.xlogy,which produces the correct limit in 0 and has a comparable performance otherwise.