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mladenk42 opened this issue Nov 13, 2015 · 8 comments · Fixed by #9131
Closed

Multinomial Bayes issue #5814

mladenk42 opened this issue Nov 13, 2015 · 8 comments · Fixed by #9131
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0.19

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@mladenk42
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Given the digits dataset (available in sklearn.datasets), we split it into train and test set.
We fit a MultinomialNB classifier on the train set, and generate predictions on that same train
set. When this is done without smoothing, the classification performance is rather low.
This is contrary to expectations, as the classifier has already seen all possible feature values.
So not including smoothing shouldn't really make such a big difference.

It seems the BernoulliNB class from sklearn also has this problem.

My inefficient but straightforward implementation performs expectedly well on the training set.
Code to reproduce the issue with some more tests is available at http://pastebin.com/2hsrA8xL
I hope the issue is not caused by some trivial implementation detail I've overlooked.
@agramfort
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agramfort commented Nov 14, 2015 via email

@mladenk42
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Might be a silly question but where do i send it :)? The scikit-learn-issues mailing list?
Thanks.

@agramfort
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agramfort commented Nov 14, 2015 via email

@mladenk42
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Since it's my first time submitting an error report I'm completely lost :(

It says here that PR-s are used to tell others about changes I pushed to a repository. I didn't push anything O_o, nor do I want to. The demo script I submitted (via pastebin link) doesn't fix the issue, or change anything in sklearn code. It's just there to reproduce the issue, so someone who knows the sklearn code can debug it more easily. Perhaps I'm misunderstanding something.

@absolutelyNoWarranty
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It seems when alpha=0.0 and the data has features which never change, feature_log_prob_ has -inf which causes the calculations down the line to become all nan.

Given
X = np.array([[1, 0],[1, 1]])
y = np.array([0, 1])

Compare

nb = BernoulliNB(alpha=0.)
nb.fit(X, y)

nb.predict_proba(X)
Out[135]:
array([[ nan,  nan],
       [ nan,  nan]])

nb.feature_log_prob_
Out[139]:
array([[  0., -inf],
       [  0.,   0.]])

and

nb = BernoulliNB(alpha=1e-15)
nb.fit(X, y)
nb.predict_proba(X)

Out[136]:
array([[  1.0000e+00,   8.8818e-16],
       [  1.0000e-15,   1.0000e+00]])

@amueller amueller added the Bug label Sep 14, 2016
@amueller
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We should probably throw an error when that happens if that creates issues.

@amueller amueller added this to the 0.19 milestone Sep 14, 2016
yl565 added a commit to yl565/scikit-learn that referenced this issue Sep 23, 2016
@yl565
Fix scikit-learn#5814
4ce3e8f
@yl565 yl565 mentioned this issue Sep 23, 2016
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@yl565
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yl565 commented Sep 24, 2016

@amueller Please see my PR to this issue.

yl565 added a commit to yl565/scikit-learn that referenced this issue Oct 17, 2016
@yl565
Fix scikit-learn#5814
d3bb0ec
herilalaina pushed a commit to herilalaina/scikit-learn that referenced this issue Jun 15, 2017
@yl565 @herilalaina
Fix scikit-learn#5814
ddb5383
@herilalaina herilalaina mentioned this issue Jun 15, 2017
Merged
@jmschrei
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Fixed via #9131

jmschrei pushed a commit that referenced this issue Jun 19, 2017
@herilalaina @jmschrei
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (#…
b4b5de8 
…9131)

* Fix #5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
dmohns pushed a commit to dmohns/scikit-learn that referenced this issue Aug 7, 2017
@herilalaina @dmohns
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (s…
8f4dd76 
…cikit-learn#9131)

* Fix scikit-learn#5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
dmohns pushed a commit to dmohns/scikit-learn that referenced this issue Aug 7, 2017
@herilalaina @dmohns
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (s…
4c89d6a 
…cikit-learn#9131)

* Fix scikit-learn#5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
NelleV pushed a commit to NelleV/scikit-learn that referenced this issue Aug 11, 2017
@herilalaina @NelleV
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (s…
34163ad 
…cikit-learn#9131)

* Fix scikit-learn#5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
paulha pushed a commit to paulha/scikit-learn that referenced this issue Aug 19, 2017
@herilalaina @paulha
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (s…
b47d6c8 
…cikit-learn#9131)

* Fix scikit-learn#5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
AishwaryaRK pushed a commit to AishwaryaRK/scikit-learn that referenced this issue Aug 29, 2017
@herilalaina @AishwaryaRK
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (s…
9a4dba1 
…cikit-learn#9131)

* Fix scikit-learn#5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
maskani-moh pushed a commit to maskani-moh/scikit-learn that referenced this issue Nov 15, 2017
@herilalaina @maskani-moh
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (s…
4fb8495 
…cikit-learn#9131)

* Fix scikit-learn#5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
jwjohnson314 pushed a commit to jwjohnson314/scikit-learn that referenced this issue Dec 18, 2017
@herilalaina
[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) (s…
1da68c1 
…cikit-learn#9131)

* Fix scikit-learn#5814

* Fix pep8 in naive_bayes.py:716

* Fix sparse matrix incompatibility

* Fix python 2.7 problem in test_naive_bayes

* Make sure the values are probabilities before log transform

* Improve docstring of  `_safe_logprob`

* Clip alpha solution

* Clip alpha solution

* Clip alpha in fit and partial_fit

* Add what's new entry

* Add test

* Remove .project

* Replace assert method

* Update what's new

* Format float into %.1e

* Update ValueError msg
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[MRG+1] Fix MultinomialNB and BernoulliNB alpha=0 bug (continuation) herilalaina/scikit-learn
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@agramfort @amueller @absolutelyNoWarranty @mladenk42 @jmschrei @yl565