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[MRG+1] correct comparison in GaussianNB for 'priors' #10005

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Merged
TomDLT merged 2 commits into from
Oct 26, 2017
Merged

[MRG+1] correct comparison in GaussianNB for 'priors' #10005

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d179e2e
fixes #9633 correct comparison in GaussianNB for 'priors'
gxyd Oct 25, 2017
183ae55
fix flake8 errors and address changes regarding comments
gxyd Oct 26, 2017
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2 changes: 1 addition & 1 deletion sklearn/naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -374,7 +374,7 @@ def _partial_fit(self, X, y, classes=None, _refit=False,
raise ValueError('Number of priors must match number of'
' classes.')
# Check that the sum is 1
if priors.sum() != 1.0:
if not np.isclose(priors.sum(), 1.0):
raise ValueError('The sum of the priors should be 1.')
# Check that the prior are non-negative
if (priors < 0).any():
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12 changes: 12 additions & 0 deletions sklearn/tests/test_naive_bayes.py
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Expand Up @@ -114,6 +114,18 @@ def test_gnb_priors():
assert_array_almost_equal(clf.class_prior_, np.array([0.3, 0.7]))


def test_gnb_priors_sum_isclose():
# test whether the class prior sum is properly tested"""
X = np.array([[-1, -1], [-2, -1], [-3, -2], [-4, -5], [-5, -4],
[1, 1], [2, 1], [3, 2], [4, 4], [5, 5]])
priors = np.array([0.08, 0.14, 0.03, 0.16, 0.11, 0.16, 0.07, 0.14,
0.11, 0.0])
Y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
clf = GaussianNB(priors)
# smoke test for issue #9633
clf.fit(X, Y)


def test_gnb_wrong_nb_priors():
""" Test whether an error is raised if the number of prior is different
from the number of class"""
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