[MRG+1] correct comparison in GaussianNB for 'priors' (scikit-learn#10005)

Author: gxyd

sklearn/naive_bayes.py

@@ -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():

sklearn/tests/test_naive_bayes.py

@@ -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"""