Added private _alpha attribute for testing ease

Micky774 · Micky774 · commit 14a360f040c3 · 2022-06-28T19:08:32.000-04:00
diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
@@ -706,8 +706,8 @@ def partial_fit(self, X, y, classes=None, sample_weight=None):
         # be called by the user explicitly just once after several consecutive
         # calls to partial_fit and prior any call to predict[_[log_]proba]
         # to avoid computing the smooth log probas at each call to partial fit
-        alpha = self._check_alpha()
-        self._update_feature_log_prob(alpha)
+        self._alpha = self._check_alpha()
+        self._update_feature_log_prob(self._alpha)
         self._update_class_log_prior(class_prior=class_prior)
         return self
 
@@ -760,8 +760,8 @@ def fit(self, X, y, sample_weight=None):
         n_classes = Y.shape[1]
         self._init_counters(n_classes, n_features)
         self._count(X, Y)
-        alpha = self._check_alpha()
-        self._update_feature_log_prob(alpha)
+        self._alpha = self._check_alpha()
+        self._update_feature_log_prob(self._alpha)
         self._update_class_log_prior(class_prior=class_prior)
         return self
 
diff --git a/sklearn/tests/test_naive_bayes.py b/sklearn/tests/test_naive_bayes.py
@@ -846,33 +846,36 @@ def test_check_alpha():
     https://github.com/scikit-learn/scikit-learn/issues/10772
     """
     _ALPHA_MIN = 1e-10
+    X = np.array([[2, 1], [1, 1]])
+    y = np.array([0, 1])
     b = BernoulliNB(alpha=0, force_alpha=True)
-    assert b._check_alpha() == 0
+    b.fit(X, y)
+    assert b._alpha == 0
 
     alphas = np.array([0.0, 1.0])
 
     b = BernoulliNB(alpha=alphas, force_alpha=True)
-    # We manually set `n_features_in_` not to have `_check_alpha` err
-    b.n_features_in_ = alphas.shape[0]
-    assert_array_equal(b._check_alpha(), alphas)
+    b.fit(X, y)
+    assert_array_equal(b._alpha, alphas)
 
     msg = (
         "alpha too small will result in numeric errors, setting alpha = %.1e"
         % _ALPHA_MIN
     )
     b = BernoulliNB(alpha=0, force_alpha=False)
     with pytest.warns(UserWarning, match=msg):
-        assert b._check_alpha() == _ALPHA_MIN
+        b.fit(X, y)
+        assert b._alpha == _ALPHA_MIN
 
     b = BernoulliNB(alpha=0)
     with pytest.warns(UserWarning, match=msg):
-        assert b._check_alpha() == _ALPHA_MIN
+        b.fit(X, y)
+        assert b._alpha == _ALPHA_MIN
 
     b = BernoulliNB(alpha=alphas, force_alpha=False)
-    # We manually set `n_features_in_` not to have `_check_alpha` err
-    b.n_features_in_ = alphas.shape[0]
     with pytest.warns(UserWarning, match=msg):
-        assert_array_equal(b._check_alpha(), np.array([_ALPHA_MIN, 1.0]))
+        b.fit(X, y)
+        assert_array_equal(b._alpha, np.array([_ALPHA_MIN, 1.0]))
 
 
 def test_alpha_vector():
