DOC Ensures that BernoulliNB passes numpydoc validation (#20429)

genvalen · glemaitre · adrinjalali · web-flow · commit e4ef854d0318 · 2021-06-30T22:03:41.000-04:00
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
Co-authored-by: Adrin Jalali &lt;adrin.jalali@gmail.com&gt;
Co-authored-by: Olivier Grisel &lt;olivier.grisel@ensta.org&gt;
Co-authored-by: Thomas J. Fan &lt;thomasjpfan@gmail.com&gt;
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -13,7 +13,6 @@
     "AffinityPropagation",
     "AgglomerativeClustering",
     "BaggingRegressor",
-    "BernoulliNB",
     "BernoulliRBM",
     "Birch",
     "CCA",
diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
@@ -71,11 +71,12 @@ def predict(self, X):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
+            The input samples.
 
         Returns
         -------
         C : ndarray of shape (n_samples,)
-            Predicted target values for X
+            Predicted target values for X.
         """
         check_is_fitted(self)
         X = self._check_X(X)
@@ -89,6 +90,7 @@ def predict_log_proba(self, X):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
+            The input samples.
 
         Returns
         -------
@@ -111,6 +113,7 @@ def predict_proba(self, X):
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
+            The input samples.
 
         Returns
         -------
@@ -203,7 +206,7 @@ def __init__(self, *, priors=None, var_smoothing=1e-9):
         self.var_smoothing = var_smoothing
 
     def fit(self, X, y, sample_weight=None):
-        """Fit Gaussian Naive Bayes according to X, y
+        """Fit Gaussian Naive Bayes according to X, y.
 
         Parameters
         ----------
@@ -576,6 +579,7 @@ def partial_fit(self, X, y, classes=None, sample_weight=None):
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         first_call = not hasattr(self, "classes_")
         X, y = self._check_X_y(X, y, reset=first_call)
@@ -622,7 +626,7 @@ def partial_fit(self, X, y, classes=None, sample_weight=None):
         return self
 
     def fit(self, X, y, sample_weight=None):
-        """Fit Naive Bayes classifier according to X, y
+        """Fit Naive Bayes classifier according to X, y.
 
         Parameters
         ----------
@@ -639,6 +643,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         X, y = self._check_X_y(X, y)
         _, n_features = X.shape
@@ -1049,18 +1054,13 @@ class BernoulliNB(_BaseDiscreteNB):
 
         .. versionadded:: 0.24
 
-    Examples
+    See Also
     --------
-    >>> import numpy as np
-    >>> rng = np.random.RandomState(1)
-    >>> X = rng.randint(5, size=(6, 100))
-    >>> Y = np.array([1, 2, 3, 4, 4, 5])
-    >>> from sklearn.naive_bayes import BernoulliNB
-    >>> clf = BernoulliNB()
-    >>> clf.fit(X, Y)
-    BernoulliNB()
-    >>> print(clf.predict(X[2:3]))
-    [3]
+    CategoricalNB : Naive Bayes classifier for categorical features.
+    ComplementNB : The Complement Naive Bayes classifier
+        described in Rennie et al. (2003).
+    GaussianNB : Gaussian Naive Bayes (GaussianNB).
+    MultinomialNB : Naive Bayes classifier for multinomial models.
 
     References
     ----------
@@ -1074,6 +1074,19 @@ class BernoulliNB(_BaseDiscreteNB):
 
     V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with
     naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.RandomState(1)
+    >>> X = rng.randint(5, size=(6, 100))
+    >>> Y = np.array([1, 2, 3, 4, 4, 5])
+    >>> from sklearn.naive_bayes import BernoulliNB
+    >>> clf = BernoulliNB()
+    >>> clf.fit(X, Y)
+    BernoulliNB()
+    >>> print(clf.predict(X[2:3]))
+    [3]
     """
 
     def __init__(self, *, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None):
@@ -1226,7 +1239,7 @@ def __init__(
         self.min_categories = min_categories
 
     def fit(self, X, y, sample_weight=None):
-        """Fit Naive Bayes classifier according to X, y
+        """Fit Naive Bayes classifier according to X, y.
 
         Parameters
         ----------
@@ -1248,6 +1261,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         return super().fit(X, y, sample_weight=sample_weight)
 
@@ -1291,6 +1305,7 @@ def partial_fit(self, X, y, classes=None, sample_weight=None):
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         return super().partial_fit(X, y, classes, sample_weight=sample_weight)
 
