DOC Ensures that LabelBinarizer passes numpydoc validation (scikit-learn#20990)

genvalen · web-flow · commit 304a5e8e165d · 2021-09-09T11:09:37.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -18,7 +18,6 @@
     "IterativeImputer",
     "KBinsDiscretizer",
     "KNNImputer",
-    "LabelBinarizer",
     "LabelPropagation",
     "LabelSpreading",
     "LocalOutlierFactor",
diff --git a/sklearn/preprocessing/_label.py b/sklearn/preprocessing/_label.py
@@ -188,7 +188,6 @@ class LabelBinarizer(TransformerMixin, BaseEstimator):
 
     Parameters
     ----------
-
     neg_label : int, default=0
         Value with which negative labels must be encoded.
 
@@ -201,7 +200,6 @@ class LabelBinarizer(TransformerMixin, BaseEstimator):
 
     Attributes
     ----------
-
     classes_ : ndarray of shape (n_classes,)
         Holds the label for each class.
 
@@ -215,6 +213,13 @@ class LabelBinarizer(TransformerMixin, BaseEstimator):
         True if the input data to transform is given as a sparse matrix, False
         otherwise.
 
+    See Also
+    --------
+    label_binarize : Function to perform the transform operation of
+        LabelBinarizer with fixed classes.
+    OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
+        scheme.
+
     Examples
     --------
     >>> from sklearn import preprocessing
@@ -248,13 +253,6 @@ class LabelBinarizer(TransformerMixin, BaseEstimator):
            [0, 1, 0],
            [0, 0, 1],
            [0, 1, 0]])
-
-    See Also
-    --------
-    label_binarize : Function to perform the transform operation of
-        LabelBinarizer with fixed classes.
-    OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
-        scheme.
     """
 
     def __init__(self, *, neg_label=0, pos_label=1, sparse_output=False):
@@ -288,7 +286,8 @@ def fit(self, y):
 
         Returns
         -------
-        self : returns an instance of self.
+        self : object
+            Returns the instance itself.
         """
         self.y_type_ = type_of_target(y)
         if "multioutput" in self.y_type_:
@@ -303,8 +302,7 @@ def fit(self, y):
         return self
 
     def fit_transform(self, y):
-        """Fit label binarizer and transform multi-class labels to binary
-        labels.
+        """Fit label binarizer/transform multi-class labels to binary labels.
 
         The output of transform is sometimes referred to as
         the 1-of-K coding scheme.
