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DOC Ensures LabelEncoder passes numpydoc validation #20456

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Jul 6, 2021
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DOC Ensures LabelEncoder passes numpydoc validation #20456

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8fd7a05
fix LabelEncoder DOCS
gloriamacia Jul 3, 2021
4847136
remove trailing spaces
glemaitre Jul 6, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,6 @@
"KernelPCA",
"KernelRidge",
"LabelBinarizer",
"LabelEncoder",
"LabelPropagation",
"LabelSpreading",
"Lars",
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16 changes: 10 additions & 6 deletions sklearn/preprocessing/_label.py
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Expand Up @@ -49,6 +49,12 @@ class LabelEncoder(TransformerMixin, BaseEstimator):
classes_ : ndarray of shape (n_classes,)
Holds the label for each class.

See Also
--------
OrdinalEncoder : Encode categorical features using an ordinal encoding
scheme.
OneHotEncoder : Encode categorical features as a one-hot numeric array.

Examples
--------
`LabelEncoder` can be used to normalize labels.
Expand Down Expand Up @@ -76,12 +82,6 @@ class LabelEncoder(TransformerMixin, BaseEstimator):
array([2, 2, 1]...)
>>> list(le.inverse_transform([2, 2, 1]))
['tokyo', 'tokyo', 'paris']

See Also
--------
OrdinalEncoder : Encode categorical features using an ordinal encoding
scheme.
OneHotEncoder : Encode categorical features as a one-hot numeric array.
"""

def fit(self, y):
Expand All @@ -95,6 +95,7 @@ def fit(self, y):
Returns
-------
self : returns an instance of self.
Fitted label encoder.
"""
y = column_or_1d(y, warn=True)
self.classes_ = _unique(y)
Expand All @@ -111,6 +112,7 @@ def fit_transform(self, y):
Returns
-------
y : array-like of shape (n_samples,)
Encoded labels.
"""
y = column_or_1d(y, warn=True)
self.classes_, y = _unique(y, return_inverse=True)
Expand All @@ -127,6 +129,7 @@ def transform(self, y):
Returns
-------
y : array-like of shape (n_samples,)
Labels as normalized encodings.
"""
check_is_fitted(self)
y = column_or_1d(y, warn=True)
Expand All @@ -147,6 +150,7 @@ def inverse_transform(self, y):
Returns
-------
y : ndarray of shape (n_samples,)
Original encoding.
"""
check_is_fitted(self)
y = column_or_1d(y, warn=True)
Expand Down