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DOC make documentation consistent regarding types in _encoders.py #19876

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Apr 13, 2021
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DOC make documentation consistent regarding types in _encoders.py #19876

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33 changes: 19 additions & 14 deletions sklearn/preprocessing/_encoders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -406,7 +406,7 @@ def fit(self, X, y=None):

Parameters
----------
X : array-like, shape [n_samples, n_features]
X : array-like of shape (n_samples, n_features)
The data to determine the categories of each feature.

y : None
Expand All @@ -431,7 +431,7 @@ def fit_transform(self, X, y=None):

Parameters
----------
X : array-like, shape [n_samples, n_features]
X : array-like of shape (n_samples, n_features)
The data to encode.

y : None
Expand All @@ -440,8 +440,10 @@ def fit_transform(self, X, y=None):

Returns
-------
X_out : sparse matrix if sparse=True else a 2-d array
Transformed input.
X_out : {ndarray, sparse matrix} of shape \
(n_samples, n_encoded_features)
Transformed input. If `sparse=True`, a sparse matrix will be
returned.
"""
self._validate_keywords()
return super().fit_transform(X, y)
Expand All @@ -452,13 +454,15 @@ def transform(self, X):

Parameters
----------
X : array-like, shape [n_samples, n_features]
X : array-like of shape (n_samples, n_features)
The data to encode.

Returns
-------
X_out : sparse matrix if sparse=True else a 2-d array
Transformed input.
X_out : {ndarray, sparse matrix} of shape \
(n_samples, n_encoded_features)
Transformed input. If `sparse=True`, a sparse matrix will be
returned.
"""
check_is_fitted(self)
# validation of X happens in _check_X called by _transform
Expand Down Expand Up @@ -522,12 +526,13 @@ def inverse_transform(self, X):

Parameters
----------
X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
X : {array-like, sparse matrix} of shape \
(n_samples, n_encoded_features)
The transformed data.

Returns
-------
X_tr : array-like, shape [n_samples, n_features]
X_tr : ndarray of shape (n_samples, n_features)
Inverse transformed array.
"""
check_is_fitted(self)
Expand Down Expand Up @@ -745,7 +750,7 @@ def fit(self, X, y=None):

Parameters
----------
X : array-like, shape [n_samples, n_features]
X : array-like of shape (n_samples, n_features)
The data to determine the categories of each feature.

y : None
Expand Down Expand Up @@ -814,12 +819,12 @@ def transform(self, X):

Parameters
----------
X : array-like, shape [n_samples, n_features]
X : array-like of shape (n_samples, n_features)
The data to encode.

Returns
-------
X_out : sparse matrix or a 2-d array
X_out : ndarray of shape (n_samples, n_features)
Transformed input.
"""
X_int, X_mask = self._transform(X, handle_unknown=self.handle_unknown,
Expand All @@ -841,12 +846,12 @@ def inverse_transform(self, X):

Parameters
----------
X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The transformed data.

Returns
-------
X_tr : array-like, shape [n_samples, n_features]
X_tr : ndarray of shape (n_samples, n_features)
Inverse transformed array.
"""
check_is_fitted(self)
Expand Down