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DOC Fixes numpydoc validation errors in StandardScalar #20368

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Jun 26, 2021
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DOC Fixes numpydoc validation errors in StandardScalar #20368

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f0299c6
DOC fix StandardScaler docstring
NicolasMillerr Jun 26, 2021
81575e8
removed StandardScaler from DOCSTRING_IGNORE_LIST
NicolasMillerr Jun 26, 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 @@ -200,7 +200,6 @@
"SplineTransformer",
"StackingClassifier",
"StackingRegressor",
"StandardScaler",
"TSNE",
"TfidfTransformer",
"TfidfVectorizer",
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40 changes: 20 additions & 20 deletions sklearn/preprocessing/_data.py
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Expand Up @@ -626,7 +626,7 @@ def minmax_scale(X, feature_range=(0, 1), *, axis=0, copy=True):


class StandardScaler(TransformerMixin, BaseEstimator):
"""Standardize features by removing the mean and scaling to unit variance
"""Standardize features by removing the mean and scaling to unit variance.

The standard score of a sample `x` is calculated as:

Expand Down Expand Up @@ -712,23 +712,6 @@ class StandardScaler(TransformerMixin, BaseEstimator):
Will be reset on new calls to fit, but increments across
``partial_fit`` calls.

Examples
--------
>>> from sklearn.preprocessing import StandardScaler
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> scaler = StandardScaler()
>>> print(scaler.fit(data))
StandardScaler()
>>> print(scaler.mean_)
[0.5 0.5]
>>> print(scaler.transform(data))
[[-1. -1.]
[-1. -1.]
[ 1. 1.]
[ 1. 1.]]
>>> print(scaler.transform([[2, 2]]))
[[3. 3.]]

See Also
--------
scale : Equivalent function without the estimator API.
Expand All @@ -748,6 +731,23 @@ class StandardScaler(TransformerMixin, BaseEstimator):
For a comparison of the different scalers, transformers, and normalizers,
see :ref:`examples/preprocessing/plot_all_scaling.py
<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.

Examples
--------
>>> from sklearn.preprocessing import StandardScaler
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> scaler = StandardScaler()
>>> print(scaler.fit(data))
StandardScaler()
>>> print(scaler.mean_)
[0.5 0.5]
>>> print(scaler.transform(data))
[[-1. -1.]
[-1. -1.]
[ 1. 1.]
[ 1. 1.]]
>>> print(scaler.transform([[2, 2]]))
[[3. 3.]]
""" # noqa

def __init__(self, *, copy=True, with_mean=True, with_std=True):
Expand Down Expand Up @@ -946,7 +946,7 @@ def partial_fit(self, X, y=None, sample_weight=None):
return self

def transform(self, X, copy=None):
"""Perform standardization by centering and scaling
"""Perform standardization by centering and scaling.

Parameters
----------
Expand Down Expand Up @@ -989,7 +989,7 @@ def transform(self, X, copy=None):
return X

def inverse_transform(self, X, copy=None):
"""Scale back the data to the original representation
"""Scale back the data to the original representation.

Parameters
----------
Expand Down