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DOC Ensures MaxAbsScaler passes numpydoc validation #20455

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Jul 6, 2021
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DOC Ensures MaxAbsScaler passes numpydoc validation #20455

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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 @@ -90,7 +90,6 @@
"MDS",
"MLPClassifier",
"MLPRegressor",
"MaxAbsScaler",
"MeanShift",
"MinCovDet",
"MiniBatchDictionaryLearning",
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30 changes: 15 additions & 15 deletions sklearn/preprocessing/_data.py
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Expand Up @@ -1072,6 +1072,19 @@ class MaxAbsScaler(TransformerMixin, BaseEstimator):
The number of samples processed by the estimator. Will be reset on
new calls to fit, but increments across ``partial_fit`` calls.

See Also
--------
maxabs_scale : Equivalent function without the estimator API.

Notes
-----
NaNs are treated as missing values: disregarded in fit, and maintained in
transform.

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 MaxAbsScaler
Expand All @@ -1085,19 +1098,6 @@ class MaxAbsScaler(TransformerMixin, BaseEstimator):
array([[ 0.5, -1. , 1. ],
[ 1. , 0. , 0. ],
[ 0. , 1. , -0.5]])

See Also
--------
maxabs_scale : Equivalent function without the estimator API.

Notes
-----
NaNs are treated as missing values: disregarded in fit, and maintained in
transform.

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>`.
"""

def __init__(self, *, copy=True):
Expand Down Expand Up @@ -1186,7 +1186,7 @@ def partial_fit(self, X, y=None):
return self

def transform(self, X):
"""Scale the data
"""Scale the data.

Parameters
----------
Expand Down Expand Up @@ -1216,7 +1216,7 @@ def transform(self, X):
return X

def inverse_transform(self, X):
"""Scale back the data to the original representation
"""Scale back the data to the original representation.

Parameters
----------
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