10000 DOC Ensures that RobustScaler passes numpydoc validation by jmloyola · Pull Request #21155 · scikit-learn/scikit-learn · GitHub
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Sep 27, 2021
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DOC Ensures that RobustScaler passes numpydoc validation #21155

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1e5255f
Remove RobustScaler from DOCSTRING_IGNORE_LIST
jmloyola Sep 27, 2021
6adec4d
Fix numpydocs from RobustScaler
jmloyola Sep 27, 2021
63023ea
Change docstrings to maintain consistency
jmloyola Sep 27, 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 @@ -32,7 +32,6 @@
"PatchExtractor",
"PolynomialFeatures",
"QuadraticDiscriminantAnalysis",
"RobustScaler",
"SelfTrainingClassifier",
"SparseRandomProjection",
"SpectralBiclustering",
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78 changes: 40 additions & 38 deletions sklearn/preprocessing/_data.py
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Expand Up @@ -1352,7 +1352,7 @@ class RobustScaler(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
Centering and scaling happen independently on each feature by
computing the relevant statistics on the samples in the training
set. Median and interquartile range are then stored to be used on
later data using the ``transform`` method.
later data using the :meth:`transform` method.

Standardization of a dataset is a common requirement for many
machine learning estimators. Typically this is done by removing the mean
Expand All @@ -1367,31 +1367,33 @@ class RobustScaler(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
Parameters
----------
with_centering : bool, default=True
If True, center the data before scaling.
This will cause ``transform`` to raise an exception when attempted on
sparse matrices, because centering them entails building a dense
If `True`, center the data before scaling.
This will cause :meth:`transform` to raise an exception when attempted
on sparse matrices, because centering them entails building a dense
matrix which in common use cases is likely to be too large to fit in
memory.

with_scaling : bool, default=True
If True, scale the data to interquartile range.
If `True`, scale the data to interquartile range.

quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0, \
default=(25.0, 75.0), == (1st quantile, 3rd quantile), == IQR
Quantile range used to calculate ``scale_``.
default=(25.0, 75.0)
Quantile range used to calculate `scale_`. By default this is equal to
the IQR, i.e., `q_min` is the first quantile and `q_max` is the third
quantile.

.. versionadded:: 0.18

copy : bool, default=True
If False, try to avoid a copy and do inplace scaling instead.
If `False`, try to avoid a copy and do inplace scaling instead.
This is not guaranteed to always work inplace; e.g. if the data is
not a NumPy array or scipy.sparse CSR matrix, a copy may still be
returned.

unit_variance : bool, default=False
If True, scale data so that normally distributed features have a
If `True`, scale data so that normally distributed features have a
variance of 1. In general, if the difference between the x-values of
``q_max`` and ``q_min`` for a standard normal distribution is greater
`q_max` and `q_min` for a standard normal distribution is greater
than 1, the dataset will be scaled down. If less than 1, the dataset
will be scaled up.

Expand Down Expand Up @@ -1419,6 +1421,21 @@ class RobustScaler(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator):

.. versionadded:: 1.0

See Also
--------
robust_scale : Equivalent function without the estimator API.
sklearn.decomposition.PCA : Further removes the linear correlation across
features with 'whiten=True'.

Notes
-----
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>`.

https://en.wikipedia.org/wiki/Median
https://en.wikipedia.org/wiki/Interquartile_range

Examples
--------
>>> from sklearn.preprocessing import RobustScaler
Expand All @@ -1432,23 +1449,6 @@ class RobustScaler(_OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
array([[ 0. , -2. , 0. ],
[-1. , 0. , 0.4],
[ 1. , 0. , -1.6]])

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

:class:`~sklearn.decomposition.PCA`
Further removes the linear correlation across features with
'whiten=True'.

Notes
-----
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>`.

https://en.wikipedia.org/wiki/Median
https://en.wikipedia.org/wiki/Interquartile_range
"""

def __init__(
Expand All @@ -1475,8 +1475,8 @@ def fit(self, X, y=None):
The data used to compute the median and quantiles
used for later scaling along the features axis.

y : None
Ignored.
y : Ignored
Not used, present here for API consistency by convention.

Returns
-------
Expand Down Expand Up @@ -1627,32 +1627,34 @@ def robust_scale(
The data to center and scale.

axis : int, default=0
axis used to compute the medians and IQR along. If 0,
Axis used to compute the medians and IQR along. If 0,
independently scale each feature, otherwise (if 1) scale
each sample.

with_centering : bool, default=True
If True, center the data before scaling.
If `True`, center the data before scaling.

with_scaling : bool, default=True
If True, scale the data to unit variance (or equivalently,
If `True`, scale the data to unit variance (or equivalently,
unit standard deviation).

quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0
default=(25.0, 75.0), == (1st quantile, 3rd quantile), == IQR
Quantile range used to calculate ``scale_``.
quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0,\
default=(25.0, 75.0)
Quantile range used to calculate `scale_`. By default this is equal to
the IQR, i.e., `q_min` is the first quantile and `q_max` is the third
quantile.

.. versionadded:: 0.18

copy : bool, default=True
set to False to perform inplace row normalization and avoid a
Set to `False` to perform inplace row normalization and avoid a
copy (if the input is already a numpy array or a scipy.sparse
CSR matrix and if axis is 1).

unit_variance : bool, default=False
If True, scale data so that normally distributed features have a
If `True`, scale data so that normally distributed features have a
variance of 1. In general, if the difference between the x-values of
``q_max`` and ``q_min`` for a standard normal distribution is greater
`q_max` and `q_min` for a standard normal distribution is greater
than 1, the dataset will be scaled down. If less than 1, the dataset
will be scaled up.

Expand Down
0