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DOC improve documentation of copy=False in preprocessing functions #27691

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merged 11 commits into from
Dec 7, 2023
Merged

DOC improve documentation of copy=False in preprocessing functions #27691

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deca6b8
Change the docstring of minmax_scale + add warning when X dtype not f…
konstantinos-p Oct 31, 2023
4c2c4dc
Fix errors raised by black and ruff
konstantinos-p Oct 31, 2023
4bcc4f0
Remove proposed warning, fix minmax_scale docstring according to Stan…
konstantinos-p Oct 31, 2023
342401d
Remove spurious newline in minmax_scale
konstantinos-p Oct 31, 2023
3b4ef64
:art: Some wording tweaks sklearn/preprocessing/_data.py
konstantinos-p Nov 6, 2023
e928bdf
Apply corrected text to scale, maxabs_scale, robust_scale, normalize,…
konstantinos-p Nov 6, 2023
8776640
Change inplace in the second sentence to in place to match the phrasi…
konstantinos-p Nov 6, 2023
475acda
Adapt the phrase scale, to more accurately reflect the transformation…
konstantinos-p Nov 8, 2023
5fd8c70
Change the example when a copy occurs for the binarize function, fix …
konstantinos-p Nov 8, 2023
02afc9f
Streamline text across all functions with single counterexample using…
konstantinos-p Dec 7, 2023
8324307
lint
lesteve Dec 7, 2023
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52 changes: 32 additions & 20 deletions sklearn/preprocessing/_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,9 +155,10 @@ def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True):
unit standard deviation).

copy : bool, default=True
Set to False to perform inplace row normalization and avoid a
copy (if the input is already a numpy array or a scipy.sparse
CSC matrix and if axis is 1).
If False, try to avoid a copy and scale in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.

Returns
-------
Expand Down Expand Up @@ -613,8 +614,10 @@ def minmax_scale(X, feature_range=(0, 1), *, axis=0, copy=True):
otherwise (if 1) scale each sample.

copy : bool, default=True
Set to False to perform inplace scaling and avoid a copy (if the input
is already a numpy array).
If False, try to avoid a copy and scale in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.

Returns
-------
Expand Down Expand Up @@ -1336,8 +1339,10 @@ def maxabs_scale(X, *, axis=0, copy=True):
otherwise (if 1) scale each sample.

copy : bool, default=True
Set to False to perform inplace scaling and avoid a copy (if the input
is already a numpy array).
If False, try to avoid a copy and scale in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.

Returns
-------
Expand Down Expand Up @@ -1713,9 +1718,10 @@ def robust_scale(
.. versionadded:: 0.18

copy : bool, default=True
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).
If False, try to avoid a copy and scale in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.

unit_variance : bool, default=False
If `True`, scale data so that normally distributed features have a
Expand Down Expand Up @@ -1826,9 +1832,10 @@ def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False):
normalize each sample, otherwise (if 0) normalize each feature.

copy : bool, default=True
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).
If False, try to avoid a copy and normalize in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.

return_norm : bool, default=False
Whether to return the computed norms.
Expand Down Expand Up @@ -2059,9 +2066,10 @@ def binarize(X, *, threshold=0.0, copy=True):
Threshold may not be less than 0 for operations on sparse matrices.

copy : bool, default=True
Set to False to perform inplace binarization and avoid a copy
(if the input is already a numpy array or a scipy.sparse CSR / CSC
matrix and if axis is 1).
If False, try to avoid a copy and binarize in place.
This is not guaranteed to always work in place; e.g. if the data is
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I think in this case (and maybe others) the previous wording is accurate that scipy CSR or CSC matrices are not copied, so you can not reuse the same doc here.

In general, the fact that a copy is made mostly depends on the check_array arguments, but the code following it may also need to be looked at closer.

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@lesteve lesteve Nov 14, 2023 •
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I still think the previous docstring was accurate whether the new one is slightly misleading.

The code below is used, which means that it accepts CSR/CSC sparse matrix without copying it with copy=False:

X = check_array(X, accept_sparse=["csr", "csc"], copy=copy)

a numpy array with an object dtype, a copy will be returned even with
copy=False.

Returns
-------
Expand Down Expand Up @@ -2945,9 +2953,10 @@ def quantile_transform(
See :term:`Glossary <random_state>`.

copy : bool, default=True
Set to False to perform inplace transformation and avoid a copy (if the
input is already a numpy array). If True, a copy of `X` is transformed,
leaving the original `X` unchanged.
If False, try to avoid a copy and transform in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.

.. versionchanged:: 0.23
The default value of `copy` changed from False to True in 0.23.
Expand Down Expand Up @@ -3481,7 +3490,10 @@ def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True):
transformed output.

copy : bool, default=True
Set to False to perform inplace computation during transformation.
If False, try to avoid a copy and transform in place.
This is not guaranteed to always work in place; e.g. if the data is
a numpy array with an int dtype, a copy will be returned even with
copy=False.

Returns
-------
Expand Down