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DOC Ensures that PatchExtractor passes numpydoc validation #21233

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merged 3 commits into from
Oct 4, 2021
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Merged

DOC Ensures that PatchExtractor passes numpydoc validation #21233

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37b9dbd
Remove PatchExtractor from DOCSTRING_IGNORE_LIST
jmloyola Oct 4, 2021
93b0d83
Fix numpydocs from PatchExtractor
jmloyola Oct 4, 2021
8e67c5e
Change docstrings to maintain consistency
jmloyola Oct 4, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -22,7 +22,6 @@
"OrthogonalMatchingPursuitCV",
"PassiveAggressiveClassifier",
"PassiveAggressiveRegressor",
"PatchExtractor",
"PolynomialFeatures",
"QuadraticDiscriminantAnalysis",
"SelfTrainingClassifier",
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22 changes: 17 additions & 5 deletions sklearn/feature_extraction/image.py
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Expand Up @@ -451,7 +451,7 @@ def reconstruct_from_patches_2d(patches, image_size):


class PatchExtractor(BaseEstimator):
"""Extracts patches from a collection of images
"""Extracts patches from a collection of images.

Read more in the :ref:`User Guide <image_feature_extraction>`.

Expand All @@ -463,16 +463,20 @@ class PatchExtractor(BaseEstimator):
The dimensions of one patch.

max_patches : int or float, default=None
The maximum number of patches per image to extract. If max_patches is a
float in (0, 1), it is taken to mean a proportion of the total number
The maximum number of patches per image to extract. If `max_patches` is
a float in (0, 1), it is taken to mean a proportion of the total number
of patches.

random_state : int, RandomState instance, default=None
Determines the random number generator used for random sampling when
`max_patches` is not None. Use an int to make the randomness
`max_patches is not None`. Use an int to make the randomness
deterministic.
See :term:`Glossary <random_state>`.

See Also
--------
reconstruct_from_patches_2d : Reconstruct image from all of its patches.

Examples
--------
>>> from sklearn.datasets import load_sample_images
Expand Down Expand Up @@ -503,11 +507,19 @@ def fit(self, X, y=None):
----------
X : array-like of shape (n_samples, n_features)
Training data.

y : Ignored
Not used, present for API consistency by convention.

Returns
-------
self : object
Returns the instance itself.
"""
return self

def transform(self, X):
"""Transforms the image samples in X into a matrix of patch data.
"""Transform the image samples in `X` into a matrix of patch data.

Parameters
----------
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