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DOC Ensures that PLSSVD passes numpydoc validation #21198

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Sep 30, 2021
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DOC Ensures that PLSSVD passes numpydoc validation #21198

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8f34739
Remove PLSSVD from DOCSTRING_IGNORE_LIST
jmloyola Sep 29, 2021
a698acd
Fix numpydocs from PLSSVD
jmloyola Sep 29, 2021
aa060f4
Change docstrings to maintain consistency
jmloyola Sep 29, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -23,7 +23,6 @@
"OrthogonalMatchingPursuit",
"OrthogonalMatchingPursuitCV",
"PLSRegression",
"PLSSVD",
"PassiveAggressiveClassifier",
"PassiveAggressiveRegressor",
"PatchExtractor",
Expand Down
33 changes: 19 additions & 14 deletions sklearn/cross_decomposition/_pls.py
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Expand Up @@ -910,10 +910,10 @@ def __init__(
class PLSSVD(TransformerMixin, BaseEstimator):
"""Partial Least Square SVD.

This transformer simply performs a SVD on the crosscovariance matrix X'Y.
It is able to project both the training data `X` and the targets `Y`. The
training data X is projected on the left singular vectors, while the
targets are projected on the right singular vectors.
This transformer simply performs a SVD on the cross-covariance matrix
`X'Y`. It is able to project both the training data `X` and the targets
`Y`. The training data `X` is projected on the left singular vectors, while
the targets are projected on the right singular vectors.

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

Expand All @@ -930,18 +930,18 @@ class PLSSVD(TransformerMixin, BaseEstimator):

copy : bool, default=True
Whether to copy `X` and `Y` in fit before applying centering, and
potentially scaling. If False, these operations will be done inplace,
potentially scaling. If `False`, these operations will be done inplace,
modifying both arrays.

Attributes
----------
x_weights_ : ndarray of shape (n_features, n_components)
The left singular vectors of the SVD of the cross-covariance matrix.
Used to project `X` in `transform`.
Used to project `X` in :meth:`transform`.

y_weights_ : ndarray of (n_targets, n_components)
The right singular vectors of the SVD of the cross-covariance matrix.
Used to project `X` in `transform`.
Used to project `X` in :meth:`transform`.

x_scores_ : ndarray of shape (n_samples, n_components)
The transformed training samples.
Expand All @@ -968,6 +968,11 @@ class PLSSVD(TransformerMixin, BaseEstimator):

.. versionadded:: 1.0

See Also
--------
PLSCanonical : Partial Least Squares transformer and regressor.
CCA : Canonical Correlation Analysis.

Examples
--------
>>> import numpy as np
Expand All @@ -984,11 +989,6 @@ class PLSSVD(TransformerMixin, BaseEstimator):
>>> X_c, Y_c = pls.transform(X, Y)
>>> X_c.shape, Y_c.shape
((4, 2), (4, 2))

See Also
--------
PLSCanonical
CCA
"""

def __init__(self, n_components=2, *, scale=True, copy=True):
Expand All @@ -1006,6 +1006,11 @@ def fit(self, X, Y):

Y : array-like of shape (n_samples,) or (n_samples, n_targets)
Targets.

Returns
-------
self : object
Fitted estimator.
"""
check_consistent_length(X, Y)
X = self._validate_data(
Expand Down Expand Up @@ -1118,7 +1123,7 @@ def transform(self, X, Y=None):
Returns
-------
x_scores : array-like or tuple of array-like
The transformed data `X_tranformed` if `Y` is not None,
The transformed data `X_tranformed` if `Y is not None`,
`(X_transformed, Y_transformed)` otherwise.
"""
check_is_fitted(self)
Expand Down Expand Up @@ -1149,7 +1154,7 @@ def fit_transform(self, X, y=None):
Returns
-------
out : array-like or tuple of array-like
The transformed data `X_tranformed` if `Y` is not None,
The transformed data `X_tranformed` if `Y is not None`,
`(X_transformed, Y_transformed)` otherwise.
"""
return self.fit(X, y).transform(X, y)