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DOC Ensures that CCA passes numpydoc validation #20504

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merged 10 commits into from
Jul 20, 2021
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DOC Ensures that CCA passes numpydoc validation #20504

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1c7eade
fixed pytest errorts for CCA
napoles-uach Jul 10, 2021
def7bc0
file formatted after black command
napoles-uach Jul 12, 2021
460343f
Update sklearn/cross_decomposition/_pls.py
napoles-uach Jul 13, 2021
4c9d59f
Update sklearn/cross_decomposition/_pls.py
napoles-uach Jul 13, 2021
75029b3
Update sklearn/cross_decomposition/_pls.py
napoles-uach Jul 13, 2021
6e67742
Update sklearn/cross_decomposition/_pls.py
napoles-uach Jul 14, 2021
c27d001
Update sklearn/cross_decomposition/_pls.py
napoles-uach Jul 16, 2021
bd68786
Update sklearn/cross_decomposition/_pls.py
napoles-uach Jul 16, 2021
e0a3769
Update sklearn/cross_decomposition/_pls.py
napoles-uach Jul 20, 2021
73fd5f5
FIX make sure to test by removing CCA from the list
glemaitre Jul 20, 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 @@ -15,7 +15,6 @@
"BaggingRegressor",
"BernoulliRBM",
"Birch",
"CCA",
"CalibratedClassifierCV",
"ClassifierChain",
"ColumnTransformer",
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35 changes: 25 additions & 10 deletions sklearn/cross_decomposition/_pls.py
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Expand Up @@ -201,6 +201,11 @@ def fit(self, X, Y):
Y : array-like of shape (n_samples,) or (n_samples, n_targets)
Target vectors, where `n_samples` is the number of samples and
`n_targets` is the number of response variables.

Returns
-------
self : object
Fitted model.
"""

check_consistent_length(X, Y)
Expand Down Expand Up @@ -372,7 +377,8 @@ def transform(self, X, Y=None, copy=True):

Returns
-------
`x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise.
x_scores, y_scores : array-like or tuple of array-like
Return `x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise.
"""
check_is_fitted(self)
X = self._validate_data(X, copy=copy, dtype=FLOAT_DTYPES, reset=False)
Expand Down Expand Up @@ -403,7 +409,8 @@ def inverse_transform(self, X):

Returns
-------
x_reconstructed : array-like of shape (n_samples, n_features)
self : ndarray of shape (n_samples, n_features)
Return the reconstructed array.

Notes
-----
Expand All @@ -430,6 +437,11 @@ def predict(self, X, copy=True):
copy : bool, default=True
Whether to copy `X` and `Y`, or perform in-place normalization.

Returns
-------
y_pred : ndarray of shape (n_samples,) or (n_samples, n_targets)
Returns predicted values.

Notes
-----
This call requires the estimation of a matrix of shape
Expand Down Expand Up @@ -459,7 +471,8 @@ def fit_transform(self, X, y=None):

Returns
-------
x_scores if Y is not given, (x_scores, y_scores) otherwise.
self : ndarray of shape (n_samples, n_components)
Return `x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise.
"""
return self.fit(X, y).transform(X, y)

Expand Down Expand Up @@ -506,6 +519,7 @@ def y_std_(self):

@property
def x_scores_(self):
"""Attribute `x_scores_` was deprecated in version 0.24."""
# TODO: raise error in 1.1 instead
if not isinstance(self, PLSRegression):
pass
Expand All @@ -519,6 +533,7 @@ def x_scores_(self):

@property
def y_scores_(self):
"""Attribute `y_scores_` was deprecated in version 0.24."""
# TODO: raise error in 1.1 instead
if not isinstance(self, PLSRegression):
warnings.warn(
Expand Down Expand Up @@ -782,7 +797,7 @@ class CCA(_PLS):
Whether to scale `X` and `Y`.

max_iter : int, default=500
the maximum number of iterations of the power method.
The maximum number of iterations of the power method.

tol : float, default=1e-06
The tolerance used as convergence criteria in the power method: the
Expand Down Expand Up @@ -843,6 +858,11 @@ class CCA(_PLS):
n_features_in_ : int
Number of features seen during :term:`fit`.

See Also
--------
PLSCanonical : Partial Least Squares transformer and regressor.
PLSSVD : Partial Least Square SVD.

Examples
--------
>>> from sklearn.cross_decomposition import CCA
Expand All @@ -852,11 +872,6 @@ class CCA(_PLS):
>>> cca.fit(X, Y)
CCA(n_components=1)
>>> X_c, Y_c = cca.transform(X, Y)

See Also
--------
PLSCanonical
PLSSVD
"""

def __init__(
Expand Down Expand Up @@ -1078,7 +1093,7 @@ def transform(self, X, Y=None):

Returns
-------
out : array-like or tuple of array-like
x_scores : array-like or tuple of array-like
The transformed data `X_tranformed` if `Y` is not None,
`(X_transformed, Y_transformed)` otherwise.
"""
Expand Down