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DOC Ensures that MultiTaskElasticNetCV passes numpydoc validation #21405

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Oct 23, 2021
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DOC Ensures that MultiTaskElasticNetCV passes numpydoc validation #21405

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be38cf6
validation fixes on MultiTaskElasticNetCV estimator docstrings
spikebh Oct 22, 2021
63cef17
Update sklearn/linear_model/_coordinate_descent.py
spikebh Oct 22, 2021
f35ab27
Update sklearn/linear_model/_coordinate_descent.py
spikebh Oct 22, 2021
2cb98fe
Merge branch 'main' into docs_multitaskelasticnetcv
glemaitre Oct 23, 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 @@ -13,7 +13,6 @@

# List of modules ignored when checking for numpydoc validation.
DOCSTRING_IGNORE_LIST = [
"MultiTaskElasticNetCV",
"SpectralCoclustering",
"SpectralEmbedding",
"StackingRegressor",
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35 changes: 19 additions & 16 deletions sklearn/linear_model/_coordinate_descent.py
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Expand Up @@ -2647,7 +2647,7 @@ class MultiTaskElasticNetCV(RegressorMixin, LinearModelCV):
prediction score is used. Note that a good choice of list of
values for l1_ratio is often to put more values close to 1
(i.e. Lasso) and less close to 0 (i.e. Ridge), as in ``[.1, .5, .7,
.9, .95, .99, 1]``
.9, .95, .99, 1]``.

eps : float, default=1e-3
Length of the path. ``eps=1e-3`` means that
Expand Down Expand Up @@ -2768,6 +2768,21 @@ class MultiTaskElasticNetCV(RegressorMixin, LinearModelCV):

.. versionadded:: 1.0

See Also
--------
MultiTaskElasticNet : Multi-task L1/L2 ElasticNet with built-in cross-validation.
ElasticNetCV : Elastic net model with best model selection by
cross-validation.
MultiTaskLassoCV : Multi-task Lasso model trained with L1/L2
mixed-norm as regularizer.

Notes
-----
The algorithm used to fit the model is coordinate descent.

To avoid unnecessary memory duplication the X and y arguments of the fit
method should be directly passed as Fortran-contiguous numpy arrays.

Examples
--------
>>> from sklearn import linear_model
Expand All @@ -2780,19 +2795,6 @@ class MultiTaskElasticNetCV(RegressorMixin, LinearModelCV):
[0.52875032 0.46958558]]
>>> print(clf.intercept_)
[0.00166409 0.00166409]

See Also
--------
MultiTaskElasticNet
ElasticNetCV
MultiTaskLassoCV

Notes
-----
The algorithm used to fit the model is coordinate descent.

To avoid unnecessary memory duplication the X and y arguments of the fit
method should be directly passed as Fortran-contiguous numpy arrays.
"""

path = staticmethod(enet_path)
Expand Down Expand Up @@ -2849,13 +2851,14 @@ def fit(self, X, y):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Data
Training data.
y : ndarray of shape (n_samples, n_targets)
Target. Will be cast to X's dtype if necessary
Training target variable. Will be cast to X's dtype if necessary.

Returns
-------
self : object
Returns MultiTaskElasticNet instance.
"""
return super().fit(X, y)

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