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DOC Ensures that BayesianRidge passes numpydoc validation #20389

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Merged
merged 4 commits into from
Jun 28, 2021
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DOC Ensures that BayesianRidge passes numpydoc validation #20389

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ea0261b
DOC Ensures that BayesianRidge passes numpydoc validation
pibieta Jun 26, 2021
4e16ead
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
pibieta Jun 26, 2021
6b7c98d
Changed the fit return documentation as suggested
pibieta Jun 26, 2021
d9af2d6
Merge branch 'main' into numpydoc_val_BayesianRidge
glemaitre Jun 28, 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 @@
"AgglomerativeClustering",
"BaggingClassifier",
"BaggingRegressor",
"BayesianRidge",
"BernoulliNB",
"BernoulliRBM",
"Binarizer",
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30 changes: 17 additions & 13 deletions sklearn/linear_model/_bayes.py
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Expand Up @@ -95,7 +95,6 @@ class BayesianRidge(RegressorMixin, LinearModel):
verbose : bool, default=False
Verbose mode when fitting the model.


Attributes
----------
coef_ : array-like of shape (n_features,)
Expand Down Expand Up @@ -137,14 +136,9 @@ class BayesianRidge(RegressorMixin, LinearModel):

.. versionadded:: 0.24

Examples
See Also
--------
>>> from sklearn import linear_model
>>> clf = linear_model.BayesianRidge()
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
BayesianRidge()
>>> clf.predict([[1, 1]])
array([1.])
ARDRegression : Bayesian ARD regression.

Notes
-----
Expand All @@ -163,6 +157,15 @@ class BayesianRidge(RegressorMixin, LinearModel):

M. E. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine,
Journal of Machine Learning Research, Vol. 1, 2001.

Examples
--------
>>> from sklearn import linear_model
>>> clf = linear_model.BayesianRidge()
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
BayesianRidge()
>>> clf.predict([[1, 1]])
array([1.])
"""

def __init__(
Expand Down Expand Up @@ -197,24 +200,25 @@ def __init__(
self.verbose = verbose

def fit(self, X, y, sample_weight=None):
"""Fit the model
"""Fit the model.

Parameters
----------
X : ndarray of shape (n_samples, n_features)
Training data
Training data.
y : ndarray of shape (n_samples,)
Target values. Will be cast to X's dtype if necessary
Target values. Will be cast to X's dtype if necessary.

sample_weight : ndarray of shape (n_samples,), default=None
Individual weights for each sample
Individual weights for each sample.

.. versionadded:: 0.20
parameter *sample_weight* support to BayesianRidge.

Returns
-------
self : returns an instance of self.
self : object
Returns the instance itself.
"""
self._normalize = _deprecate_normalize(
self.normalize, default=False, estimator_name=self.__class__.__name__
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