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DOC Apply numpydoc validation to VotingRegressor methods #15969

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Merged
merged 6 commits into from
Dec 26, 2019
Merged

DOC Apply numpydoc validation to VotingRegressor methods #15969

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a040a33
Apply numpydoc validation to VotingRegressor methods
Tiffany8 Nov 2, 2019
e6ca073
Merge branch 'master' into issue/15440-apply-numpydoc-validation-to-v…
Tiffany8 Nov 2, 2019
05d6271
voting docstrings files;numpy doc validation
Dec 24, 2019
db4f4b9
addressing comments + formatting
Dec 24, 2019
df4e20f
fixed flake8 error
Dec 24, 2019
350ac2f
removed merged conflict warning text
Dec 24, 2019
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18 changes: 9 additions & 9 deletions sklearn/ensemble/_voting.py
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Expand Up @@ -326,7 +326,7 @@ class VotingRegressor(RegressorMixin, _BaseVoting):

Parameters
----------
estimators : list of (string, estimator) tuples
estimators : list of (str, estimator) tuples
Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones
of those original estimators that will be stored in the class attribute
``self.estimators_``. An estimator can be set to ``'drop'`` using
Expand Down Expand Up @@ -357,6 +357,10 @@ class VotingRegressor(RegressorMixin, _BaseVoting):

.. versionadded:: 0.20

See Also
--------
VotingClassifier: Soft Voting/Majority Rule classifier.

Examples
--------
>>> import numpy as np
Expand All @@ -370,10 +374,6 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
>>> er = VotingRegressor([('lr', r1), ('rf', r2)])
>>> print(er.fit(X, y).predict(X))
[ 3.3 5.7 11.8 19.7 28. 40.3]

See also
--------
VotingClassifier: Soft Voting/Majority Rule classifier.
"""

def __init__(self, estimators, weights=None, n_jobs=None):
Expand All @@ -382,7 +382,7 @@ def __init__(self, estimators, weights=None, n_jobs=None):
self.n_jobs = n_jobs

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

Parameters
----------
Expand All @@ -401,6 +401,7 @@ def fit(self, X, y, sample_weight=None):
Returns
-------
self : object
Fitted estimator.
"""
y = column_or_1d(y, warn=True)
return super().fit(X, y, sample_weight)
Expand Down Expand Up @@ -435,9 +436,8 @@ def transform(self, X):

Returns
-------
predictions
array-like of shape (n_samples, n_classifiers), being
values predicted by each regressor.
predictions: array of shape (n_samples, n_classifiers)
Values predicted by each regressor.
"""
check_is_fitted(self)
return self._predict(X)