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[MRG] Apply numpydoc validation to VotingRegressor methods #15500

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[MRG] Apply numpydoc validation to VotingRegressor methods #15500

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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
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5 changes: 5 additions & 0 deletions maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,11 @@
"RidgeClassifier.fit",
"RidgeClassifierCV.decision_function",
"SGDClassifier.decision_function",
"VotingRegressor$",
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I am unfamiliar with this $ notation. I see it above for LogisticRegression$ and not below for KernelDensity. What does it mean and what made you decide to put $?

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It means end of the string with regular expression. LogisticRegression without $ would match any of its methods, not just the main docstring

"VotingRegressor.fit",
"VotingRegressor.transform",
"VotingRegressor.predict",
"VotingRegressor.score",
"KernelDensity",
"KernelDensity.fit",
"KernelDensity.score",
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6 changes: 4 additions & 2 deletions sklearn/base.py
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Expand Up @@ -369,7 +369,7 @@ class RegressorMixin:
_estimator_type = "regressor"

def score(self, X, y, sample_weight=None):
"""Returns the coefficient of determination R^2 of the prediction.
"""Return the coefficient of determination R^2 of the prediction.
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I think the convention has been to write "Returns"

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This change is okay since we follow https://www.python.org/dev/peps/pep-0257/

The docstring is a phrase ending in a period. It prescribes the function or method's effect as a command ("Do this", "Return that"), not as a description; e.g. don't write "Returns the pathname ...".


The coefficient R^2 is defined as (1 - u/v), where u is the residual
sum of squares ((y_true - y_pred) ** 2).sum() and v is the total
Expand Down Expand Up @@ -549,11 +549,13 @@ def fit_transform(self, X, y=None, **fit_params):
y : numpy array of shape [n_samples]
Target values.

**fit_params : Any number of parameters
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Maybe say "dict of keyword arguments"

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Suggested change
**fit_params : Any number of parameters
**fit_params : dict

Fit parameters.

Returns
-------
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.

"""
# non-optimized default implementation; override when a better
# method is possible for a given clustering algorithm
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15 changes: 8 additions & 7 deletions sklearn/ensemble/_voting.py
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Expand Up @@ -328,7 +328,7 @@ 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 @@ -359,6 +359,10 @@ class VotingRegressor(RegressorMixin, _BaseVoting):

.. versionadded:: 0.20

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

Examples
--------
>>> import numpy as np
Expand All @@ -372,10 +376,6 @@ 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 @@ -384,7 +384,7 @@ 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 @@ -403,6 +403,7 @@ def fit(self, X, y, sample_weight=None):
Returns
-------
self : object
Returns self.
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Suggested change
Returns self.
Fitted estimator.

although I agree that it's probably not very useful in any case.

"""
y = column_or_1d(y, warn=True)
return super().fit(X, y, sample_weight)
Expand Down Expand Up @@ -438,7 +439,7 @@ def transform(self, X):
Returns
-------
predictions
array-like of shape (n_samples, n_classifiers), being
Array-like of shape (n_samples, n_classifiers), being
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no actually this should be,

predictions : array-like of shape (n_samples, n_classifiers)
    Values predicted by each regressor.

(with the right indentation)

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out of curiosity, why should this be an array-like instead of an array. Can it output anything other than ndarrays? (My only guess would be that if a dataframe was input it could output one and that dfs fall under the array-like definition, but haven't followed in detail the inclusion of dfs as possible inputs)

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You are right, it can only return an array. We use array-like for input dtypes, and it was probably copy-pasted there...

values predicted by each regressor.
"""
check_is_fitted(self)
Expand Down