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DOC Ensures that EmpiricalCovariance passes numpydoc validation #20551

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Jul 20, 2021
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DOC Ensures that EmpiricalCovariance passes numpydoc validation #20551

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3ede750
Remove EmpiricalCovariance from DOCSTRING_IGNORE_LIST.
genvalen Jul 17, 2021
bce365d
Ensure EmpiricalCovariance passes numpydoc validation
genvalen Jul 17, 2021
1c3f1a9
Edit sentence to be more like class summary.
genvalen Jul 17, 2021
11de041
Add more options to See Also section.
genvalen Jul 19, 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 @@ -24,7 +24,6 @@
"DummyClassifier",
"ElasticNetCV",
"EllipticEnvelope",
"EmpiricalCovariance",
"ExtraTreeClassifier",
"ExtraTreeRegressor",
"ExtraTreesClassifier",
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19 changes: 15 additions & 4 deletions sklearn/covariance/_empirical_covariance.py
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Expand Up @@ -99,7 +99,7 @@ def empirical_covariance(X, *, assume_centered=False):


class EmpiricalCovariance(BaseEstimator):
"""Maximum likelihood covariance estimator
"""Maximum likelihood covariance estimator.

Read more in the :ref:`User Guide <covariance>`.

Expand Down Expand Up @@ -131,6 +131,18 @@ class EmpiricalCovariance(BaseEstimator):

.. versionadded:: 0.24

See Also
--------
EllipticEnvelope : An object for detecting outliers in
a Gaussian distributed dataset.
GraphicalLasso : Sparse inverse covariance estimation
with an l1-penalized estimator.
LedoitWolf : LedoitWolf Estimator.
MinCovDet : Minimum Covariance Determinant
(robust estimator of covariance).
OAS : Oracle Approximating Shrinkage Estimator.
ShrunkCovariance : Covariance estimator with shrinkage.

Examples
--------
>>> import numpy as np
Expand All @@ -148,7 +160,6 @@ class EmpiricalCovariance(BaseEstimator):
[0.2818..., 0.3928...]])
>>> cov.location_
array([0.0622..., 0.0193...])

"""

def __init__(self, *, store_precision=True, assume_centered=False):
Expand Down Expand Up @@ -191,8 +202,7 @@ def get_precision(self):
return precision

def fit(self, X, y=None):
"""Fits the Maximum Likelihood Estimator covariance model
according to the given training data and parameters.
"""Fit the maximum liklihood covariance estimator to X.

Parameters
----------
Expand All @@ -206,6 +216,7 @@ def fit(self, X, y=None):
Returns
-------
self : object
Returns the instance itself.
"""
X = self._validate_data(X)
if self.assume_centered:
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