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DOC Ensures that ShrunkCovariance passes numpydoc validation #20571

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merged 3 commits into from
Jul 20, 2021
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DOC Ensures that ShrunkCovariance passes numpydoc validation #20571

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957a0b3
Remove ShrunkCovariance from DOCSTRING_IGNORE_LIST.
genvalen Jul 20, 2021
ec0d0a3
Ensure ShrunkCovariance passes numpydoc validation.
genvalen Jul 20, 2021
4a01603
Update sklearn/covariance/_shrunk_covariance.py
glemaitre Jul 20, 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 @@ -134,7 +134,6 @@
"SelectPercentile",
"SelfTrainingClassifier",
"SequentialFeatureSelector",
"ShrunkCovariance",
"SimpleImputer",
"SkewedChi2Sampler",
"SparseCoder",
Expand Down
38 changes: 26 additions & 12 deletions sklearn/covariance/_shrunk_covariance.py
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Expand Up @@ -61,14 +61,14 @@ def shrunk_covariance(emp_cov, shrinkage=0.1):


class ShrunkCovariance(EmpiricalCovariance):
"""Covariance estimator with shrinkage
"""Covariance estimator with shrinkage.

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

Parameters
----------
store_precision : bool, default=True
Specify if the estimated precision is stored
Specify if the estimated precision is stored.

assume_centered : bool, default=False
If True, data will not be centered before computation.
Expand Down Expand Up @@ -97,6 +97,28 @@ class ShrunkCovariance(EmpiricalCovariance):

.. versionadded:: 0.24

See Also
--------
EllipticEnvelope : An object for detecting outliers in
a Gaussian distributed dataset.
EmpiricalCovariance : Maximum likelihood covariance estimator.
GraphicalLasso : Sparse inverse covariance estimation
with an l1-penalized estimator.
GraphicalLassoCV : Sparse inverse covariance with cross-validated
choice of the l1 penalty.
LedoitWolf : LedoitWolf Estimator.
MinCovDet : Minimum Covariance Determinant
(robust estimator of covariance).
OAS : Oracle Approximating Shrinkage Estimator.

Notes
-----
The regularized covariance is given by:

(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)

where mu = trace(cov) / n_features

Examples
--------
>>> import numpy as np
Expand All @@ -114,14 +136,6 @@ class ShrunkCovariance(EmpiricalCovariance):
[0.2536..., 0.4110...]])
>>> cov.location_
array([0.0622..., 0.0193...])

Notes
-----
The regularized covariance is given by:

(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)

where mu = trace(cov) / n_features
"""

def __init__(self, *, store_precision=True, assume_centered=False, shrinkage=0.1):
Expand All @@ -131,8 +145,7 @@ def __init__(self, *, store_precision=True, assume_centered=False, shrinkage=0.1
self.shrinkage = shrinkage

def fit(self, X, y=None):
"""Fit the shrunk covariance model according to the given training data
and parameters.
"""Fit the shrunk covariance model to X.

Parameters
----------
Expand All @@ -146,6 +159,7 @@ def fit(self, X, y=None):
Returns
-------
self : object
Returns the instance itself.
"""
X = self._validate_data(X)
# Not calling the parent object to fit, to avoid a potential
Expand Down