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DOC Ensures that EllipticEnvelope passes numpydoc validation #20548

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Merged
merged 5 commits into from
Jul 20, 2021
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DOC Ensures that EllipticEnvelope passes numpydoc validation #20548

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89827e6
Remove EllipticEnvelope from DOCSTRING_IGNORE_LIST.
genvalen Jul 17, 2021
9932fc6
Ensure EllipticEnvelope passes numpydoc validation
genvalen Jul 17, 2021
531f2ca
Add period
genvalen Jul 17, 2021
ae7980e
Add more options to See Also section.
genvalen Jul 17, 2021
5b6f0a0
Merge branch 'main' into EllipticEnvelope_numpydoc
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 @@ -23,7 +23,6 @@
"DictionaryLearning",
"DummyClassifier",
"ElasticNetCV",
"EllipticEnvelope",
"ExtraTreeClassifier",
"ExtraTreeRegressor",
"ExtraTreesClassifier",
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49 changes: 30 additions & 19 deletions sklearn/covariance/_elliptic_envelope.py
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Expand Up @@ -88,6 +88,29 @@ class EllipticEnvelope(OutlierMixin, MinCovDet):

.. versionadded:: 0.24

See Also
--------
EmpiricalCovariance : Maximum likelihood covariance estimator.
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.

Notes
-----
Outlier detection from covariance estimation may break or not
perform well in high-dimensional settings. In particular, one will
always take care to work with ``n_samples > n_features ** 2``.

References
----------
.. [1] Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the
minimum covariance determinant estimator" Technometrics 41(3), 212
(1999)

Examples
--------
>>> import numpy as np
Expand All @@ -107,22 +130,6 @@ class EllipticEnvelope(OutlierMixin, MinCovDet):
[0.2535..., 0.3053...]])
>>> cov.location_
array([0.0813... , 0.0427...])

See Also
--------
EmpiricalCovariance, MinCovDet

Notes
-----
Outlier detection from covariance estimation may break or not
perform well in high-dimensional settings. In particular, one will
always take care to work with ``n_samples > n_features ** 2``.

References
----------
.. [1] Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the
minimum covariance determinant estimator" Technometrics 41(3), 212
(1999)
"""

def __init__(
Expand Down Expand Up @@ -152,6 +159,11 @@ def fit(self, X, y=None):

y : Ignored
Not used, present for API consistency by convention.

Returns
-------
self : object
Returns the instance itself.
"""
if self.contamination != "auto":
if not (0.0 < self.contamination <= 0.5):
Expand Down Expand Up @@ -202,8 +214,7 @@ def score_samples(self, X):

def predict(self, X):
"""
Predict the labels (1 inlier, -1 outlier) of X according to the
fitted model.
Predict labels (1 inlier, -1 outlier) of X according to fitted model.

Parameters
----------
Expand All @@ -222,7 +233,7 @@ def predict(self, X):
return is_inlier

def score(self, X, y, sample_weight=None):
"""Returns the mean accuracy on the given test data and labels.
"""Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy
which is a harsh metric since you require for each sample that
Expand Down