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DOC Ensures that GraphicalLassoCV passes numpydoc validation #20546

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Jul 20, 2021
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DOC Ensures that GraphicalLassoCV passes numpydoc validation #20546

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c8925be
Remove GraphicalLassoCV from DOCSTRING_IGNORE_LIST.
genvalen Jul 17, 2021
40c2fef
Ensure GraphicalLassoCV passes numpydoc validation
genvalen Jul 17, 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 @@ -41,7 +41,6 @@
"GaussianRandomProjection",
"GradientBoostingClassifier",
"GradientBoostingRegressor",
"GraphicalLassoCV",
"GridSearchCV",
"HalvingGridSearchCV",
"HalvingRandomSearchCV",
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41 changes: 22 additions & 19 deletions sklearn/covariance/_graph_lasso.py
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Expand Up @@ -637,7 +637,7 @@ class GraphicalLassoCV(GraphicalLasso):
stable.

n_jobs : int, default=None
number of jobs to run in parallel.
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
Expand Down Expand Up @@ -710,6 +710,24 @@ class GraphicalLassoCV(GraphicalLasso):

.. versionadded:: 0.24

See Also
--------
graphical_lasso : L1-penalized covariance estimator.
GraphicalLasso : Sparse inverse covariance with
cross-validated choice of the l1 penalty.

Notes
-----
The search for the optimal penalization parameter (alpha) is done on an
iteratively refined grid: first the cross-validated scores on a grid are
computed, then a new refined grid is centered around the maximum, and so
on.

One of the challenges which is faced here is that the solvers can
fail to converge to a well-conditioned estimate. The corresponding
values of alpha then come out as missing values, but the optimum may
be close to these missing values.

Examples
--------
>>> import numpy as np
Expand All @@ -730,22 +748,6 @@ class GraphicalLassoCV(GraphicalLasso):
[0.017, 0.036, 0.094, 0.69 ]])
>>> np.around(cov.location_, decimals=3)
array([0.073, 0.04 , 0.038, 0.143])

See Also
--------
graphical_lasso, GraphicalLasso

Notes
-----
The search for the optimal penalization parameter (alpha) is done on an
iteratively refined grid: first the cross-validated scores on a grid are
computed, then a new refined grid is centered around the maximum, and so
on.

One of the challenges which is faced here is that the solvers can
fail to converge to a well-conditioned estimate. The corresponding
values of alpha then come out as missing values, but the optimum may
be close to these missing values.
"""

def __init__(
Expand Down Expand Up @@ -776,19 +778,20 @@ def __init__(
self.n_jobs = n_jobs

def fit(self, X, y=None):
"""Fits the GraphicalLasso covariance model to X.
"""Fit the GraphicalLasso covariance model to X.

Parameters
----------
X : array-like of shape (n_samples, n_features)
Data from which to compute the covariance estimate
Data from which to compute the covariance estimate.

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

Returns
-------
self : object
Returns the instance itself.
"""
# Covariance does not make sense for a single feature
X = self._validate_data(X, ensure_min_features=2, estimator=self)
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