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DOC Ensures that GraphicalLasso passes numpydoc validation #20527

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Jul 15, 2021
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DOC Ensures that GraphicalLasso passes numpydoc validation #20527

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fa6b3b7
Remove GraphicalLasso from DOCSTRING_IGNORE_LIST.
genvalen Jul 14, 2021
d5f4b43
Ensure GraphicalLasso passes numpydoc validation in_graph_lasso.py
genvalen Jul 14, 2021
40d3ea0
Ensure GraphicalLasso passes numpydoc validation in _empirical_covari…
genvalen Jul 14, 2021
a49f776
Update sklearn/covariance/_graph_lasso.py
genvalen Jul 15, 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 @@ -42,7 +42,6 @@
"GaussianRandomProjection",
"GradientBoostingClassifier",
"GradientBoostingRegressor",
"GraphicalLasso",
"GraphicalLassoCV",
"GridSearchCV",
"HalvingGridSearchCV",
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8 changes: 3 additions & 5 deletions sklearn/covariance/_empirical_covariance.py
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Expand Up @@ -218,8 +218,7 @@ def fit(self, X, y=None):
return self

def score(self, X_test, y=None):
"""Computes the log-likelihood of a Gaussian data set with
`self.covariance_` as an estimator of its covariance matrix.
"""Compute the log-likelihood of a Gaussian data set with `self.covariance_`.

Parameters
----------
Expand Down Expand Up @@ -247,8 +246,7 @@ def score(self, X_test, y=None):
return res

def error_norm(self, comp_cov, norm="frobenius", scaling=True, squared=True):
"""Computes the Mean Squared Error between two covariance estimators.
(In the sense of the Frobenius norm).
"""Compute the Mean Squared Error between two covariance estimators.

Parameters
----------
Expand Down Expand Up @@ -299,7 +297,7 @@ def error_norm(self, comp_cov, norm="frobenius", scaling=True, squared=True):
return result

def mahalanobis(self, X):
"""Computes the squared Mahalanobis distances of given observations.
"""Compute the squared Mahalanobis distances of given observations.

Parameters
----------
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15 changes: 9 additions & 6 deletions sklearn/covariance/_graph_lasso.py
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Expand Up @@ -372,6 +372,12 @@ class GraphicalLasso(EmpiricalCovariance):

.. versionadded:: 0.24

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

Examples
--------
>>> import numpy as np
Expand All @@ -392,10 +398,6 @@ class GraphicalLasso(EmpiricalCovariance):
[0.019, 0.034, 0.093, 0.69 ]])
>>> np.around(cov.location_, decimals=3)
array([0.073, 0.04 , 0.038, 0.143])

See Also
--------
graphical_lasso, GraphicalLassoCV
"""

def __init__(
Expand All @@ -418,19 +420,20 @@ def __init__(
self.verbose = verbose

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