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DOC Ensures Lasso passes numpydoc validation #20409

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Jun 27, 2021
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DOC Ensures Lasso passes numpydoc validation #20409

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562593a
Modified _coordinate_descent.py and base.py files
jaglima Jun 27, 2021
354fad9
Modified _coordinate_descent.py and base.py files - flake8 fix
jaglima Jun 27, 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 @@ -88,7 +88,6 @@
"LabelSpreading",
"Lars",
"LarsCV",
"Lasso",
"LassoCV",
"LassoLars",
"LassoLarsCV",
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3 changes: 1 addition & 2 deletions sklearn/base.py
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Expand Up @@ -558,8 +558,7 @@ class RegressorMixin:
_estimator_type = "regressor"

def score(self, X, y, sample_weight=None):
"""Return the coefficient of determination :math:`R^2` of the
prediction.
"""Return the coefficient of determination :math:`R^2` of the prediction.

The coefficient :math:`R^2` is defined as :math:`(1 - \\frac{u}{v})`,
where :math:`u` is the residual sum of squares ``((y_true - y_pred)
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42 changes: 22 additions & 20 deletions sklearn/linear_model/_coordinate_descent.py
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Expand Up @@ -478,10 +478,11 @@ def enet_path(

See Also
--------
MultiTaskElasticNet
MultiTaskElasticNetCV
ElasticNet
ElasticNetCV
MultiTaskElasticNet : Multi-task ElasticNet model trained with L1/L2 mixed-norm \
as regularizer.
MultiTaskElasticNetCV : Multi-task L1/L2 ElasticNet with built-in cross-validation.
ElasticNet : Linear regression with combined L1 and L2 priors as regularizer.
ElasticNetCV : Elastic Net model with iterative fitting along a regularization path.

Notes
-----
Expand Down Expand Up @@ -865,6 +866,7 @@ def fit(self, X, y, sample_weight=None, check_input=True):
Returns
-------
self : object
Fitted estimator.

Notes
-----
Expand Down Expand Up @@ -1178,6 +1180,22 @@ class Lasso(ElasticNet):

.. versionadded:: 0.24

See Also
--------
lars_path : Regularization path using LARS.
lasso_path : Regularization path using Lasso.
LassoLars : Lasso Path along the regularization parameter usingLARS algorithm.
LassoCV : Lasso alpha parameter by cross-validation.
LassoLarsCV : Lasso least angle parameter algorithm by cross-validation.
sklearn.decomposition.sparse_encode : Sparse coding array estimator.

Notes
-----
The algorithm used to fit the model is coordinate descent.

To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.

Examples
--------
>>> from sklearn import linear_model
Expand All @@ -1188,22 +1206,6 @@ class Lasso(ElasticNet):
[0.85 0. ]
>>> print(clf.intercept_)
0.15...

See Also
--------
lars_path
lasso_path
LassoLars
LassoCV
LassoLarsCV
sklearn.decomposition.sparse_encode

Notes
-----
The algorithm used to fit the model is coordinate descent.

To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.
"""

path = staticmethod(enet_path)
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