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DOC Ensures that LassoCV passes numpydoc validation #20453

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Jul 7, 2021
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DOC Ensures that LassoCV passes numpydoc validation #20453

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56b3431
Fixed doc strings errors
jaglima Jul 3, 2021
c364d53
Update sklearn/linear_model/_coordinate_descent.py
jaglima Jul 6, 2021
863a647
Update sklearn/linear_model/_coordinate_descent.py
jaglima Jul 6, 2021
a81986e
Merge branch 'main' into doc_lassocv
glemaitre Jul 7, 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 @@ -70,7 +70,6 @@
"LabelPropagation",
"LabelSpreading",
"LarsCV",
"LassoCV",
"LatentDirichletAllocation",
"LedoitWolf",
"LinearSVR",
Expand Down
72 changes: 40 additions & 32 deletions sklearn/linear_model/_coordinate_descent.py
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Expand Up @@ -235,18 +235,18 @@ def lasso_path(

y : {array-like, sparse matrix} of shape (n_samples,) or \
(n_samples, n_targets)
Target values
Target values.

eps : float, default=1e-3
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``
``alpha_min / alpha_max = 1e-3``.

n_alphas : int, default=100
Number of alphas along the regularization path
Number of alphas along the regularization path.

alphas : ndarray, default=None
List of alphas where to compute the models.
If ``None`` alphas are set automatically
If ``None`` alphas are set automatically.

precompute : 'auto', bool or array-like of shape \
(n_features, n_features), default='auto'
Expand All @@ -269,14 +269,14 @@ def lasso_path(
Amount of verbosity.

return_n_iter : bool, default=False
whether to return the number of iterations or not.
Whether to return the number of iterations or not.

positive : bool, default=False
If set to True, forces coefficients to be positive.
(Only allowed when ``y.ndim == 1``).

**params : kwargs
keyword arguments passed to the coordinate descent solver.
Keyword arguments passed to the coordinate descent solver.

Returns
-------
Expand All @@ -294,6 +294,18 @@ def lasso_path(
The number of iterations taken by the coordinate descent optimizer to
reach the specified tolerance for each alpha.

See Also
--------
lars_path : Compute Least Angle Regression or Lasso path using LARS
algorithm.
Lasso : The Lasso is a linear model that estimates sparse coefficients.
LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
LassoCV : Lasso linear model with iterative fitting along a regularization
path.
LassoLarsCV : Cross-validated Lasso using the LARS algorithm.
sklearn.decomposition.sparse_encode : Estimator that can be used to
transform signals into sparse linear combination of atoms from a fixed.

Notes
-----
For an example, see
Expand Down Expand Up @@ -331,15 +343,6 @@ def lasso_path(
>>> print(coef_path_continuous([5., 1., .5]))
[[0. 0. 0.46915237]
[0.2159048 0.4425765 0.23668876]]

See Also
--------
lars_path
Lasso
LassoLars
LassoCV
LassoLarsCV
sklearn.decomposition.sparse_encode
"""
return enet_path(
X,
Expand Down Expand Up @@ -1481,6 +1484,7 @@ def fit(self, X, y, sample_weight=None):
Returns
-------
self : object
Returns an instance of fitted model.
"""

# Do as _deprecate_normalize but without warning as it's raised
Expand Down Expand Up @@ -1840,16 +1844,16 @@ class LassoCV(RegressorMixin, LinearModelCV):

.. versionadded:: 0.24

Examples
See Also
--------
>>> from sklearn.linear_model import LassoCV
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(noise=4, random_state=0)
>>> reg = LassoCV(cv=5, random_state=0).fit(X, y)
>>> reg.score(X, y)
0.9993...
>>> reg.predict(X[:1,])
array([-78.4951...])
lars_path : Compute Least Angle Regression or Lasso path using LARS
algorithm.
lasso_path : Compute Lasso path with coordinate descent.
Lasso : The Lasso is a linear model that estimates sparse coefficients.
LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
LassoCV : Lasso linear model with iterative fitting along a regularization
path.
LassoLarsCV : Cross-validated Lasso using the LARS algorithm.

Notes
-----
Expand All @@ -1860,13 +1864,16 @@ class LassoCV(RegressorMixin, LinearModelCV):
To avoid unnecessary memory duplication the X argument of the fit method
should be directly passed as a Fortran-contiguous numpy array.

See Also
Examples
--------
lars_path
lasso_path
LassoLars
Lasso
LassoLarsCV
>>> from sklearn.linear_model import LassoCV
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(noise=4, random_state=0)
>>> reg = LassoCV(cv=5, random_state=0).fit(X, y)
>>> reg.score(X, y)
0.9993...
>>> reg.predict(X[:1,])
array([-78.4951...])
"""

path = staticmethod(lasso_path)
Expand Down Expand Up @@ -3013,12 +3020,13 @@ def fit(self, X, y):
Parameters
----------
X : ndarray of shape (n_samples, n_features)
Data
Data.
y : ndarray of shape (n_samples, n_targets)
Target. Will be cast to X's dtype if necessary
Target. Will be cast to X's dtype if necessary.

Returns
-------
self : object
Returns an instance of fitted model.
"""
return super().fit(X, y)