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DOC Ensures that orthogonal_mp passes numpydoc validation #22501

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Feb 16, 2022
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DOC Ensures that orthogonal_mp passes numpydoc validation #22501

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747b711
Add descriptions for orthogonal_mp, change wording to avoid triggerin…
combscCode Feb 16, 2022
b7d210b
Merge remote-tracking branch 'upstream/main' into docstrings-orthogon…
combscCode Feb 16, 2022
53a2bec
change yields/returns wording to generates
combscCode Feb 16, 2022
7f7970d
reduce line length to pass flake8
combscCode Feb 16, 2022
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11 changes: 5 additions & 6 deletions sklearn/linear_model/_omp.py
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Expand Up @@ -342,7 +342,7 @@ def orthogonal_mp(
Coefficients of the OMP solution. If `return_path=True`, this contains
the whole coefficient path. In this case its shape is
(n_features, n_features) or (n_features, n_targets, n_features) and
iterating over the last axis yields coefficients in increasing order
iterating over the last axis generates coefficients in increasing order
of active features.

n_iters : array-like or int
Expand All @@ -351,10 +351,10 @@ def orthogonal_mp(

See Also
--------
OrthogonalMatchingPursuit
orthogonal_mp_gram
lars_path
sklearn.decomposition.sparse_encode
OrthogonalMatchingPursuit : Orthogonal Matching Pursuit model.
orthogonal_mp_gram : Solve OMP problems using Gram matrix and the product X.T * y.
lars_path : Compute Least Angle Regression or Lasso path using LARS algorithm.
sklearn.decomposition.sparse_encode : Sparse coding.

Notes
-----
Expand All @@ -367,7 +367,6 @@ def orthogonal_mp(
M., Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal
Matching Pursuit Technical Report - CS Technion, April 2008.
https://www.cs.technion.ac.il/~ronrubin/Publications/KSVD-OMP-v2.pdf

"""
X = check_array(X, order="F", copy=copy_X)
copy_X = False
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1 change: 0 additions & 1 deletion sklearn/tests/test_docstrings.py
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Expand Up @@ -52,7 +52,6 @@
"sklearn.inspection._plot.partial_dependence.plot_partial_dependence",
"sklearn.linear_model._least_angle.lars_path",
"sklearn.linear_model._least_angle.lars_path_gram",
"sklearn.linear_model._omp.orthogonal_mp",
"sklearn.linear_model._omp.orthogonal_mp_gram",
"sklearn.linear_model._ridge.ridge_regression",
"sklearn.manifold._locally_linear.locally_linear_embedding",
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