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DOC Ensures that SpectralCoclustering passes numpydoc validation #21282

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DOC Ensures that SpectralCoclustering passes numpydoc validation #21282

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SpectralCoclustering pass
pdavis327 Oct 7, 2021
a636eab
DOC Ensures that StandardScaler passes numpydoc validation
pdavis327 Oct 8, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Expand Up @@ -26,7 +26,6 @@
"SelfTrainingClassifier",
"SparseRandomProjection",
"SpectralBiclustering",
"SpectralCoclustering",
"SpectralEmbedding",
"StackingRegressor",
"TransformedTargetRegressor",
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1 change: 0 additions & 1 deletion sklearn/base.py
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Expand Up @@ -758,7 +758,6 @@ def get_indices(self, i):
Indices of rows in the dataset that belong to the bicluster.
col_ind : ndarray, dtype=np.intp
Indices of columns in the dataset that belong to the bicluster.

"""
rows = self.rows_[i]
columns = self.columns_[i]
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29 changes: 20 additions & 9 deletions sklearn/cluster/_bicluster.py
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Expand Up @@ -114,14 +114,20 @@ def _check_parameters(self):
)

def fit(self, X, y=None):
"""Creates a biclustering for X.
"""Create a biclustering for X.

Parameters
----------
X : array-like of shape (n_samples, n_features)
Training instances to cluster.

y : Ignored
Not used.
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Replace with

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


Returns
-------
self: object
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Replace with

self : object

Fitted biclustering for X.
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Replace with

Returns a fitted instance.

"""
X = self._validate_data(X, accept_sparse="csr", dtype=np.float64)
self._check_parameters()
Expand Down Expand Up @@ -255,6 +261,13 @@ class SpectralCoclustering(BaseSpectral):
initialization. Use an int to make the randomness deterministic.
See :term:`Glossary <random_state>`.

References
----------

* Dhillon, Inderjit S, 2001. `Co-clustering documents and words using
bipartite spectral graph partitioning
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3011>`__.

Attributes
----------
rows_ : array-like of shape (n_row_clusters, n_rows)
Expand Down Expand Up @@ -284,6 +297,12 @@ class SpectralCoclustering(BaseSpectral):

.. versionadded:: 1.0

See Also
--------
SpectralClustering: Performs a low-dimension embedding of the
affinity matrix between samples, followed by clustering, e.g., by KMeans,
of the components of the eigenvectors in the low dimensional space.
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I am not able to make suggestions. I assume that when opening the PR, you unchecked the checkbox that allows maintainers to make some editing.

Here, you should replace with:

SpectralClustering : Apply clustering to a projection of the normalized Laplacian.


Examples
--------
>>> from sklearn.cluster import SpectralCoclustering
Expand All @@ -297,14 +316,6 @@ class SpectralCoclustering(BaseSpectral):
array([0, 0], dtype=int32)
>>> clustering
SpectralCoclustering(n_clusters=2, random_state=0)

References
----------

* Dhillon, Inderjit S, 2001. `Co-clustering documents and words using
bipartite spectral graph partitioning
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.3011>`__.

"""

def __init__(
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