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DOC Ensures that SpectralCoclustering passes numpydoc validation #21463

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Oct 26, 2021
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DOC Ensures that SpectralCoclustering passes numpydoc validation #21463

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26f808e
Remove SpectralCoclustering from DOCSTRING_IGNORE_LIST
Icyshaman Oct 26, 2021
a515718
Made changes in documentation of SpectralCoclustering
Icyshaman Oct 26, 2021
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4 changes: 1 addition & 3 deletions maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,9 +12,7 @@
numpydoc_validation = pytest.importorskip("numpydoc.validate")

# List of modules ignored when checking for numpydoc validation.
DOCSTRING_IGNORE_LIST = [
"SpectralCoclustering",
]
DOCSTRING_IGNORE_LIST = []

FUNCTION_DOCSTRING_IGNORE_LIST = [
"sklearn._config.config_context",
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22 changes: 11 additions & 11 deletions sklearn/cluster/_bicluster.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,6 @@ def _scale_normalize(X):

Returns the normalized matrix and the row and column scaling
factors.

"""
X = make_nonnegative(X)
row_diag = np.asarray(1.0 / np.sqrt(X.sum(axis=1))).squeeze()
Expand All @@ -48,7 +47,6 @@ def _bistochastic_normalize(X, max_iter=1000, tol=1e-5):
"""Normalize rows and columns of ``X`` simultaneously so that all
rows sum to one constant and all columns sum to a different
constant.

"""
# According to paper, this can also be done more efficiently with
# deviation reduction and balancing algorithms.
Expand Down Expand Up @@ -137,7 +135,6 @@ def fit(self, X, y=None):
def _svd(self, array, n_components, n_discard):
"""Returns first `n_components` left and right singular
vectors u and v, discarding the first `n_discard`.

"""
if self.svd_method == "randomized":
kwargs = {}
Expand Down Expand Up @@ -290,6 +287,17 @@ class SpectralCoclustering(BaseSpectral):

.. versionadded:: 1.0

See Also
--------
SpectralBiclustering : Partitions rows and columns under the assumption
that the data has an underlying checkerboard structure.

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>`__.

Examples
--------
>>> from sklearn.cluster import SpectralCoclustering
Expand All @@ -303,14 +311,6 @@ 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__(
Expand Down