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DOC Ensures v_measure_score passes numpydoc validation #24097

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Aug 4, 2022
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DOC Ensures v_measure_score passes numpydoc validation #24097

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60a5027
Fix doc validation error for `v_measure_score`
Vincent-Maladiere Aug 3, 2022
d5a731a
Merge branch 'main' into fix_doc_v_measure_score
Vincent-Maladiere Aug 4, 2022
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20 changes: 9 additions & 11 deletions sklearn/metrics/cluster/_supervised.py
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Original file line number Diff line number Diff line change
Expand Up @@ -642,16 +642,15 @@ def v_measure_score(labels_true, labels_pred, *, beta=1.0):
measure the agreement of two independent label assignments strategies
on the same dataset when the real ground truth is not known.


Read more in the :ref:`User Guide <homogeneity_completeness>`.

Parameters
----------
labels_true : int array, shape = [n_samples]
ground truth class labels to be used as a reference
Ground truth class labels to be used as a reference.

labels_pred : array-like of shape (n_samples,)
cluster labels to evaluate
Cluster labels to evaluate.

beta : float, default=1.0
Ratio of weight attributed to ``homogeneity`` vs ``completeness``.
Expand All @@ -662,7 +661,13 @@ def v_measure_score(labels_true, labels_pred, *, beta=1.0):
Returns
-------
v_measure : float
score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling
Score between 0.0 and 1.0. 1.0 stands for perfectly complete labeling.

See Also
--------
homogeneity_score : Homogeneity metric of cluster labeling.
completeness_score : Completeness metric of cluster labeling.
normalized_mutual_info_score : Normalized Mutual Information.

References
----------
Expand All @@ -671,15 +676,8 @@ def v_measure_score(labels_true, labels_pred, *, beta=1.0):
conditional entropy-based external cluster evaluation measure
<https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_

See Also
--------
homogeneity_score
completeness_score
normalized_mutual_info_score

Examples
--------

Perfect labelings are both homogeneous and complete, hence have score 1.0::

>>> from sklearn.metrics.cluster import v_measure_score
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1 change: 0 additions & 1 deletion sklearn/tests/test_docstrings.py
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Expand Up @@ -40,7 +40,6 @@
"sklearn.metrics.cluster._supervised.entropy",
"sklearn.metrics.cluster._supervised.fowlkes_mallows_score",
"sklearn.metrics.cluster._supervised.rand_score",
"sklearn.metrics.cluster._supervised.v_measure_score",
"sklearn.metrics.pairwise.pairwise_distances_chunked",
"sklearn.preprocessing._data.maxabs_scale",
"sklearn.preprocessing._data.scale",
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