DOC Ensures that pair_confusion_matrix passes numpydoc validation (#24094)

Vincent-Maladiere · glemaitre · web-flow · commit 076442c815b1 · 2022-08-03T16:23:33.000+02:00
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py
@@ -159,7 +159,7 @@ def contingency_matrix(
 
 
 def pair_confusion_matrix(labels_true, labels_pred):
-    """Pair confusion matrix arising from two clusterings.
+    """Pair confusion matrix arising from two clusterings [1]_.
 
     The pair confusion matrix :math:`C` computes a 2 by 2 similarity matrix
     between two clusterings by considering all pairs of samples and counting
@@ -188,9 +188,15 @@ def pair_confusion_matrix(labels_true, labels_pred):
 
     See Also
     --------
-    rand_score: Rand Score
-    adjusted_rand_score: Adjusted Rand Score
-    adjusted_mutual_info_score: Adjusted Mutual Information
+    rand_score: Rand Score.
+    adjusted_rand_score: Adjusted Rand Score.
+    adjusted_mutual_info_score: Adjusted Mutual Information.
+
+    References
+    ----------
+    .. [1] :doi:`Hubert, L., Arabie, P. "Comparing partitions."
+           Journal of Classification 2, 193–218 (1985).
+           <10.1007/BF01908075>`
 
     Examples
     --------
@@ -211,12 +217,6 @@ def pair_confusion_matrix(labels_true, labels_pred):
              [0, 2]]...
 
     Note that the matrix is not symmetric.
-
-    References
-    ----------
-    .. L. Hubert and P. Arabie, Comparing Partitions, Journal of
-      Classification 1985
-      https://link.springer.com/article/10.1007%2FBF01908075
     """
     labels_true, labels_pred = check_clusterings(labels_true, labels_pred)
     n_samples = np.int64(labels_true.shape[0])
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -39,7 +39,6 @@
     "sklearn.metrics.cluster._supervised.adjusted_rand_score",
     "sklearn.metrics.cluster._supervised.entropy",
     "sklearn.metrics.cluster._supervised.fowlkes_mallows_score",
-    "sklearn.metrics.cluster._supervised.pair_confusion_matrix",
     "sklearn.metrics.cluster._supervised.rand_score",
     "sklearn.metrics.cluster._supervised.v_measure_score",
     "sklearn.metrics.pairwise.pairwise_distances_chunked",
