DOC Ensure completeness_score passes numpydoc validation (#23016)

aj-white · web-flow · commit 59428da95751 · 2022-04-01T17:14:36.000-04:00
diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py
@@ -554,7 +554,7 @@ def homogeneity_score(labels_true, labels_pred):
 
 
 def completeness_score(labels_true, labels_pred):
-    """Completeness metric of a cluster labeling given a ground truth.
+    """Compute completeness metric of a cluster labeling given a ground truth.
 
     A clustering result satisfies completeness if all the data points
     that are members of a given class are elements of the same cluster.
@@ -572,15 +572,20 @@ def completeness_score(labels_true, labels_pred):
     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.
 
     Returns
     -------
     completeness : 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.
+    v_measure_score : V-Measure (NMI with arithmetic mean option).
 
     References
     ----------
@@ -589,11 +594,6 @@ def completeness_score(labels_true, labels_pred):
        conditional entropy-based external cluster evaluation measure
        <https://aclweb.org/anthology/D/D07/D07-1043.pdf>`_
 
-    See Also
-    --------
-    homogeneity_score
-    v_measure_score
-
     Examples
     --------
 
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -59,7 +59,6 @@
     "sklearn.metrics.cluster._bicluster.consensus_score",
     "sklearn.metrics.cluster._supervised.adjusted_mutual_info_score",
     "sklearn.metrics.cluster._supervised.adjusted_rand_score",
-    "sklearn.metrics.cluster._supervised.completeness_score",
     "sklearn.metrics.cluster._supervised.entropy",
     "sklearn.metrics.cluster._supervised.fowlkes_mallows_score",
     "sklearn.metrics.cluster._supervised.homogeneity_completeness_v_measure",
