DOC Ensures that silhouette_samples passes numpydoc (scikit-learn#21851)

mlant · KiraLazu · glemaitre · commit d7218551205f · 2021-12-25T12:26:49.000+01:00
Co-authored-by: mlant &lt;marie.lanternier@free.fr&gt;
diff --git a/sklearn/metrics/cluster/_unsupervised.py b/sklearn/metrics/cluster/_unsupervised.py
@@ -188,7 +188,7 @@ def silhouette_samples(X, labels, *, metric="euclidean", **kwds):
         If ``X`` is the distance array itself, use "precomputed" as the metric.
         Precomputed distance matrices must have 0 along the diagonal.
 
-    `**kwds` : optional keyword parameters
+    **kwds : optional keyword parameters
         Any further parameters are passed directly to the distance function.
         If using a ``scipy.spatial.distance`` metric, the parameters are still
         metric dependent. See the scipy docs for usage examples.
@@ -208,7 +208,6 @@ def silhouette_samples(X, labels, *, metric="euclidean", **kwds):
 
     .. [2] `Wikipedia entry on the Silhouette Coefficient
        <https://en.wikipedia.org/wiki/Silhouette_(clustering)>`_
-
     """
     X, labels = check_X_y(X, labels, accept_sparse=["csc", "csr"])
 
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -117,7 +117,6 @@
     "sklearn.metrics.cluster._supervised.pair_confusion_matrix",
     "sklearn.metrics.cluster._supervised.rand_score",
     "sklearn.metrics.cluster._supervised.v_measure_score",
-    "sklearn.metrics.cluster._unsupervised.silhouette_samples",
     "sklearn.metrics.pairwise.additive_chi2_kernel",
     "sklearn.metrics.pairwise.check_paired_arrays",
     "sklearn.metrics.pairwise.check_pairwise_arrays",
