DOC Ensures that sklearn.utils.extmath.weighted_mode passes numpydoc validation (#24571)

awinml · jeremiedbb · web-flow · commit c674e589f9aa · 2022-10-07T13:05:54.000+02:00
Co-authored-by: jeremie du boisberranger &lt;jeremiedbb@yahoo.fr&gt;
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
@@ -15,7 +15,6 @@
     "sklearn.utils.extmath.fast_logdet",
     "sklearn.utils.extmath.randomized_svd",
     "sklearn.utils.extmath.svd_flip",
-    "sklearn.utils.extmath.weighted_mode",
     "sklearn.utils.fixes.delayed",
     # To be fixed in upstream issue:
     # https://github.com/joblib/threadpoolctl/issues/108
diff --git a/sklearn/utils/extmath.py b/sklearn/utils/extmath.py
@@ -605,7 +605,7 @@ def _randomized_eigsh(
 
 
 def weighted_mode(a, w, *, axis=0):
-    """Returns an array of the weighted modal (most common) value in a.
+    """Return an array of the weighted modal (most common) value in the passed array.
 
     If there is more than one such value, only the first is returned.
     The bin-count for the modal bins is also returned.
@@ -614,10 +614,10 @@ def weighted_mode(a, w, *, axis=0):
 
     Parameters
     ----------
-    a : array-like
-        n-dimensional array of which to find mode(s).
-    w : array-like
-        n-dimensional array of weights for each value.
+    a : array-like of shape (n_samples,)
+        Array of which values to find mode(s).
+    w : array-like of shape (n_samples,)
+        Array of weights for each value.
     axis : int, default=0
         Axis along which to operate. Default is 0, i.e. the first axis.
 
@@ -628,6 +628,11 @@ def weighted_mode(a, w, *, axis=0):
     score : ndarray
         Array of weighted counts for each mode.
 
+    See Also
+    --------
+    scipy.stats.mode: Calculates the Modal (most common) value of array elements
+        along specified axis.
+
     Examples
     --------
     >>> from sklearn.utils.extmath import weighted_mode
@@ -645,10 +650,6 @@ def weighted_mode(a, w, *, axis=0):
 
     The value 2 has the highest score: it appears twice with weights of
     1.5 and 2: the sum of these is 3.5.
-
-    See Also
-    --------
-    scipy.stats.mode
     """
     if axis is None:
         a = np.ravel(a)
