DOC Ensures that NuSVC passes numpydoc validation (#20461)

jmloyola · web-flow · commit 0a7e9eedcfe9 · 2021-07-20T17:34:22.000+02:00
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -82,7 +82,6 @@
     "NearestCentroid",
     "NeighborhoodComponentsAnalysis",
     "Normalizer",
-    "NuSVC",
     "NuSVR",
     "Nystroem",
     "OAS",
diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py
@@ -823,7 +823,7 @@ class NuSVC(BaseSVC):
         SVC. If not given, all classes are supposed to have
         weight one. The "balanced" mode uses the values of y to automatically
         adjust weights inversely proportional to class frequencies as
-        ``n_samples / (n_classes * np.bincount(y))``
+        ``n_samples / (n_classes * np.bincount(y))``.
 
     verbose : bool, default=False
         Enable verbose output. Note that this setting takes advantage of a
@@ -927,20 +927,6 @@ class NuSVC(BaseSVC):
     shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
         Array dimensions of training vector ``X``.
 
-    Examples
-    --------
-    >>> import numpy as np
-    >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
-    >>> y = np.array([1, 1, 2, 2])
-    >>> from sklearn.pipeline import make_pipeline
-    >>> from sklearn.preprocessing import StandardScaler
-    >>> from sklearn.svm import NuSVC
-    >>> clf = make_pipeline(StandardScaler(), NuSVC())
-    >>> clf.fit(X, y)
-    Pipeline(steps=[('standardscaler', StandardScaler()), ('nusvc', NuSVC())])
-    >>> print(clf.predict([[-0.8, -1]]))
-    [1]
-
     See Also
     --------
     SVC : Support Vector Machine for classification using libsvm.
@@ -956,6 +942,20 @@ class NuSVC(BaseSVC):
     .. [2] `Platt, John (1999). "Probabilistic outputs for support vector
         machines and comparison to regularizedlikelihood methods."
         <http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639>`_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
+    >>> y = np.array([1, 1, 2, 2])
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> from sklearn.svm import NuSVC
+    >>> clf = make_pipeline(StandardScaler(), NuSVC())
+    >>> clf.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()), ('nusvc', NuSVC())])
+    >>> print(clf.predict([[-0.8, -1]]))
+    [1]
     """
 
     _impl = "nu_svc"
