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DOC Ensures that NuSVC passes numpydoc validation #20461

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Merged
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Jul 20, 2021
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DOC Ensures that NuSVC passes numpydoc validation #20461

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a114063
Remove NuSVC from DOCSTRING_IGNORE_LIST.
jmloyola Jul 3, 2021
281fcf7
Fix numpydocs from NuSVC.
jmloyola Jul 3, 2021
30615bb
Merge 'upstream/main' into fix_numpydoc_nusvc
jmloyola Jul 13, 2021
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1 change: 0 additions & 1 deletion maint_tools/test_docstrings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,7 +89,6 @@
"NearestCentroid",
"NeighborhoodComponentsAnalysis",
"Normalizer",
"NuSVC",
"NuSVR",
"Nystroem",
"OAS",
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30 changes: 15 additions & 15 deletions sklearn/svm/_classes.py
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Expand Up @@ -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
Expand Down Expand Up @@ -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.
Expand All @@ -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"
Expand Down