DOC Ensures that SVC passes numpydoc validation (#20457)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
scikit-learn · Jul 13, 2021 · 9c2b3b6 · 9c2b3b6
1 parent 67eb690
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diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -128,7 +128,6 @@
     "RobustScaler",
     "SGDOneClassSVM",
     "SGDRegressor",
-    "SVC",
     "SVR",
     "SelectFdr",
     "SelectFpr",

diff --git a/sklearn/svm/_base.py b/sklearn/svm/_base.py
@@ -158,6 +158,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            Fitted estimator.
 
         Notes
         -----
@@ -617,6 +618,12 @@ def _validate_for_predict(self, X):
 
     @property
     def coef_(self):
+        """Weights assigned to the features when `kernel="linear"`.
+
+        Returns
+        -------
+        ndarray of shape (n_features, n_classes)
+        """
         if self.kernel != "linear":
             raise AttributeError("coef_ is only available when using a linear kernel")
 
@@ -637,6 +644,7 @@ def _get_coef(self):
 
     @property
     def n_support_(self):
+        """Number of support vectors for each class."""
         try:
             check_is_fitted(self)
         except NotFittedError:
@@ -710,11 +718,12 @@ def _validate_targets(self, y):
         return np.asarray(y, dtype=np.float64, order="C")
 
     def decision_function(self, X):
-        """Evaluates the decision function for the samples in X.
+        """Evaluate the decision function for the samples in X.
 
         Parameters
         ----------
         X : array-like of shape (n_samples, n_features)
+            The input samples.
 
         Returns
         -------
@@ -940,10 +949,22 @@ def _get_coef(self):
 
     @property
     def probA_(self):
+        """Parameter learned in Platt scaling when `probability=True`.
+
+        Returns
+        -------
+        ndarray of shape  (n_classes * (n_classes - 1) / 2)
+        """
         return self._probA
 
     @property
     def probB_(self):
+        """Parameter learned in Platt scaling when `probability=True`.
+
+        Returns
+        -------
+        ndarray of shape  (n_classes * (n_classes - 1) / 2)
+        """
         return self._probB
 
 

diff --git a/sklearn/svm/_classes.py b/sklearn/svm/_classes.py
@@ -574,7 +574,7 @@ class SVC(BaseSVC):
         weight one.
         The "balanced" mode uses the values of y to automatically adjust
         weights inversely proportional to class frequencies in the input data
-        as ``n_samples / (n_classes * np.bincount(y))``
+        as ``n_samples / (n_classes * np.bincount(y))``.
 
     verbose : bool, default=False
         Enable verbose output. Note that this setting takes advantage of a
@@ -675,22 +675,6 @@ class SVC(BaseSVC):
     shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
         Array dimensions of training vector ``X``.
 
-    Examples
-    --------
-    >>> import numpy as np
-    >>> from sklearn.pipeline import make_pipeline
-    >>> from sklearn.preprocessing import StandardScaler
-    >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
-    >>> y = np.array([1, 1, 2, 2])
-    >>> from sklearn.svm import SVC
-    >>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
-    >>> clf.fit(X, y)
-    Pipeline(steps=[('standardscaler', StandardScaler()),
-                    ('svc', SVC(gamma='auto'))])
-
-    >>> print(clf.predict([[-0.8, -1]]))
-    [1]
-
     See Also
     --------
     SVR : Support Vector Machine for Regression implemented using libsvm.
@@ -707,6 +691,22 @@ class SVC(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
+    >>> from sklearn.pipeline import make_pipeline
+    >>> from sklearn.preprocessing import StandardScaler
+    >>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
+    >>> y = np.array([1, 1, 2, 2])
+    >>> from sklearn.svm import SVC
+    >>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
+    >>> clf.fit(X, y)
+    Pipeline(steps=[('standardscaler', StandardScaler()),
+                    ('svc', SVC(gamma='auto'))])
+
+    >>> print(clf.predict([[-0.8, -1]]))
+    [1]
     """
 
     _impl = "c_svc"