FIX OvR/OvO classifier decision_function shape fixes (#9100)

* fix OVR classifier edgecase bugs

* add regression tests for OVO and OVR decision function shapes

doc/whats_new.rst

@@ -418,6 +418,11 @@ API changes summary
      :func:`model_selection.cross_val_predict`.
      :issue:`2879` by :user:`Stephen Hoover <stephen-hoover>`.
 
+   - The ``decision_function`` output shape for binary classification in
+     :class:`multi_class.OneVsRestClassifier` and
+     :class:`multi_class.OneVsOneClassifier` is now ``(n_samples,)`` to conform
+     to scikit-learn conventions. :issue:`9100` by `Andreas Müller`_.
+
    - Gradient boosting base models are no longer estimators. By `Andreas Müller`_.
 
    - :class:`feature_selection.SelectFromModel` now validates the ``threshold``

sklearn/multiclass.py

@@ -368,6 +368,8 @@ def decision_function(self, X):
         T : array-like, shape = [n_samples, n_classes]
         """
         check_is_fitted(self, 'estimators_')
+        if len(self.estimators_) == 1:
+            return self.estimators_[0].decision_function(X)
         return np.array([est.decision_function(X).ravel()
                          for est in self.estimators_]).T
 
@@ -574,6 +576,8 @@ def predict(self, X):
             Predicted multi-class targets.
         """
         Y = self.decision_function(X)
+        if self.n_classes_ == 2:
+            return self.classes_[(Y > 0).astype(np.int)]
         return self.classes_[Y.argmax(axis=1)]
 
     def decision_function(self, X):
@@ -606,7 +610,8 @@ def decision_function(self, X):
                                  for est, Xi in zip(self.estimators_, Xs)]).T
         Y = _ovr_decision_function(predictions,
                                    confidences, len(self.classes_))
-
+        if self.n_classes_ == 2:
+            return Y[:, 1]
         return Y
 
     @property

sklearn/tests/test_multiclass.py

@@ -251,6 +251,9 @@ def conduct_test(base_clf, test_predict_proba=False):
         assert_equal(set(clf.classes_), classes)
         y_pred = clf.predict(np.array([[0, 0, 4]]))[0]
         assert_equal(set(y_pred), set("eggs"))
+        if hasattr(base_clf, 'decision_function'):
+            dec = clf.decision_function(X)
+            assert_equal(dec.shape, (5,))
 
         if test_predict_proba:
             X_test = np.array([[0, 0, 4]])
@@ -524,6 +527,12 @@ def test_ovo_decision_function():
     n_samples = iris.data.shape[0]
 
     ovo_clf = OneVsOneClassifier(LinearSVC(random_state=0))
+    # first binary
+    ovo_clf.fit(iris.data, iris.target == 0)
+    decisions = ovo_clf.decision_function(iris.data)
+    assert_equal(decisions.shape, (n_samples,))
+
+    # then multi-class
     ovo_clf.fit(iris.data, iris.target)
     decisions = ovo_clf.decision_function(iris.data)