TST small improvement of test for sample weight in svm

examples/svm/plot_weighted_samples.py

@@ -9,6 +9,8 @@ is proportional to its weight.
 The sample weighting rescales the C parameter, which means that the classifier
 puts more emphasis on getting these points right. The effect might often be
 subtle.
+To emphasis the effect here, we particularly weight outliers, making the
+deformation of the decision boundary very visible.
 """
 print(__doc__)
 
@@ -39,8 +41,9 @@ X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
 Y = [1] * 10 + [-1] * 10
 sample_weight_last_ten = abs(np.random.randn(len(X)))
 sample_weight_constant = np.ones(len(X))
-# and assign a bigger weight to the last 5 samples
+# and bigger weights to some outliers
 sample_weight_last_ten[15:] *= 5
+sample_weight_last_ten[9] *= 15
 
 # for reference, first fit without class weights
 

sklearn/svm/base.py

@@ -104,7 +104,7 @@ class BaseLibSVM(six.with_metaclass(ABCMeta, BaseEstimator)):
             Training vectors, where n_samples is the number of samples
             and n_features is the number of features.
 
-        y : array-like, shape (n_samples)
+        y : array-like, shape (n_samples,)
             Target values (class labels in classification, real numbers in
             regression)
 
@@ -268,7 +268,7 @@ class BaseLibSVM(six.with_metaclass(ABCMeta, BaseEstimator)):
 
         Returns
         -------
-        y_pred : array, shape (n_samples)
+        y_pred : array, shape (n_samples,)
         """
         X = self._validate_for_predict(X)
         predict = self._sparse_predict if self._sparse else self._dense_predict

sklearn/svm/tests/test_svm.py

@@ -340,6 +340,14 @@ def test_sample_weights():
     clf.fit(X, Y, sample_weight=sample_weight)
     assert_array_equal(clf.predict(X[2]), [2.])
 
+    # test that rescaling all samples is the same as changing C
+    clf = svm.SVC()
+    clf.fit(X, Y)
+    dual_coef_no_weight = clf.dual_coef_
+    clf.set_params(C=100)
+    clf.fit(X, Y, sample_weight=np.repeat(0.01, len(X)))
+    assert_array_almost_equal(dual_coef_no_weight, clf.dual_coef_)
+
 
 def test_auto_weight():
     """Test class weights for imbalanced data"""