ENH addressed @ogrisel's comments.

Author: amueller

sklearn/svm/tests/test_svm.py

@@ -339,7 +339,7 @@ def test_auto_weight():
     from sklearn.utils import compute_class_weight
     X, y = iris.data[:, :2], iris.target
     unbalanced = np.delete(np.arange(y.size), np.where(y > 1)[0][::2])
-    
+
     classes = np.unique(y[unbalanced])
     class_weights = compute_class_weight('auto', classes, y[unbalanced])
     assert_true(np.argmax(class_weights) == 2)

sklearn/utils/__init__.py

@@ -353,11 +353,12 @@ def compute_class_weight(class_weight, classes, y):
 
     Parameters
     ----------
-    class_weight : dict or 'auto'
+    class_weight : dict, 'auto' or None
         If 'auto', class weights will be given inverse proportional
         to the frequency of the class in the data.
         If a dictionary is given, keys are classes and values
         are corresponding class weights.
+        If None is given, the class weights will be uniform.
     classes : list
         List of the classes occuring in the data, as given by
         ``np.unique(y_org)`` with ``y_org`` the original class labels.