scikit-learn
diff --git a/‎scikits/learn/preprocessing/__init__.py
Lines changed: 38 additions & 2 deletions b/‎scikits/learn/preprocessing/__init__.py
Lines changed: 38 additions & 2 deletions
diff --git a/‎scikits/learn/preprocessing/tests/test_preprocessing.py
Lines changed: 11 additions & 0 deletions b/‎scikits/learn/preprocessing/tests/test_preprocessing.py
Lines changed: 11 additions & 0 deletions
@@ -128,6 +128,10 @@ def transform(self, X, copy=True):
         return X
 
 
+def _is_multilabel(y):
+    return isinstance(y[0], tuple) or isinstance(y[0], list)
+
+
 class LabelBinarizer(BaseEstimator, TransformerMixin):
     """Binarize labels in a one-vs-all fashion.
 
@@ -160,6 +164,10 @@ class LabelBinarizer(BaseEstimator, TransformerMixin):
     >>> clf.transform([1, 6])
     array([[ 1.,  0.,  0.,  0.],
            [ 0.,  0.,  0.,  1.]])
+
+    >>> clf.fit_transform([(1,2),(3,)])
+    array([[ 1.,  1.,  0.],
+           [ 0.,  0.,  1.]])
     """
 
     def fit(self, y):
@@ -174,7 +182,11 @@ def fit(self, y):
         -------
         self : returns an instance of self.
         """
-        self.classes_ = np.unique(y)
+        self.multilabel = _is_multilabel(y)
+        if self.multilabel:
+            self.classes_ = np.unique(reduce(lambda a,b:a+b, y))
+        else:
+            self.classes_ = np.unique(y)
         return self
 
     def transform(self, y):
@@ -192,13 +204,30 @@ def transform(self, y):
         -------
         Y : numpy array of shape [n_samples, n_classes]
         """
+
         if len(self.classes_) == 2:
             Y = np.zeros((len(y), 1))
+        else:
+            Y = np.zeros((len(y), len(self.classes_)))
+
+        if self.multilabel:
+            if not _is_multilabel(y):
+                raise ValueError, "y should be a list of label lists/tuples"
+
+            # inverse map: label => column index
+            imap = dict((v,k) for k,v in enumerate(self.classes_))
+
+            for i, label_tuple in enumerate(y):
+                for label in label_tuple:
+                    Y[i, imap[label]] = 1
+
+            return Y
+
+        elif len(self.classes_) == 2:
             Y[y == self.classes_[1], 0] = 1
             return Y
 
         elif len(self.classes_) >= 2:
-            Y = np.zeros((len(y), len(self.classes_)))
             for i, k in enumerate(self.classes_):
                 Y[y == k, i] = 1
             return Y
@@ -225,8 +254,15 @@ def inverse_transform(self, Y):
         this allows to use the output of a linear model's decision_function
         method directly as the input of inverse_transform.
         """
+        if self.multilabel:
+            Y = np.array(Y > 0, dtype=int)
+            return [tuple(self.classes_[np.flatnonzero(Y[i])])
+                    for i in range(Y.shape[0])]
+
         if len(Y.shape) == 1 or Y.shape[1] == 1:
             y = np.array(Y.ravel() > 0, dtype=int)
+
         else:
             y = Y.argmax(axis=1)
+
         return self.classes_[y]
@@ -150,6 +150,17 @@ def test_label_binarizer():
     assert_array_equal(expected, got)
     assert_array_equal(lb.inverse_transform(got), inp)
 
+def test_label_binarizer_multilabel():
+
+    inp = [(2, 3), (1,), (1, 2)]
+    expected = np.array([[0, 1, 1],
+                         [1, 0, 0],
+                         [1, 1, 0]])
+    got = lb.fit_transform(inp)
+    assert_array_equal(expected, got)
+    assert_equal(lb.inverse_transform(got), inp)
+
 def test_label_binarizer_iris():
     lb = LabelBinarizer()
     Y = lb.fit_transform(iris.target)