fixed issue scikit-learn#3450 for hamming loss

sklearn/metrics/classification.py

@@ -202,6 +202,7 @@ def confusion_matrix(y_true, y_pred, labels=None):
         If none is given, those that appear at least once
         in ``y_true`` or ``y_pred`` are used in sorted order.
 
+
     Returns
     -------
     C : array, shape = [n_classes, n_classes]
@@ -1392,7 +1393,7 @@ class 2       1.00      0.67      0.80         3
     return report
 
 
-def hamming_loss(y_true, y_pred, classes=None):
+def hamming_loss(y_true, y_pred, classes=None, sample_weight=None):
     """Compute the average Hamming loss.
 
     The Hamming loss is the fraction of labels that are incorrectly predicted.
@@ -1410,6 +1411,9 @@ def hamming_loss(y_true, y_pred, classes=None):
     classes : array, shape = [n_labels], optional
         Integer array of labels.
 
+    sample_weight : array-like of shape = [n_samples], optional
+        Sample weights.
+
     Returns
     -------
     loss : float or int,
@@ -1457,6 +1461,7 @@ def hamming_loss(y_true, y_pred, classes=None):
     >>> hamming_loss(np.array([[0, 1], [1, 1]]), np.zeros((2, 2)))
     0.75
     """
+    check_consistent_length(y_true, y_pred, sample_weight)
     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
 
     if classes is None:
@@ -1465,11 +1470,11 @@ def hamming_loss(y_true, y_pred, classes=None):
         classes = np.asarray(classes)
 
     if y_type.startswith('multilabel'):
-        n_differences = count_nonzero(y_true - y_pred)
+        n_differences = count_nonzero(y_true - y_pred, sample_weight)
         return (n_differences / (y_true.shape[0] * len(classes)))
 
     elif y_type in ["binary", "multiclass"]:
-        return sp_hamming(y_true, y_pred)
+        return _weighted_sum(y_true != y_pred, sample_weight, normalize=True)
     else:
         raise ValueError("{0} is not supported".format(y_type))