[GitHub] piiswrong closed pull request #6496: Add hinge loss for svm

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piiswrong closed pull request #6496: Add hinge loss for svm
URL: https://github.com/apache/incubator-mxnet/pull/6496
 
 
   

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diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py
index c57f12fc5d..fb91c30cdb 100644
--- a/python/mxnet/metric.py
+++ b/python/mxnet/metric.py
@@ -941,6 +941,70 @@ def __init__(self, name='caffe',
             name, output_names=output_names, label_names=label_names)
 
 
+@register
+class HingeLoss(EvalMetric):
+    """Computes Hinge loss for SVM.
+
+    The hinge loss for one example:
+
+    .. math::
+        \\L_i={\sum_{j\neq y_i}max(0, w_j^Tx_i-w_{y_i}^Tx_i+margin)}
+
+    Parameters
+    ----------
+    name : str
+        Name of this metric instance for display.
+    use_linear : boolean, Optional
+        Whether to use L1-regularized objective (the default is False).
+    margin : float, Optional
+        Margin for the SVM (the default is 1.0).
+    output_names : list of str, or None
+        Name of predictions that should be used when updating with update_dict.
+        By default include all predictions.
+    label_names : list of str, or None
+        Name of labels that should be used when updating with update_dict.
+        By default include all labels.
+
+    Examples
+    --------
+    >>> predicts = [mx.nd.array(np.array([-1.73, -1.24, 0.89, -0.99, 0.05]).reshape(1, -1))]
+    >>> labels = [mx.nd.array(np.array([2]))]
+    >>> hinge_loss_l1 = mx.metric.HingeLoss(use_linear=True)
+    >>> hinge_loss_l1.update(labels = labels, preds = predicts)
+    >>> print hinge_loss_l1.get()
+    ('hinge_loss', 0.1600000262260437)
+    >>> hinge_loss_l2 = mx.metric.HingeLoss()
+    >>> hinge_loss_l2.update(labels = labels, preds = predicts)
+    >>> print hinge_loss_l2.get()
+    ('hinge_loss', 0.02560000866651535)
+    """
+    def __init__(self, name='hinge_loss', use_linear=False, margin=1.0,
+                 output_names=None, label_names=None):
+        super(HingeLoss, self).__init__(
+            name, output_names=output_names, label_names=label_names)
+        self.use_linear = use_linear
+        self.margin = margin
+
+    def norm(self, x):
+        return x if self.use_linear else x**2.0
+
+    def update(self, labels, preds):
+        check_label_shapes(labels, preds)
+
+        for label, pred in zip(labels, preds):
+            n = label.shape[0]
+            pred = pred.asnumpy()
+            label = label.asnumpy().astype('int32')
+
+            pred = pred - pred[numpy.arange(n), label].reshape(-1, 1) + self.margin
+            pred[numpy.arange(n), label] = 0
+
+            loss = numpy.maximum(0, pred)
+
+            self.sum_metric += numpy.sum(self.norm(loss))
+            self.num_inst += n
+
+
 @register
 class CustomMetric(EvalMetric):
     """Computes a customized evaluation metric.


 

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