sxjscience commented on a change in pull request #9777: [MX-9588] Add micro 
averaging strategy for F1 metric
URL: https://github.com/apache/incubator-mxnet/pull/9777#discussion_r167992849
 
 

 ##########
 File path: python/mxnet/metric.py
 ##########
 @@ -475,8 +475,84 @@ def update(self, labels, preds):
             self.num_inst += num_samples
 
 
+class _BinaryClassificationMixin(object):
+    """
+    Private mixin for keeping track of TPR, FPR, TNR, FNR counts for a 
classification metric.
+    """
+
+    def __init__(self):
+        self._true_positives = 0
+        self._false_negatives = 0
+        self._false_positives = 0
+        self._true_negatives = 0
+
+    def _update_binary_stats(self, label, pred):
+        """
+        Update various binary classification counts for a single (label, pred)
+        pair.
+
+        Parameters
+        ----------
+        label : `NDArray`
+            The labels of the data.
+
+        pred : `NDArray`
+            Predicted values.
+        """
+        pred = pred.asnumpy()
+        label = label.asnumpy().astype('int32')
+        pred_label = numpy.argmax(pred, axis=1)
+
+        check_label_shapes(label, pred)
+        if len(numpy.unique(label)) > 2:
+            raise ValueError("%s currently only supports binary 
classification."
+                             % self.__class__.__name__)
+
+        for y_pred, y_true in zip(pred_label, label):
 
 Review comment:
   I've used codes like 
   ```python
   tp = nd.sum((pred == 1) * (label == 1)).asscalar()
   fp = nd.sum((pred == 1) * (label == 0)).asscalar()
   fn = nd.sum((pred == 0) * (label == 1)).asscalar()
   precision = float(tp) / (tp + fp)
   recall = float(tp) / (tp + fn)
   f1 = 2 * (precision * recall) / (precision + recall)
   ```
   to calculate the F1 and I find it's much faster in GPU.

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