szha 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_r167986170
 
 

 ##########
 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):
+            if y_pred == 1 and y_true == 1:
+                self._true_positives += 1.
+            elif y_pred == 1 and y_true == 0:
+                self._false_positives += 1.
+            elif y_pred == 0 and y_true == 1:
+                self._false_negatives += 1.
+            else:
+                self._true_negatives += 1.
+
+
+    @property
+    def _precision(self):
+        if self._true_positives + self._false_positives > 0:
+            return self._true_positives / (self._true_positives + 
self._false_positives)
+        else:
+            return 0.
+
+    @property
+    def _recall(self):
+        if self._true_positives + self._false_negatives > 0:
+            return self._true_positives / (self._true_positives + 
self._false_negatives)
+        else:
+            return 0.
+
+    @property
+    def _fscore(self):
+        if self._precision + self._recall > 0:
+            return 2 * self._precision * self._recall / (self._precision + 
self._recall)
+        else:
+            return 0.
+
+    @property
+    def _total_examples(self):
+        return self._false_negatives + self._false_positives + \
+               self._true_negatives + self._true_positives
+
+    def _reset_stats(self):
+        self._false_positives = 0
+        self._false_negatives = 0
+        self._true_positives = 0
+        self._true_negatives = 0
+
 @register
-class F1(EvalMetric):
+class F1(EvalMetric, _BinaryClassificationMixin):
 
 Review comment:
   This is one of the cases where usage of mix-ins is proper for backward 
compatibility and ease of use.

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