sethah 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_r167943292
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File path: python/mxnet/metric.py
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@@ -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 agree, but there's another issue for that, so I assumed it would be done
in a separate PR. https://github.com/apache/incubator-mxnet/issues/9586
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