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_r168324990
##########
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):
+ 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:
Composition is equally flexible with the drawback of extra dereference,
though I'm fine either way.
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