acphile commented on a change in pull request #18083: Changes to mxnet.metric
URL: https://github.com/apache/incubator-mxnet/pull/18083#discussion_r410213003
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File path: python/mxnet/gluon/metric.py
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@@ -777,16 +815,113 @@ class F1(EvalMetric):
--------
>>> predicts = [mx.nd.array([[0.3, 0.7], [0., 1.], [0.4, 0.6]])]
>>> labels = [mx.nd.array([0., 1., 1.])]
- >>> f1 = mx.metric.F1()
+ >>> f1 = mx.gluon.metric.F1()
>>> f1.update(preds = predicts, labels = labels)
>>> print f1.get()
('f1', 0.8)
"""
def __init__(self, name='f1',
- output_names=None, label_names=None, average="macro"):
+ output_names=None, label_names=None, threshold=0.5,
average="macro"):
+ self.average = average
+ self.metrics = _BinaryClassificationMetrics(threshold=threshold)
+ EvalMetric.__init__(self, name=name,
+ output_names=output_names, label_names=label_names,
+ has_global_stats=True)
+
+ def update(self, labels, preds):
+ """Updates the internal evaluation result.
+
+ Parameters
+ ----------
+ labels : list of `NDArray`
+ The labels of the data.
+
+ preds : list of `NDArray`
+ Predicted values.
+ """
+ labels, preds = check_label_shapes(labels, preds, True)
+
+ for label, pred in zip(labels, preds):
+ self.metrics.update_binary_stats(label, pred)
+
+ if self.average == "macro":
Review comment:
Averaging F1 per batch here previously existed in metric.py before I made
changes. This calculation also exists in MAE, MSE, RMSE, and
PearsonCorrelation. Should I remove all of them accordingly? For the average
"macro" in sklearn, it seems used in calculating F1 score for
multiclass/multilabel targets. But currently our F1 only supports binary
classification. I think I need to make extensions for F1.
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