leezu commented on a change in pull request #18083:
URL: https://github.com/apache/incubator-mxnet/pull/18083#discussion_r412625767
##########
File path: python/mxnet/gluon/metric.py
##########
@@ -619,90 +583,100 @@ def update_binary_stats(self, label, pred):
"""
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__)
+ if self.class_type == "binary":
+ self._set(1)
+ if len(numpy.unique(label)) > 2:
+ raise ValueError("Wrong label for binary classification.")
+ if pred.shape == label.shape:
+ pass
+ elif pred.shape[-1] > 2:
+ raise ValueError("The shape of prediction {} is wrong for
binary classification.".format(pred.shape))
+ elif pred.shape[-1] == 2:
+ pred = pred.reshape(-1, 2)[:, 1]
+ pred_label = predict_with_threshold(pred, self.threshold).flat
+ label = label.flat
+
+ elif self.class_type == "multiclass":
+ num = pred.shape[-1]
+ self._set(num)
+ assert label.max() < num, "pred contains fewer classes than label!"
+ pred_label = one_hot(pred.argmax(axis=-1).reshape(-1), num)
+ label = one_hot(label.reshape(-1), num)
+
+ elif self.class_type == "multilabel":
+ num = pred.shape[-1]
+ self._set(num)
+ assert pred.shape == label.shape, "The shape of label should be
same as that of prediction for multilabel classification."
+ pred_label = predict_with_threshold(pred,
self.threshold).reshape(-1, num)
+ label = label.reshape(-1, num)
+ else:
+ raise ValueError("Wrong class_type {}! Only supports ['binary',
'multiclass', 'multilabel']".format(self.class_type))
+
+ check_label_shapes(label, pred_label)
+
pred_true = (pred_label == 1)
pred_false = 1 - pred_true
label_true = (label == 1)
label_false = 1 - label_true
- true_pos = (pred_true * label_true).sum()
- false_pos = (pred_true * label_false).sum()
- false_neg = (pred_false * label_true).sum()
- true_neg = (pred_false * label_false).sum()
+ true_pos = (pred_true * label_true).sum(0)
+ false_pos = (pred_true * label_false).sum(0)
+ false_neg = (pred_false * label_true).sum(0)
+ true_neg = (pred_false * label_false).sum(0)
self.true_positives += true_pos
- self.global_true_positives += true_pos
self.false_positives += false_pos
- self.global_false_positives += false_pos
self.false_negatives += false_neg
- self.global_false_negatives += false_neg
self.true_negatives += true_neg
- self.global_true_negatives += true_neg
@property
def precision(self):
- if self.true_positives + self.false_positives > 0:
- return float(self.true_positives) / (self.true_positives +
self.false_positives)
+ if self.num_classes is not None:
+ return self.true_positives / numpy.maximum(self.true_positives +
self.false_positives, 1e-12)
else:
return 0.
@property
def global_precision(self):
- if self.global_true_positives + self.global_false_positives > 0:
- return float(self.global_true_positives) /
(self.global_true_positives + self.global_false_positives)
+ if self.num_classes is not None:
+ return self.true_positives.sum() /
numpy.maximum(self.true_positives.sum() + self.false_positives.sum(), 1e-12)
else:
return 0.
-
+
@property
def recall(self):
- if self.true_positives + self.false_negatives > 0:
- return float(self.true_positives) / (self.true_positives +
self.false_negatives)
+ if self.num_classes is not None:
+ return self.true_positives / numpy.maximum(self.true_positives +
self.false_negatives, 1e-12)
else:
return 0.
@property
def global_recall(self):
- if self.global_true_positives + self.global_false_negatives > 0:
- return float(self.global_true_positives) /
(self.global_true_positives + self.global_false_negatives)
+ if self.num_classes is not None:
+ return self.true_positives.sum() /
numpy.maximum(self.true_positives.sum() + self.false_negatives.sum(), 1e-12)
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.
+ return (1 + self.beta ** 2) * self.precision * self.recall /
numpy.maximum(self.beta ** 2 * self.precision + self.recall, 1e-12)
@property
def global_fscore(self):
Review comment:
Would it make sense to adjust the name?
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