def compute(self) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
"""Compute the receiver operating characteristic.
Returns:
3-element tuple containing
fpr:
tensor with false positive rates.
If multiclass, this is a list of such tensors, one for each class.
tpr:
tensor with true positive rates.
If multiclass, this is a list of such tensors, one for each class.
thresholds:
thresholds used for computing false- and true postive rates
"""
preds = torch.cat(self.preds, dim=0)
target = torch.cat(self.target, dim=0)
if not self.num_classes:
raise ValueError(f"`num_classes` bas to be positive number, but got {self.num_classes}")
return _roc_compute(preds, target, self.num_classes, self.pos_label)
def compute(
self
) -> Union[Tuple[torch.Tensor, torch.Tensor, torch.Tensor], Tuple[
List[torch.Tensor], List[torch.Tensor], List[torch.Tensor]]]:
"""
Compute the receiver operating characteristic
Returns: 3-element tuple containing
fpr:
tensor with false positive rates.
If multiclass, this is a list of such tensors, one for each class.
tpr:
tensor with true positive rates.
If multiclass, this is a list of such tensors, one for each class.
thresholds:
thresholds used for computing false- and true postive rates
"""
preds = torch.cat(self.preds, dim=0)
target = torch.cat(self.target, dim=0)
return _roc_compute(preds, target, self.num_classes, self.pos_label)