torcheval.metrics.functional.multiclass_binned_auroc¶
-
torcheval.metrics.functional.
multiclass_binned_auroc
(input: Tensor, target: Tensor, *, num_classes: int, threshold: Union[int, List[float], Tensor] = 200, average: Optional[str] = 'macro') Tuple[Tensor, Tensor] [source]¶ Compute AUROC, which is the area under the ROC Curve, for multiclass classification. Its class version is
torcheval.metrics.MulticlassAUROC
. See alsobinary_binned_auroc
Parameters: - input (Tensor) – Tensor of label predictions It should be probabilities or logits with shape of (n_sample, n_class).
- target (Tensor) – Tensor of ground truth labels with shape of (n_samples, ).
- num_classes (int) – Number of classes.
- threshold – A integer representing number of bins, a list of thresholds, or a tensor of thresholds.
- average (str, optional) –
'macro'
[default]:- Calculate metrics for each class separately, and return their unweighted mean.
None
:- Calculate the metric for each class separately, and return the metric for every class.
- Examples::
>>> import torch >>> from torcheval.metrics.functional import multiclass_binned_auroc >>> input = torch.tensor([[0.1, 0.2, 0.1], [0.4, 0.2, 0.1], [0.6, 0.1, 0.2], [0.4, 0.2, 0.3], [0.6, 0.2, 0.4]]) >>> target = torch.tensor([0, 1, 2, 1, 0]) >>> multiclass_binned_auroc(input, target, num_classes=3, threshold=5) tensor(0.4000) >>> multiclass_binned_auroc(input, target, num_classes=3, threshold=5, average=None) tensor([0.5000, 0.2500, 0.2500, 0.0000, 1.0000])