torcheval.metrics.functional.multiclass_auroc

torcheval.metrics.functional.multiclass_auroc(input: Tensor, target: Tensor, *, num_classes: int, average: Optional[str] = 'macro') → Tensor

Compute AUROC, which is the area under the ROC Curve, for multiclass classification. Its class version is torcheval.metrics.MulticlassAUROC. See also binary_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.
• 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_auroc
>>> input = torch.tensor([[0.1, 0.1, 0.1, 0.1], [0.5, 0.5, 0.5, 0.5], [0.7, 0.7, 0.7, 0.7], [0.8, 0.8, 0.8, 0.8]])
>>> target = torch.tensor([0, 1, 2, 3])
>>> multiclass_auroc(input, target, num_classes=4)
0.5
>>> multiclass_auroc(input, target, num_classes=4, average=None)
tensor([0.0000, 0.3333, 0.6667, 1.0000])