torcheval.metrics.functional.multilabel_precision_recall_curve

torcheval.metrics.functional.multilabel_precision_recall_curve(input: Tensor, target: Tensor, *, num_labels: Optional[int] = None) → Tuple[List[Tensor], List[Tensor], List[Tensor]]

Returns precision-recall pairs and their corresponding thresholds for multi-label classification tasks. If there are no samples for a label in the target tensor, its recall values are set to 1.0.

Its class version is torcheval.metrics.MultilabelPrecisionRecallCurve. See also binary_precision_recall_curve, multiclass_precision_recall_curve

Parameters:

• input (Tensor) – Tensor of label predictions It should be probabilities or logits with shape of (n_sample, n_label).
• target (Tensor) – Tensor of ground truth labels with shape of (n_samples, n_label).
• num_labels (Optional) – Number of labels.

Returns:

List[torch.Tensor], recall: List[torch.Tensor], thresholds: List[torch.Tensor])

precision: List of precision result. Each index indicates the result of a label. recall: List of recall result. Each index indicates the result of a label. thresholds: List of threshold. Each index indicates the result of a label.

Return type:

a tuple of (precision

Examples:

>>> import torch
>>> from torcheval.metrics.functional import multilabel_precision_recall_curve
>>> input = torch.tensor([[0.75, 0.05, 0.35], [0.45, 0.75, 0.05], [0.05, 0.55, 0.75], [0.05, 0.65, 0.05]])
>>> target = torch.tensor([[1, 0, 1], [0, 0, 0], [0, 1, 1], [1, 1, 1]])
>>> multilabel_precision_recall_curve(input, target, num_labels=3)
([tensor([0.5, 0.5, 1.0, 1.0]),
tensor([0.5, 0.66666667, 0.5, 0.0, 1.0]),
tensor([0.75, 1.0, 1.0, 1.0])],
[tensor([1.0, 0.5, 0.5, 0.0]),
tensor([1.0, 1.0, 0.5, 0.0, 0.0]),
tensor([1.0, 0.66666667, 0.33333333, 0.0])],
[tensor([0.05, 0.45, 0.75]),
tensor([0.05, 0.55, 0.65, 0.75]),
tensor([0.05, 0.35, 0.75])])