torcheval.metrics.functional.binary_recall_at_fixed_precision¶
- torcheval.metrics.functional.binary_recall_at_fixed_precision(input: Tensor, target: Tensor, *, min_precision: float) Tuple[Tensor, Tensor] ¶
Returns the highest possible recall value given the minimum precision for binary classification tasks.
Its class version is
torcheval.metrics.BinaryRecallAtFixedPrecision
.- Parameters:
input (Tensor) – Tensor of label predictions It should be probabilities with shape of (n_samples, )
target (Tensor) – Tensor of ground truth labels with shape of (n_samples, )
min_precision (float) – Minimum precision threshold
- Returns:
recall (Tensor): Max recall value given the minimum precision
thresholds (Tensor): Corresponding threshold to max recall
- Return type:
Tuple
Examples:
>>> import torch >>> from torcheval.metrics.functional import binary_recall_at_fixed_precision >>> input = torch.tensor([0.1, 0.4, 0.6, 0.6, 0.6, 0.35, 0.8]) >>> target = torch.tensor([0, 0, 1, 1, 1, 1, 1]) >>> binary_recall_at_fixed_precision(input, target, min_precision=0.5) (torch.tensor(1.0), torch.tensor(0.35))