torcheval.metrics.functional.binary_auprc¶
- torcheval.metrics.functional.binary_auprc(input: Tensor, target: Tensor, *, num_tasks: int = 1) Tensor ¶
Compute AUPRC, also called Average Precision, which is the area under the Precision-Recall Curve, for binary classification. Its class version is
torcheval.metrics.BinaryAUPRC
.Precision is defined as \(\frac{T_p}{T_p+F_p}\), it is the probability that a positive prediction from the model is a true positive. Recall is defined as \(\frac{T_p}{T_p+F_n}\), it is the probability that a true positive is predicted to be positive by the model.
The precision-recall curve plots the recall on the x axis and the precision on the y axis, both of which are bounded between 0 and 1. This function returns the area under that graph. If the area is near one, the model supports a threshold which correctly identifies a high percentage of true positives while also rejecting enough false examples so that most of the true predictions are true positives.
- Parameters:
input (Tensor) – Tensor of label predictions It should be predicted label, probabilities or logits with shape of (num_tasks, n_sample) or (n_sample, ).
target (Tensor) – Tensor of ground truth labels with shape of (num_tasks, n_sample) or (n_sample, ).
num_tasks (int) – Number of tasks that need BinaryAUPRC calculation. Default value is 1. Binary AUPRC for each task will be calculated independently. Results are equivelent to calling binary_auprc for each row.
Examples:
>>> import torch >>> from torcheval.metrics.functional import binary_auprc >>> input = torch.tensor([0.1, 0.5, 0.7, 0.8]) >>> target = torch.tensor([1, 0, 1, 1]) >>> binary_auprc(input, target) tensor(0.9167) # scalar returned with 1D input tensors >>> input = torch.tensor([[1, 1, 1, 0]]) >>> target = torch.tensor([[1, 0, 1, 0]]) >>> binary_auprc(input, target) tensor([0.6667]) # 1D tensor returned with 2D input tensors >>> input = torch.tensor([[0.1, 0.5, 0.7, 0.8], >>> [1, 1, 1, 0]]) >>> target = torch.tensor([[1, 0, 1, 1], >>> [1, 0, 1, 0]]) >>> binary_auprc(input, target, num_tasks=2) tensor([0.9167, 0.6667])