torcheval.metrics.functional.binary_precision

torcheval.metrics.functional.binary_precision(input: Tensor, target: Tensor, *, threshold: float = 0.5) → Tensor

Compute precision score for binary classification class, which is calculated as the ratio between the number of true positives (TP) and the total number of predicted positives (TP + FP). Its class version is torcheval.metrics.BinaryPrecision. See also multiclass_precision

Parameters:

• input (Tensor) – Tensor of label predictions It could be the predicted labels, with shape of (n_sample, ). torch.where(input < threshold, 0, 1) will be applied to the input.
• target (Tensor) – Tensor of ground truth labels with shape of (n_sample,).
• threshold (float, default 0.5) – Threshold for converting input into predicted labels for each sample.

Examples:

>>> import torch
>>> from torcheval.metrics.functional import binary_precision
>>> input = torch.tensor([0, 0, 1, 1])
>>> target = torch.tensor([1, 0, 1, 1])
>>> binary_precision(input, target)
tensor(1.)  # 2 / 2

>>> metric = BinaryPrecision(threshold=0.7)
>>> input = torch.tensor([0, 0.8, 0.6, 0.7])
>>> target = torch.tensor([1, 0, 1, 1])
>>> binary_precision(input, target)
tensor(0.5)  # 1 / 2