torcheval.metrics.functional.multiclass_precision

torcheval.metrics.functional.multiclass_precision(input: Tensor, target: Tensor, *, num_classes: int | None = None, average: str | None = 'micro') → Tensor

Compute precision score, which is the ratio of the true positives (TP) and the total number of points classified as positives (TP + FP). Its class version is torcheval.metrics.MultiClassPrecision.

Parameters:

• input (Tensor) – Tensor of label predictions It could be the predicted labels, with shape of (n_sample, ). It could also be probabilities or logits with shape of (n_sample, n_class). torch.argmax will be used to convert input into predicted labels.

• target (Tensor) – Tensor of ground truth labels with shape of (n_sample, ).

• num_classes – Number of classes.

• average –

  • 'micro' [default]:

    Calculate the metrics globally, by using the total true positives and false positives across all classes.

  • 'macro':

    Calculate metrics for each class separately, and return their unweighted mean. Classes with 0 true instances and predicted instances are ignored.

  • 'weighted':

    Calculate metrics for each class separately, and return their average weighted by the number of instances for each class in the target tensor. Classes with 0 true instances and predicted instances are ignored.

  • None:

    Calculate the metric for each class separately, and return the metric for every class. NaN is returned if a class has no sample in target.

Examples:

>>> import torch
>>> from torcheval.metrics.functional import multiclass_precision
>>> input = torch.tensor([0, 2, 1, 3])
>>> target = torch.tensor([0, 1, 2, 3])
>>> multiclass_precision(input, target)
tensor(0.5)
>>> multiclass_precision(input, target, average=None, num_classes=4)
tensor([1., 0., 0., 1.])
>>> multiclass_precision(input, target, average="macro", num_classes=4)
tensor(0.5)
>>> input = torch.tensor([[0.9, 0.1, 0, 0], [0.1, 0.2, 0.4, 0,3], [0, 1.0, 0, 0], [0, 0, 0.2, 0.8]])
>>> multiclass_precision(input, target)
tensor(0.5)