torcheval.metrics.functional.binary_confusion_matrix

torcheval.metrics.functional.binary_confusion_matrix(input: Tensor, target: Tensor, *, threshold: float = 0.5, normalize: str | None = None) → Tensor

Compute binary confusion matrix, a 2 by 2 tensor with counts ( (true positive, false negative) , (false positive, true negative) )

Parameters:

• input (Tensor) – Tensor of label predictions 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. torch.where(input < threshold, 0, 1) will be applied to the input.

• normalize –

  • None [default]:

    Give raw counts (‘none’ also defaults to this)

  • 'pred':

    Normalize across the prediction, i.e. such that the rows add to one.

  • 'true':

    Normalize across the condition positive, i.e. such that the columns add to one.

  • 'all'”

    Normalize across all examples, i.e. such that all matrix entries add to one.

Examples:

>>> import torch
>>> from torcheval.metrics.functional import binary_confusion_matrix
>>> input = torch.tensor([0, 1, 0.7, 0.6])
>>> target = torch.tensor([0, 1, 1, 0])
>>> binary_confusion_matrix(input, target)
tensor([[1, 1],
        [0, 2]])

>>> input = torch.tensor([1, 1, 0, 0])
>>> target = torch.tensor([0, 1, 1, 1])
>>> binary_confusion_matrix(input, target, threshold=1)
tensor([[0, 1],
        [2, 1]])

>>> input = torch.tensor([1, 1, 0, 0])
>>> target = torch.tensor([0, 1, 1, 1])
>>> binary_confusion_matrix(input, target, normalize="true")
tensor([[0.0000, 1.0000],
        [0.6667, 0.3333]])