Precision / Recall for multi-label classification

[RR-28023]

Hi!

Is there a function or a set of arguments that I can use in order to calculate Precision and Recall for a multi-label problem?

Note that with multi-label I mean that each sample can be classified into more than one class.

The following is not returning what I would expect:

import torch
from torchmetrics import Accuracy, Precision, Recall
target = torch.tensor([
    [0, 0, 1, 1, 0],  # Sample 1 belongs to class 2 and 3 (zero-indexed)
    [0, 0, 1, 0, 0], # Sample 2 belongs to class 2 (zero-indexed)
])
preds = torch.tensor([
    [0, 0, 0, 0, 0],  # Sample 1 predicted to belong to no class
    [0, 0, 0, 0, 0], # Sample 2 predicted to belong to no class
])
metric = Precision(num_classes=5, mdmc_average="samplewise")
print(metric(preds, target))

It returns: tensor(0.7000), but it should be 0% since there are no True Positives.

Thanks!