Multilabelcoverageerror

multilabelcoverageerror

Metric MultilabelCoverageError from torchmetrics (torchmetrics.classification.MultilabelCoverageError)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with MultilabelCoverageError, or mentions torchmetrics.classification.MultilabelCoverageError directly, or wants the standard torchmetrics implementation.

Reference signature

from torchmetrics.classification import MultilabelCoverageError

# MultilabelCoverageError(num_labels: int, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None

Library docstring

Compute `Multilabel coverage error`_.

The score measure how far we need to go through the ranked scores to cover all true labels. The best value is equal
to the average number of labels in the target tensor per sample.

As input to ``forward`` and ``update`` the metric accepts the following input:

- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor
  containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider
  the input to be logits and will auto apply sigmoid per element.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor
  containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified).

.. tip::
   Additional dimension ``...`` will be flattened into the batch dimension.

As output to ``forward`` and ``compute`` the metric returns the following output:

- ``mlce`` (:class:`~torch.Tensor`): A tensor containing the multilabel coverage error.

Args:
    num_labels: Integer specifying the number of labels
    ignore_index:
        Specifies a target value that is ignored and does not contribute to the metric calculation
    validate_args: bool indicating if input arguments and tensors should be validated for correctness.
        Set to ``False`` for faster computations.

Example:
    >>> from torch import rand, randint
    >>> from torchmetrics.classification import MultilabelCoverageError
    >>> preds = rand(10, 5)
    >>> target = randint(2, (10, 5))
    >>> mlce = MultilabelCoverageError(num_labels=5)
    >>> mlce(preds, target)
    tensor(3.9000)

Quick recipe

import torchmetrics.classification as _m
score = _m.MultilabelCoverageError(y_true, y_pred)

Don'ts

• Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
• Always check the library version's argument order — sklearn is (y_true, y_pred) while torchmetrics is (preds, target).