Multilabelexactmatch

multilabelexactmatch

Metric MultilabelExactMatch from torchmetrics (torchmetrics.classification.MultilabelExactMatch)

When to invoke this skill

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

Reference signature

from torchmetrics.classification import MultilabelExactMatch

# MultilabelExactMatch(num_labels: int, threshold: float = 0.5, multidim_average: Literal['global', 'samplewise'] = 'global', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None

Library docstring

Compute Exact match (also known as subset accuracy) for multilabel tasks.

Exact Match is a stricter version of accuracy where all labels have to match exactly for the sample to be
correctly classified.

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

- ``preds`` (:class:`~torch.Tensor`): An int tensor or float tensor of shape ``(N, C, ..)``. If preds is a
  floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply
  sigmoid per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``.

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

- ``mlem`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument:

    - If ``multidim_average`` is set to ``global`` the output will be a scalar tensor
    - If ``multidim_average`` is set to ``samplewise`` the output will be a tensor of shape ``(N,)``

If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present,
which the reduction will then be applied over instead of the sample dimension ``N``.

Args:
    num_labels: Integer specifying the number of labels
    threshold: Threshold for transforming probability to binary (0,1) predictions
    multidim_average:
        Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

        - ``global``: Additional dimensions are flatted along the batch dimension
        - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
          The statistics in this case are calculated over the additional dimensions.

    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 (preds is int tensor):
    >>> from torch import tensor
    >>> from torchmetrics.classification

Quick recipe

import torchmetrics.classification as _m
score = _m.MultilabelExactMatch(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).