Multilabelexactmatch

---
name: multilabelexactmatch
description: Compute the MultilabelExactMatch metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MultilabelExactMatch, or asks how to score with MultilabelExactMatch.
metadata:
  skill_kind: metric
  source_lib: torchmetrics
  import_path: torchmetrics.classification.MultilabelExactMatch
  source: library_introspection
---

# 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

```python
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

```python
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)`.