Multilabelaccuracy

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

# multilabelaccuracy

> Metric `MultilabelAccuracy` from `torchmetrics` (torchmetrics.classification.MultilabelAccuracy)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import MultilabelAccuracy

# MultilabelAccuracy(num_labels: int, threshold: float = 0.5, average: Optional[Literal['micro', 'macro', 'weighted', 'none']] = 'macro', multidim_average: Literal['global', 'samplewise'] = 'global', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None
```

## Library docstring

```
Compute `Accuracy`_ for multilabel tasks.

.. math::
    \text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)

Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.

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

- ``preds`` (:class:`~torch.Tensor`): An int 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:

- ``mla`` (:class:`~torch.Tensor`): A tensor with the accuracy score whose returned shape depends on the
  ``average`` and ``multidim_average`` arguments:

    - If ``multidim_average`` is set to ``global``:

      - If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
      - If ``average=None/'none'``, the shape will be ``(C,)``

    - If ``multidim_average`` is set to ``samplewise``:

      - If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
      - If ``average=None/'none'``, the shape will be ``(N, C)``

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
    average:
        Defines the reduction that is applied over labels. Should be one of the following:

        - ``micro``: Sum statistics over all labels
        - ``macro``: Calculate statistics for each label and average them
        - ``weighted``: calculates statistics for each label and computes weighted average using their support
        - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction

    multidim_average:
        Defines how additionally dimensions ``...`` shou
```

## Quick recipe

```python
import torchmetrics.classification as _m
score = _m.MultilabelAccuracy(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)`.