Multilabelprecisionatfixedrecall

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

# multilabelprecisionatfixedrecall

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import MultilabelPrecisionAtFixedRecall

# MultilabelPrecisionAtFixedRecall(num_labels: int, min_recall: float, thresholds: Union[int, list[float], torch.Tensor, NoneType] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None
```

## Library docstring

```
Compute the highest possible precision value given the minimum recall thresholds provided.

This is done by first calculating the precision-recall curve for different thresholds and the find the precision for
a given recall level.

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, ...)``. Target should be a tensor containing
  ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value
  1 always encodes the positive class.

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

As output to ``forward`` and ``compute`` the metric returns a tuple of either 2 tensors or 2 lists containing:

- ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the maximum precision for the
  given recall level per class
- ``threshold`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_classes, )`` with the corresponding threshold
  level per class

.. note::
   The implementation both supports calculating the metric in a non-binned but accurate version and a binned version
   that is less accurate but more memory efficient. Setting the `thresholds` argument to ``None`` will activate the
   non-binned  version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds`
   argument to either an integer, list or a 1d tensor will use a binned version that uses memory of
   size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory).

Args:
    num_labels: Integer specifying the number of labels
    min_recall: float value specifying minimum recall threshold.
    thresholds:
        Can be one of:

        - If set to ``None``, will use a non-binned approach where thresholds are dynamically calculated from
          all the data. Most accurate but also m
```

## Quick recipe

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