Precisionrecallcurve

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

# precisionrecallcurve

> Metric `PrecisionRecallCurve` from `torchmetrics` (torchmetrics.PrecisionRecallCurve)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import PrecisionRecallCurve

# PrecisionRecallCurve(task: Literal['binary', 'multiclass', 'multilabel'], thresholds: Union[int, list[float], torch.Tensor, NoneType] = None, num_classes: Optional[int] = None, num_labels: Optional[int] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric
```

## Library docstring

```
Compute the precision-recall curve.

The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the
tradeoff between the two values can been seen.

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
:class:`~torchmetrics.classification.BinaryPrecisionRecallCurve`,
:class:`~torchmetrics.classification.MulticlassPrecisionRecallCurve` and
:class:`~torchmetrics.classification.MultilabelPrecisionRecallCurve` for the specific details of each argument
influence and examples.

Legacy Example:
    >>> pred = torch.tensor([0, 0.1, 0.8, 0.4])
    >>> target = torch.tensor([0, 1, 1, 0])
    >>> pr_curve = PrecisionRecallCurve(task="binary")
    >>> precision, recall, thresholds = pr_curve(pred, target)
    >>> precision
    tensor([0.5000, 0.6667, 0.5000, 1.0000, 1.0000])
    >>> recall
    tensor([1.0000, 1.0000, 0.5000, 0.5000, 0.0000])
    >>> thresholds
    tensor([0.0000, 0.1000, 0.4000, 0.8000])

    >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05],
    ...                      [0.05, 0.75, 0.05, 0.05, 0.05],
    ...                      [0.05, 0.05, 0.75, 0.05, 0.05],
    ...                      [0.05, 0.05, 0.05, 0.75, 0.05]])
    >>> target = torch.tensor([0, 1, 3, 2])
    >>> pr_curve = PrecisionRecallCurve(task="multiclass", num_classes=5)
    >>> precision, recall, thresholds = pr_curve(pred, target)
    >>> precision
    [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]),
     tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])]
    >>> recall
    [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])]
    >>> thresholds
    [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]),
     tensor(0.0500)]
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.PrecisionRecallCurve(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)`.