Retrievalprecisionrecallcurve

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

# retrievalprecisionrecallcurve

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import RetrievalPrecisionRecallCurve

# _RetrievalPrecisionRecallCurve(max_k: Optional[int] = None, adaptive_k: bool = False, empty_target_action: str = 'neg', ignore_index: Optional[int] = None, **kwargs: Any) -> None
```

## Library docstring

```
Wrapper for deprecated import.

>>> from torch import tensor
>>> indexes = tensor([0, 0, 0, 0, 1, 1, 1])
>>> preds = tensor([0.4, 0.01, 0.5, 0.6, 0.2, 0.3, 0.5])
>>> target = tensor([True, False, False, True, True, False, True])
>>> r = _RetrievalPrecisionRecallCurve(max_k=4)
>>> precisions, recalls, top_k = r(preds, target, indexes=indexes)
>>> precisions
tensor([1.0000, 0.5000, 0.6667, 0.5000])
>>> recalls
tensor([0.5000, 0.5000, 1.0000, 1.0000])
>>> top_k
tensor([1, 2, 3, 4])
```

## Quick recipe

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