Binaryaverageprecision

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

# binaryaverageprecision

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import BinaryAveragePrecision

# BinaryAveragePrecision(thresholds: Union[int, list[float], torch.Tensor, NoneType] = None, ignore_index: Optional[int] = None, validate_args: bool = True, normalization: Optional[Literal['sigmoid', 'softmax']] = 'sigmoid', **kwargs: Any) -> None
```

## Library docstring

```
Compute the average precision (AP) score for binary tasks.

The AP score summarizes a precision-recall curve as an weighted mean of precisions at each threshold, with the
difference in recall from the previous threshold as weight:

.. math::
    AP = \sum_{n} (R_n - R_{n-1}) P_n

where :math:`P_n, R_n` is the respective precision and recall at threshold index :math:`n`. This value is
equivalent to the area under the precision-recall curve (AUPRC).

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

- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)`` 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, ...)`` 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.

As output to ``forward`` and ``compute`` the metric returns the following output:

- ``bap`` (:class:`~torch.Tensor`): A single scalar with the average precision score

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

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})` (constant memory).

Args:
    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 most memory consuming approach.
        - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from
          0 to 1 as bins for the calculation.
        - If set to an `list` of floats, will use the ind
```

## Quick recipe

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