Binaryspecificity

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

# binaryspecificity

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import BinarySpecificity

# BinarySpecificity(threshold: float = 0.5, multidim_average: Literal['global', 'samplewise'] = 'global', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None
```

## Library docstring

```
Compute `Specificity`_ for binary tasks.

.. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}

Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and false positives
respectively. The metric is only proper defined when :math:`\text{TN} + \text{FP} \neq 0`. If this case is
encountered a score of 0 is returned.

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

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

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

- ``bs`` (:class:`~torch.Tensor`): If ``multidim_average`` is set to ``global``, the metric returns a scalar value.
  If ``multidim_average`` is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value
  per sample.

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:
    threshold: Threshold for transforming probability to binary {0,1} predictions
    multidim_average:
        Defines how additionally dimensions ``...`` should be handled. Should be one of the following:

        - ``global``: Additional dimensions are flatted along the batch dimension
        - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
          The statistics in this case are calculated over the additional dimensions.

    ignore_index:
        Specifies a target value that is ignored and does not contribute to the metric calculation
    validate_args: bool indicating if input arguments and tensors should be validated for correctness.
        Set to ``False`` for faster computations.

Example (preds is int tensor):
    >>> from torch import tensor
   
```

## Quick recipe

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