Specificity

specificity

Metric Specificity from torchmetrics (torchmetrics.Specificity)

When to invoke this skill

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

Reference signature

from torchmetrics import Specificity

# Specificity(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, average: Optional[Literal['micro', 'macro', 'weighted', 'none']] = 'micro', multidim_average: Optional[Literal['global', 'samplewise']] = 'global', top_k: Optional[int] = 1, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric

Library docstring

Compute `Specificity`_.

.. 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 for any class/label, the metric for that class/label will be set to 0 and the overall metric may
therefore be affected in turn.

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.BinarySpecificity`, :class:`~torchmetrics.classification.MulticlassSpecificity`
and :class:`~torchmetrics.classification.MultilabelSpecificity` for the specific details of each argument influence
and examples.

Legacy Example:
    >>> from torch import tensor
    >>> preds  = tensor([2, 0, 2, 1])
    >>> target = tensor([1, 1, 2, 0])
    >>> specificity = Specificity(task="multiclass", average='macro', num_classes=3)
    >>> specificity(preds, target)
    tensor(0.6111)
    >>> specificity = Specificity(task="multiclass", average='micro', num_classes=3)
    >>> specificity(preds, target)
    tensor(0.6250)

Quick recipe

import torchmetrics as _m
score = _m.Specificity(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).