Sensitivityatspecificity

sensitivityatspecificity

Metric SensitivityAtSpecificity from torchmetrics (torchmetrics.SensitivityAtSpecificity)

When to invoke this skill

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

Reference signature

from torchmetrics import SensitivityAtSpecificity

# SensitivityAtSpecificity(task: Literal['binary', 'multiclass', 'multilabel'], min_specificity: float, 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 highest possible sensitivity value given the minimum specificity thresholds provided.

This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and the
find the sensitivity for a given specificity level.

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.BinarySensitivityAtSpecificity`,
:class:`~torchmetrics.classification.MulticlassSensitivityAtSpecificity` and
:class:`~torchmetrics.classification.MultilabelSensitivityAtSpecificity` for the specific details of each argument
influence and examples.

Quick recipe

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