Recallatfixedprecision

recallatfixedprecision

Metric RecallAtFixedPrecision from torchmetrics (torchmetrics.RecallAtFixedPrecision)

When to invoke this skill

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

Reference signature

from torchmetrics import RecallAtFixedPrecision

# RecallAtFixedPrecision(task: Literal['binary', 'multiclass', 'multilabel'], min_precision: 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 recall value given the minimum precision thresholds provided.

This is done by first calculating the precision-recall curve for different thresholds and the find the recall for
a given precision 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.BinaryRecallAtFixedPrecision`,
:class:`~torchmetrics.classification.MulticlassRecallAtFixedPrecision` and
:class:`~torchmetrics.classification.MultilabelRecallAtFixedPrecision` for the specific details of each argument
influence and examples.

Quick recipe

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