logauc

Metric LogAUC from torchmetrics (torchmetrics.LogAUC)

When to invoke this skill

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

Reference signature

from torchmetrics import LogAUC

# LogAUC(task: Literal['binary', 'multiclass', 'multilabel'], thresholds: Union[int, List[float], torch.Tensor, NoneType] = None, fpr_range: Optional[Tuple[float, float]] = (0.001, 0.1), 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 `Log AUC`_ score for multiclass classification tasks.

The score is computed by first computing the ROC curve, which then is interpolated to the specified range of false
positive rates (FPR) and then the log is taken of the FPR before the area under the curve (AUC) is computed. The
score is commonly used in applications where the positive and negative are imbalanced and a low false positive rate
is of high importance.

This module 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.BinaryLogAUC`, :class:`~torchmetrics.classification.MulticlassLogAUC` and
:class:`~torchmetrics.classification.MultilabelLogAUC` for the specific details of each argument influence and
examples.

Quick recipe

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