Hingeloss

hingeloss

Metric HingeLoss from torchmetrics (torchmetrics.HingeLoss)

When to invoke this skill

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

Reference signature

from torchmetrics import HingeLoss

# HingeLoss(task: Literal['binary', 'multiclass'], num_classes: Optional[int] = None, squared: bool = False, multiclass_mode: Optional[Literal['crammer-singer', 'one-vs-all']] = 'crammer-singer', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric

Library docstring

Compute the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs).

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'`` or ``'multiclass'``. See the documentation of
:class:`~torchmetrics.classification.BinaryHingeLoss` and :class:`~torchmetrics.classification.MulticlassHingeLoss`
for the specific details of each argument influence and examples.

Legacy Example:
    >>> from torch import tensor
    >>> target = tensor([0, 1, 1])
    >>> preds = tensor([0.5, 0.7, 0.1])
    >>> hinge = HingeLoss(task="binary")
    >>> hinge(preds, target)
    tensor(0.9000)

    >>> target = tensor([0, 1, 2])
    >>> preds = tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
    >>> hinge = HingeLoss(task="multiclass", num_classes=3)
    >>> hinge(preds, target)
    tensor(1.5551)

    >>> target = tensor([0, 1, 2])
    >>> preds = tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
    >>> hinge = HingeLoss(task="multiclass", num_classes=3, multiclass_mode="one-vs-all")
    >>> hinge(preds, target)
    tensor([1.3743, 1.1945, 1.2359])

Quick recipe

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