Multiclasshingeloss

---
name: multiclasshingeloss
description: Compute the MulticlassHingeLoss metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute MulticlassHingeLoss, or asks how to score with MulticlassHingeLoss.
metadata:
  skill_kind: metric
  source_lib: torchmetrics
  import_path: torchmetrics.classification.MulticlassHingeLoss
  source: library_introspection
---

# multiclasshingeloss

> Metric `MulticlassHingeLoss` from `torchmetrics` (torchmetrics.classification.MulticlassHingeLoss)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import MulticlassHingeLoss

# MulticlassHingeLoss(num_classes: int, squared: bool = False, multiclass_mode: Literal['crammer-singer', 'one-vs-all'] = 'crammer-singer', ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None
```

## Library docstring

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

The metric can be computed in two ways. Either, the definition by Crammer and Singer is used:

.. math::
    \text{Hinge loss} = \max\left(0, 1 - \hat{y}_y + \max_{i \ne y} (\hat{y}_i)\right)

Where :math:`y \in {0, ..., \mathrm{C}}` is the target class (where :math:`\mathrm{C}` is the number of classes),
and :math:`\hat{y} \in \mathbb{R}^\mathrm{C}` is the predicted output per class. Alternatively, the metric can
also be computed in one-vs-all approach, where each class is valued against all other classes in a binary fashion.

As input to ``forward`` and ``update`` the metric accepts the following input:

- ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor
  containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider
  the input to be logits and will auto apply softmax per sample.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing
  ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index`
  is specified).

.. tip::
   Additional dimension ``...`` will be flattened into the batch dimension.

As output to ``forward`` and ``compute`` the metric returns the following output:

- ``mchl`` (:class:`~torch.Tensor`): A tensor containing the multi-class hinge loss.

Args:
    num_classes: Integer specifying the number of classes
    squared:
        If True, this will compute the squared hinge loss. Otherwise, computes the regular hinge loss.
    multiclass_mode:
        Determines how to compute the metric
    ignore_index:
        Specifies a target value that is ignored and does not contribute to the metric calculation
    validate_args: bool indicating if input arguments and tensors should be validated for correctness.
        Set to ``False`` for faster computations.
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example:
    >>> from torchmetrics.classification import MulticlassHingeLoss
    >>> preds = torch.tenso
```

## Quick recipe

```python
import torchmetrics.classification as _m
score = _m.MulticlassHingeLoss(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)`.