Hammingdistance

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

# hammingdistance

> Metric `HammingDistance` from `torchmetrics` (torchmetrics.HammingDistance)

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics import HammingDistance

# HammingDistance(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, average: Optional[Literal['micro', 'macro', 'weighted', 'none']] = 'micro', multidim_average: Optional[Literal['global', 'samplewise']] = 'global', top_k: Optional[int] = 1, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric
```

## Library docstring

```
Compute the average `Hamming distance`_ (also known as Hamming loss).

.. math::
    \text{Hamming distance} = \frac{1}{N \cdot L} \sum_i^N \sum_l^L 1(y_{il} \neq \hat{y}_{il})

Where :math:`y` is a tensor of target values, :math:`\hat{y}` is a tensor of predictions,
and :math:`\bullet_{il}` refers to the :math:`l`-th label of the :math:`i`-th sample of that
tensor.

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.BinaryHammingDistance`,
:class:`~torchmetrics.classification.MulticlassHammingDistance` and
:class:`~torchmetrics.classification.MultilabelHammingDistance` for the specific details of each argument influence
and examples.

Legacy Example:
    >>> from torch import tensor
    >>> target = tensor([[0, 1], [1, 1]])
    >>> preds = tensor([[0, 1], [0, 1]])
    >>> hamming_distance = HammingDistance(task="multilabel", num_labels=2)
    >>> hamming_distance(preds, target)
    tensor(0.2500)
```

## Quick recipe

```python
import torchmetrics as _m
score = _m.HammingDistance(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)`.