Multilabeljaccardindex

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

# multilabeljaccardindex

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

## When to invoke this skill

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

## Reference signature

```python
from torchmetrics.classification import MultilabelJaccardIndex

# MultilabelJaccardIndex(num_labels: int, threshold: float = 0.5, average: Optional[Literal['micro', 'macro', 'weighted', 'none']] = 'macro', ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any) -> None
```

## Library docstring

```
Calculate the Jaccard index for multilabel tasks.

The `Jaccard index`_ (also known as the intersection over union or jaccard similarity coefficient) is an statistic
that can be used to determine the similarity and diversity of a sample set. It is defined as the size of the
intersection divided by the union of the sample sets:

.. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}

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

- ``preds`` (:class:`~torch.Tensor`): A int tensor or float tensor of shape ``(N, C, ...)``. If preds is a
  floating point tensor with values outside [0,1] range we consider the input to be logits and will auto apply
  sigmoid per element. Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``

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

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

- ``mlji`` (:class:`~torch.Tensor`): A tensor containing the Multi-label Jaccard Index loss.

Args:
    num_classes: Integer specifying the number of labels
    threshold: Threshold for transforming probability to binary (0,1) predictions
    ignore_index:
        Specifies a target value that is ignored and does not contribute to the metric calculation
    average:
        Defines the reduction that is applied over labels. Should be one of the following:

        - ``micro``: Sum statistics over all labels
        - ``macro``: Calculate statistics for each label and average them
        - ``weighted``: calculates statistics for each label and computes weighted average using their support
        - ``"none"`` or ``None``: calculates statistic for each label and applies no reduction

    validate_args: bool indicating if input arguments and tensors should be validated for correctness.
        Set to ``False`` for faster computations.
    zero_division:
        Value to replace when there is a division by zero. Should be `0` or `1`.
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example (preds is int tensor):
 
```

## Quick recipe

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