Binaryjaccardindex

binaryjaccardindex

Metric BinaryJaccardIndex from torchmetrics (torchmetrics.classification.BinaryJaccardIndex)

When to invoke this skill

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

Reference signature

from torchmetrics.classification import BinaryJaccardIndex

# BinaryJaccardIndex(threshold: float = 0.5, ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any) -> None

Library docstring

Calculate the Jaccard index for binary 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 or float tensor of shape ``(N, ...)``. 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, ...)``.

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

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

- ``bji`` (:class:`~torch.Tensor`): A tensor containing the Binary Jaccard Index.

Args:
    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
    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):
    >>> from torch import tensor
    >>> from torchmetrics.classification import BinaryJaccardIndex
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0, 1, 0, 0])
    >>> metric = BinaryJaccardIndex()
    >>> metric(preds, target)
    tensor(0.5000)

Example (preds is float tensor):
    >>> from torchmetrics.classification import BinaryJaccardIndex
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0.35, 0.85, 0.48, 0.01])
    >>> metric = BinaryJaccardIndex()
    >>> metric(preds, target)
    t

Quick recipe

import torchmetrics.classification as _m
score = _m.BinaryJaccardIndex(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).