Binarycohenkappa

binarycohenkappa

Metric BinaryCohenKappa from torchmetrics (torchmetrics.classification.BinaryCohenKappa)

When to invoke this skill

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

Reference signature

from torchmetrics.classification import BinaryCohenKappa

# BinaryCohenKappa(threshold: float = 0.5, ignore_index: Optional[int] = None, weights: Optional[Literal['linear', 'quadratic', 'none']] = None, validate_args: bool = True, **kwargs: Any) -> None

Library docstring

Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for binary tasks.

.. math::
    \kappa = (p_o - p_e) / (1 - p_e)

where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
the expected agreement when both annotators assign labels randomly. Note that
:math:`p_e` is estimated using a per-annotator empirical prior over the
class labels.

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:

- ``bc_kappa`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score

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
    weights: Weighting type to calculate the score. Choose from:

        - ``None`` or ``'none'``: no weighting
        - ``'linear'``: linear weighting
        - ``'quadratic'``: quadratic weighting

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

Example (preds is float tensor):
    >>> from torchmetrics.classification import BinaryCohenKappa
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = ten

Quick recipe

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