Multiclassmatthewscorrcoef

multiclassmatthewscorrcoef

Metric MulticlassMatthewsCorrCoef from torchmetrics (torchmetrics.classification.MulticlassMatthewsCorrCoef)

When to invoke this skill

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

Reference signature

from torchmetrics.classification import MulticlassMatthewsCorrCoef

# MulticlassMatthewsCorrCoef(num_classes: int, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> None

Library docstring

Calculate `Matthews correlation coefficient`_ for multiclass tasks.

This metric measures the general correlation or quality of a classification.

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

- ``preds`` (:class:`~torch.Tensor`): A int tensor of shape ``(N, ...)`` or float tensor of shape ``(N, C, ..)``.
  If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically convert
  probabilities/logits into an int tensor.
- ``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:

- ``mcmcc`` (:class:`~torch.Tensor`): A tensor containing the Multi-class Matthews Correlation Coefficient.

Args:
    num_classes: Integer specifying the number of classes
    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 (pred is integer tensor):
    >>> from torch import tensor
    >>> from torchmetrics.classification import MulticlassMatthewsCorrCoef
    >>> target = tensor([2, 1, 0, 0])
    >>> preds = tensor([2, 1, 0, 1])
    >>> metric = MulticlassMatthewsCorrCoef(num_classes=3)
    >>> metric(preds, target)
    tensor(0.7000)

Example (pred is float tensor):
    >>> from torchmetrics.classification import MulticlassMatthewsCorrCoef
    >>> target = tensor([2, 1, 0, 0])
    >>> preds = tensor([[0.16, 0.26, 0.58],
    ...                 [0.22, 0.61, 0.17],
    ...                 [0.71, 0.09, 0.20],
    ...                 [0.05, 0.82, 0.13]])
    >>> metric = MulticlassMatthewsCorrCoef(num_classes=3)
    >>> metric(preds, target)
    tensor(0.7000)

Quick recipe

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