Multilabelmatthewscorrcoef

multilabelmatthewscorrcoef

Metric MultilabelMatthewsCorrCoef from torchmetrics (torchmetrics.classification.MultilabelMatthewsCorrCoef)

When to invoke this skill

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

Reference signature

from torchmetrics.classification import MultilabelMatthewsCorrCoef

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

Library docstring

Calculate `Matthews correlation coefficient`_ for multilabel 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`): An int 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:

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

Args:
    num_labels: 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
    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 MultilabelMatthewsCorrCoef
    >>> target = tensor([[0, 1, 0], [1, 0, 1]])
    >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
    >>> metric = MultilabelMatthewsCorrCoef(num_labels=3)
    >>> metric(preds, target)
    tensor(0.3333)

Example (preds is float tensor):
    >>> from torchmetrics.classification import MultilabelMatthewsCorrCoef
    >>> target = tensor([[0, 1, 0], [1, 0, 1]])
    >>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
    >>> metric = MultilabelMatthewsCorrCoef(num_labels=3)
    >>> metric(preds, target)
    tensor(0.3333)

Quick recipe

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