Confusionmatrix

confusionmatrix

Metric ConfusionMatrix from torchmetrics (torchmetrics.ConfusionMatrix)

When to invoke this skill

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

Reference signature

from torchmetrics import ConfusionMatrix

# ConfusionMatrix(task: Literal['binary', 'multiclass', 'multilabel'], threshold: float = 0.5, num_classes: Optional[int] = None, num_labels: Optional[int] = None, normalize: Optional[Literal['true', 'pred', 'all', 'none']] = None, ignore_index: Optional[int] = None, validate_args: bool = True, **kwargs: Any) -> torchmetrics.metric.Metric

Library docstring

Compute the `confusion matrix`_.

This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of
:class:`~torchmetrics.classification.BinaryConfusionMatrix`,
:class:`~torchmetrics.classification.MulticlassConfusionMatrix` and
:class:`~torchmetrics.classification.MultilabelConfusionMatrix` for the specific details of each argument influence
and examples.

Legacy Example:
    >>> from torch import tensor
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0, 1, 0, 0])
    >>> confmat = ConfusionMatrix(task="binary", num_classes=2)
    >>> confmat(preds, target)
    tensor([[2, 0],
            [1, 1]])

    >>> target = tensor([2, 1, 0, 0])
    >>> preds = tensor([2, 1, 0, 1])
    >>> confmat = ConfusionMatrix(task="multiclass", num_classes=3)
    >>> confmat(preds, target)
    tensor([[1, 1, 0],
            [0, 1, 0],
            [0, 0, 1]])

    >>> target = tensor([[0, 1, 0], [1, 0, 1]])
    >>> preds = tensor([[0, 0, 1], [1, 0, 1]])
    >>> confmat = ConfusionMatrix(task="multilabel", num_labels=3)
    >>> confmat(preds, target)
    tensor([[[1, 0], [0, 1]],
            [[1, 0], [1, 0]],
            [[0, 1], [0, 1]]])

Quick recipe

import torchmetrics as _m
score = _m.ConfusionMatrix(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).