Adjustedmutualinfoscore

adjustedmutualinfoscore

Metric AdjustedMutualInfoScore from torchmetrics (torchmetrics.clustering.AdjustedMutualInfoScore)

When to invoke this skill

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

Reference signature

from torchmetrics.clustering import AdjustedMutualInfoScore

# AdjustedMutualInfoScore(average_method: Literal['min', 'geometric', 'arithmetic', 'max'] = 'arithmetic', **kwargs: Any) -> None

Library docstring

Compute `Adjusted Mutual Information Score`_.

.. math::
    AMI(U,V) = \frac{MI(U,V) - E(MI(U,V))}{avg(H(U), H(V)) - E(MI(U,V))}

Where :math:`U` is a tensor of target values, :math:`V` is a tensor of predictions, :math:`M_p(U,V)` is the
generalized mean of order :math:`p` of :math:`U` and :math:`V`, and :math:`MI(U,V)` is the mutual information score
between clusters :math:`U` and :math:`V`. The metric is symmetric, therefore swapping :math:`U` and :math:`V` yields
the same mutual information score.

This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not
be available in practice since clustering in generally is used for unsupervised learning.

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

- ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels
- ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels

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

- ``ami_score`` (:class:`~torch.Tensor`): A tensor with the Adjusted Mutual Information Score

Args:
    average_method: Method used to calculate generalized mean for normalization. Choose between
        ``'min'``, ``'geometric'``, ``'arithmetic'``, ``'max'``.
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example::
    >>> import torch
    >>> from torchmetrics.clustering import AdjustedMutualInfoScore
    >>> preds = torch.tensor([2, 1, 0, 1, 0])
    >>> target = torch.tensor([0, 2, 1, 1, 0])
    >>> ami_score = AdjustedMutualInfoScore(average_method="arithmetic")
    >>> ami_score(preds, target)
    tensor(-0.2500)

Quick recipe

import torchmetrics.clustering as _m
score = _m.AdjustedMutualInfoScore(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).