Homogeneityscore

homogeneityscore

Metric HomogeneityScore from torchmetrics (torchmetrics.clustering.HomogeneityScore)

When to invoke this skill

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

Reference signature

from torchmetrics.clustering import HomogeneityScore

# HomogeneityScore(**kwargs: Any) -> None

Library docstring

Compute `Homogeneity Score`_.

The homogeneity score is a metric to measure the homogeneity of a clustering. A clustering result satisfies
homogeneity if all of its clusters contain only data points which are members of a single class. The metric is not
symmetric, therefore swapping ``preds`` and ``target`` yields a different 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:

- ``rand_score`` (:class:`~torch.Tensor`): A tensor with the Rand Score

Args:
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example:
    >>> import torch
    >>> from torchmetrics.clustering import HomogeneityScore
    >>> preds = torch.tensor([2, 1, 0, 1, 0])
    >>> target = torch.tensor([0, 2, 1, 1, 0])
    >>> metric = HomogeneityScore()
    >>> metric(preds, target)
    tensor(0.4744)

Quick recipe

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