Rand Score

rand-score

Metric rand_score from scikit-learn (sklearn.metrics.rand_score)

When to invoke this skill

The user has predictions + ground truth and asks to evaluate with rand_score, or mentions sklearn.metrics.rand_score directly, or wants the standard scikit-learn implementation.

Reference signature

from sklearn.metrics import rand_score

# rand_score(labels_true, labels_pred)

Library docstring

Rand index.

The Rand Index computes a similarity measure between two clusterings
by considering all pairs of samples and counting pairs that are
assigned in the same or different clusters in the predicted and
true clusterings [1]_ [2]_.

The raw RI score [3]_ is:

.. code-block:: text

    RI = (number of agreeing pairs) / (number of pairs)

Read more in the :ref:`User Guide <rand_score>`.

Parameters
----------
labels_true : array-like of shape (n_samples,), dtype=integral
    Ground truth class labels to be used as a reference.

labels_pred : array-like of shape (n_samples,), dtype=integral
    Cluster labels to evaluate.

Returns
-------
RI : float
   Similarity score between 0.0 and 1.0, inclusive, 1.0 stands for
   perfect match.

See Also
--------
adjusted_rand_score: Adjusted Rand Score.
adjusted_mutual_info_score: Adjusted Mutual Information.

References
----------
.. [1] :doi:`Hubert, L., Arabie, P. "Comparing partitions."
   Journal of Classification 2, 193–218 (1985).
   <10.1007/BF01908075>`.

.. [2] `Wikipedia: Simple Matching Coefficient
    <https://en.wikipedia.org/wiki/Simple_matching_coefficient>`_

.. [3] `Wikipedia: Rand Index <https://en.wikipedia.org/wiki/Rand_index>`_

Examples
--------
Perfectly matching labelings have a score of 1 even

  >>> from sklearn.metrics.cluster import rand_score
  >>> rand_score([0, 0, 1, 1], [1, 1, 0, 0])
  1.0

Labelings that assign all classes members to the same clusters
are complete but may not always be pure, hence penalized:

  >>> rand_score([0, 0, 1, 2], [0, 0, 1, 1])
  0.83

Quick recipe

import sklearn.metrics as _m
score = _m.rand_score(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).