Consensus Score

consensus-score

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

When to invoke this skill

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

Reference signature

from sklearn.metrics import consensus_score

# consensus_score(a, b, *, similarity='jaccard')

Library docstring

The similarity of two sets of biclusters.

Similarity between individual biclusters is computed. Then the best
matching between sets is found by solving a linear sum assignment problem,
using a modified Jonker-Volgenant algorithm.
The final score is the sum of similarities divided by the size of
the larger set.

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

Parameters
----------
a : tuple (rows, columns)
    Tuple of row and column indicators for a set of biclusters.

b : tuple (rows, columns)
    Another set of biclusters like ``a``.

similarity : 'jaccard' or callable, default='jaccard'
    May be the string "jaccard" to use the Jaccard coefficient, or
    any function that takes four arguments, each of which is a 1d
    indicator vector: (a_rows, a_columns, b_rows, b_columns).

Returns
-------
consensus_score : float
   Consensus score, a non-negative value, sum of similarities
   divided by size of larger set.

See Also
--------
scipy.optimize.linear_sum_assignment : Solve the linear sum assignment problem.

References
----------
* Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis
  for bicluster acquisition
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881408/>`__.

Examples
--------
>>> from sklearn.metrics import consensus_score
>>> a = ([[True, False], [False, True]], [[False, True], [True, False]])
>>> b = ([[False, True], [True, False]], [[True, False], [False, True]])
>>> consensus_score(a, b, similarity='jaccard')
1.0

Quick recipe

import sklearn.metrics as _m
score = _m.consensus_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).