sklearn.metrics.consensus_score#
- sklearn.metrics.consensus_score(a, b, *, similarity='jaccard')[source]#
The similarity of two sets of biclusters.
Similarity between individual biclusters is computed. Then the best matching between sets is found using the Hungarian algorithm. The final score is the sum of similarities divided by the size of the larger set.
Read more in the User Guide.
- Parameters:
- atuple (rows, columns)
Tuple of row and column indicators for a set of biclusters.
- btuple (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_scorefloat
Consensus score, a non-negative value, sum of similarities divided by size of larger set.
References
Hochreiter, Bodenhofer, et. al., 2010. FABIA: factor analysis for bicluster acquisition.
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
