cdlib.evaluation.f1¶
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f1
(first_partition: object, second_partition: object) → cdlib.evaluation.comparison.MatchingResult¶ Compute the average F1 score of the optimal algorithms matches among the partitions in input. Works on overlapping/non-overlapping complete/partial coverage partitions.
Parameters: - first_partition – NodeClustering object
- second_partition – NodeClustering object
Returns: MatchingResult object
Example: >>> from cdlib import evaluation, algorithms >>> g = nx.karate_club_graph() >>> louvain_communities = algorithms.louvain(g) >>> leiden_communities = algorithms.leiden(g) >>> evaluation.f1(louvain_communities,leiden_communities)
Reference: - Rossetti, G., Pappalardo, L., & Rinzivillo, S. (2016). A novel approach to evaluate algorithms detection internal on ground truth. In Complex Networks VII (pp. 133-144). Springer, Cham.