cdlib.evaluation.sample_expected_sim

cdlib.evaluation.sample_expected_sim(first_partition: object, second_partition: object, measure: str = 'jaccard_index', random_model: str = 'perm', n_samples: int = 1, keep_samples: bool = False) → MatchingResult

This function calculates the expected Similarity for all pair-wise comparisons between Clusterings drawn from one of six random models.

Note

Clustering 2 is considered the gold-standard clustering for one-sided expectations

Parameters:

• first_partition – NodeClustering object

• second_partition – NodeClustering object

• measure – The similarity measure to evaluate. Must be one of [ecs, jaccard_index, rand_index, fowlkes_mallows_index, classification_error, czekanowski_index, dice_index, sorensen_index, rogers_tanimoto_index, southwood_index, mi, rmi, vi, geometric_accuracy, overlap_quality, sample_expected_sim]

• random_model –

  The random model to use:

  ’all’uniform distribution over the set of all clusterings of

  n_elements

  ’all1’one-sided selection from the uniform distribution over the set

  of all clusterings of n_elements

  ’num’uniform distribution over the set of all clusterings of

  n_elements in n_clusters

  ’num1’one-sided selection from the uniform distribution over the set

  of all clusterings of n_elements in n_clusters

  ’perm’ : the permutation model for a fixed cluster size sequence

  ’perm1’one-sided selection from the permutation model for a fixed

  cluster size sequence, same as ‘perm’

• n_samples – The number of random Clusterings sampled to determine the expected similarity.

• keep_samples – If True, returns the Similarity samples themselves, otherwise return their mean.

Returns:

MatchingResult object

Example:

>>> from cdlib import evaluation, algorithms
>>> import networkx as nx
>>> g = nx.karate_club_graph()
>>> louvain_communities = algorithms.louvain(g)
>>> leiden_communities = algorithms.leiden(g)
>>> evaluation.sample_expected_sim(louvain_communities,leiden_communities)

Note

The function requires the clusim library to be installed. You can install it via pip: pip install clusim