def analyze_cluster_run_diff(year, graphfile):
with multiprocessing.Pool(4, initializer=init_worker, initargs=(graphfile,)) as p:
clusterings = p.map(_cluster, range(100))
init_worker(graphfile)
scores_nmi = [
evaluation.normalized_mutual_information(
NodeClustering(c1, g, None), NodeClustering(c2, g, None)
).score
for c1, c2 in itertools.combinations(clusterings, 2)
]
scores_rand = [
evaluation.adjusted_rand_index(
NodeClustering(c1, g, None), NodeClustering(c2, g, None)
).score
for c1, c2 in itertools.combinations(clusterings, 2)
]
# scores_pairs = [
# evaluation.adjusted_mutual_information(
# NodeClustering(c1, g, None),
# NodeClustering(c2, g, None)
# ).score
# for c1, c2 in zip(clusterings[::2],clusterings[1::2])
# ]
return {
"NMI": scores_nmi,
"Rand": scores_rand,
}
def test_nmi(self):
g = nx.karate_club_graph()
louvain_communities = louvain(g)
leiden_communities = leiden(g)
score = evaluation.normalized_mutual_information(louvain_communities, leiden_communities)
self.assertLessEqual(score, 1)
self.assertGreaterEqual(score, 0)
def get_scores(idx1, idx2):
c1 = significant_clusterings[idx1]
c2 = significant_clusterings[idx2]
score_nmi = evaluation.normalized_mutual_information(
NodeClustering(c1, None, None), NodeClustering(c2, None, None)
).score
score_rand = evaluation.adjusted_rand_index(
NodeClustering(c1, None, None), NodeClustering(c2, None, None)
).score
return score_nmi, score_rand
def normalized_mutual_information(self, clustering):
"""
Normalized Mutual Information between two clusterings.
Normalized Mutual Information (NMI) is an normalization of the Mutual
Information (MI) score to scale the results between 0 (no mutual
information) and 1 (perfect correlation). In this function, mutual
information is normalized by ``sqrt(H(labels_true) * H(labels_pred))``
:param clustering: NodeClustering object
:return: normalized mutual information score
:Example:
>>> from cdlib.algorithms import louvain
>>> g = nx.karate_club_graph()
>>> communities = louvain(g)
>>> mod = communities.normalized_mutual_information([[1,2], [3,4]])
"""
return evaluation.normalized_mutual_information(self, clustering)