def run(self, data, seed=None):
if (data.is_directed()) and False:
raise Exception("only undirected is supported")
if seed is not None:self.logger.info("seed ignored")
g = convert.to_igraph(data)
timecost, ret = utils.timeit(lambda: g.community_infomap(edge_weights='weight' if data.is_weighted() else None))
vc = ret
clusters = {}
for i, a in enumerate(vc):
clusters[i] = a
modularity = vc.modularity
self.logger.info("Made %d clusters in %f seconds. modularity=%f " % (len(clusters), timecost, modularity))
result = {}
result['timecost'] = timecost
result['modularity'] = modularity
result['runname'] = self.name
result['dataname'] = data.name
result['meta'] = self.get_meta()
result['clusters'] = clusters
save_result(result)
self.result = result
return self
def testClustersProperties(self):
g = self.graph_unweighted_undirect
assert not g.is_weighted()
p = GraphClusterMetrics(g, list(g.get_ground_truth().values())[0])
self.assertEqual(p.num_edges * 2, p.sum_weight)
self.assertEqual(np.sum(list(p.cluster_out_sum_weights.values())) + np.sum(list(p.cluster_sum_intra_weights.values())), p.sum_weight)
print ('num_edges', p.num_edges)
print ('num_vertices', p.num_vertices)
print ('num_clusters', p.num_clusters)
print ('cluster_sizes', p.cluster_sizes)
print ('cluster_indexes', p.cluster_indexes)
print ('cluster_edge_sizes', p.cluster_edge_sizes)
print ('cluster_sum_intra_weights', p.cluster_sum_intra_weights)
print ('unweighted_intra_cluster_densities', p.unweighted_intra_cluster_densities)
print ('cluster_sum_weighted_degrees', p.cluster_sum_weighted_degrees)
print ('intra_cluster_densities', p.intra_cluster_densities)
print ('intra_cluster_density', p.intra_cluster_density)
print ('inter_cluster_density', p.inter_cluster_density)
print ('relative_cluster_densities', p.relative_cluster_densities)
print ("modularity2", p.modularity2)
print ("snap_modularities", p.snap_modularities())
print ("cluster_expansions", p.cluster_expansions)
print ("cluster_cut_ratios", p.cluster_cut_ratios)
print ("conductance", p.conductance)
print ("normalized_cut", p.normalized_cut)
print ("cluster_max_out_degree_fraction", p.cluster_max_out_degree_fraction)
print ("cluster_avg_out_degree_fraction", p.cluster_avg_out_degree_fraction)
print ("cluster_flake_out_degree_fraction", p.cluster_flake_out_degree_fraction)
print ("separability", p.separability)
print ("cluster_clustering_coefficient", p.cluster_clustering_coefficient)
print ("cluster_local_clustering_coefficient", p.cluster_local_clustering_coefficient)
print ("AAA", convert.to_igraph(self.graph_unweighted_undirect).transitivity_local_undirected())
print ("AAA", convert.to_igraph(self.graph_unweighted_undirect).transitivity_undirected())
def to_graph_igraph(self):
from gct.dataset import convert
return convert.to_igraph(self)
def testToIGraph(self):
for data in self.graphs:
g = convert.to_igraph(data)
print (data.is_weighted(), g.is_weighted(), data.is_directed(), g.is_directed())