def test_rberpots(self):
g = get_string_graph()
coms = algorithms.rber_pots(g)
self.assertEqual(type(coms.communities), list)
if len(coms.communities) > 0:
self.assertEqual(type(coms.communities[0]), list)
self.assertEqual(type(coms.communities[0][0]), str)
elif (options.method == 'eigenvector'):
communities = algorithms.eigenvector(g, **clust_kwargs)
elif (options.method == 'gdmp2'):
communities = algorithms.gdmp2(g, **clust_kwargs)
elif (options.method == 'greedy_modularity'):
communities = algorithms.greedy_modularity(g,
weight='weight',
**clust_kwargs)
#elif(options.method == 'infomap'):
# communities = algorithms.infomap(g)
elif (options.method == 'label_propagation'):
communities = algorithms.label_propagation(g, **clust_kwargs)
elif (options.method == 'markov_clustering'):
communities = algorithms.markov_clustering(g, **clust_kwargs)
elif (options.method == 'rber_pots'):
communities = algorithms.rber_pots(g, weights='weight', **clust_kwargs)
elif (options.method == 'rb_pots'):
communities = algorithms.rb_pots(g, weights='weight', **clust_kwargs)
elif (options.method == 'significance_communities'):
communities = algorithms.significance_communities(g, **clust_kwargs)
elif (options.method == 'spinglass'):
communities = algorithms.spinglass(g, **clust_kwargs)
elif (options.method == 'surprise_communities'):
communities = algorithms.surprise_communities(g, **clust_kwargs)
elif (options.method == 'walktrap'):
communities = algorithms.walktrap(g, **clust_kwargs)
#elif(options.method == 'sbm_dl'):
# communities = algorithms.sbm_dl(g)
#elif(options.method == 'sbm_dl_nested'):
# communities = algorithms.sbm_dl_nested(g)
elif (options.method == 'lais2'):
# CONSIGLIO: passare alla cella successiva che carica i risultati da file
# In[30]:
accuracy_spinglass = 0
accuracy_eigenvector = 0
accuracy_leiden = 0
accuracy_cpm = 0
accuracy_rber_pots = 0
for i in range(10):
result_spinglass_tmp = algorithms.spinglass(g1)
result_eigenvector_tmp = algorithms.eigenvector(g1)
result_leiden_tmp = algorithms.leiden(g1)
result_cpm_tmp = algorithms.cpm(g1, resolution_parameter=.00018)
result_rber_pots_tmp = algorithms.rber_pots(g1, resolution_parameter=.32)
#definizione colonne che servono per calcolare l'accuracy
nodes1['community_spinglass'] = -1
for i in range(len(result_spinglass_tmp.communities)):
for j in result_spinglass_tmp.communities[i]:
nodes1.loc[j, 'community_spinglass'] = i
nodes1['community_eigenvector'] = -1
for i in range(len(result_eigenvector_tmp.communities)):
for j in result_eigenvector_tmp.communities[i]:
nodes1.loc[j, 'community_eigenvector'] = i
nodes1['community_leiden'] = -1
for i in range(len(result_leiden_tmp.communities)):
for j in result_leiden_tmp.communities[i]:
nodes1.loc[j, 'community_leiden'] = i
nodes1['community_cpm'] = -1
