def benchmark_scores(samplesize=1):
"""Generate score data sets."""
v = [
0.1
] #, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7]
f = open("benchmarks-opt1.csv", "w+")
f.write("Spinglass, InfoMap, Leiden, Mu\n")
# Read and evaluate graph optimizer scores for set 1.
for mu in v:
print(mu)
for i in range(samplesize):
filename = "./Graphs/lfr-graph{}-mu-{}.txt".format(i, mu)
g = nx.read_gpickle(filename)
scorespinglass = 0
try: # There is a probability the optimizers fail, indicated by -1
scorespinglass = benchmark_score(g, algorithms.spinglass(g))
except:
scorespinglass = -1
scoreinfomap = 0
try:
scoreinfomap = benchmark_score(g, algorithms.infomap(g))
except:
scoreinfomap = -1
scoreleiden = benchmark_score(g, algorithms.leiden(g))
vals = (scorespinglass, scoreinfomap, scoreleiden, mu)
f.write("%f, %f, %f, %f\n" % vals)
print("%f, %f, %f, %f" % vals)
f.close()
f = open("benchmarks-opt2.csv", "w+")
f.write("Spinglass, InfoMap, Leiden, Mu\n")
# Read and evaluate graph optimizer scores for set 2.
for mu in v:
print(mu)
for i in range(samplesize):
filename = "./Graphs/lfr2-graph{}-mu-{}.txt".format(i, mu)
g = nx.read_gpickle(filename)
scorespinglass = 0
try:
scorespinglass = benchmark_score(g, algorithms.spinglass(g))
except:
scorespinglass = -1
scoreinfomap = 0
try:
scoreinfomap = benchmark_score(g, algorithms.infomap(g))
except:
scoreinfomap = -1
scoreleiden = benchmark_score(g, algorithms.leiden(g))
vals = (scorespinglass, scoreinfomap, scoreleiden, mu)
f.write("%f, %f, %f, %f\n" % vals)
print("%f, %f, %f, %f" % vals)
f.close()
def test_spinglass(self):
g = get_string_graph()
com = algorithms.spinglass(g)
self.assertEqual(type(com.communities), list)
if len(com.communities) > 0:
self.assertEqual(type(com.communities[0]), list)
self.assertEqual(type(com.communities[0][0]), str)
def spinglass(self):
template = pd.read_csv("data/communities_template.node", " ", header='infer')
saveTo = "Results/Communities/" + self.name + "_spinglass_communities.node"
G = nx.Graph(self.g)
result = algorithms.spinglass(G)
modularity = result.newman_girvan_modularity().score
significance = result.significance().score
communities = result.to_node_community_map()
n_communities = list(communities.values())[-1][0] + 1
print("\nSpinglass Algorithm: ")
print("#Communities: ", n_communities)
print("Modularity: ", modularity)
print("Significance: ", significance)
c = self.get_ordered_communities(communities)
template['Spinglass'] = c
print("\n")
template['Degree'] = [v for v in self.centrality]
template['RegionName'] = self.region_names
pd.DataFrame.to_csv(template, saveTo, " ", header=False, index=False)
return c
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'):
communities = algorithms.lais2(g, **clust_kwargs)
elif (options.method == 'big_clam'):
communities = algorithms.big_clam(g, **clust_kwargs)
elif (options.method == 'danmf'):
communities = algorithms.danmf(g, **clust_kwargs)
elif (options.method == 'ego_networks'):
# Si applicano gli algoritmi di community discovery e si salvano i risultati su file.
#
# ATTENZIONE: richiede qualche minuto
#
# 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
