if 'knode' in data['tools']:
initial_head = initial_head + ", th_knode, size_result_knode, f1_score_knode, recall_knode, runtime_knode"
if 'aminsga2' in data['tools']:
initial_head = initial_head + ", th_aminsga2, size_result_aminsga2, f1_score_aminsga2, recall_aminsga2, runtime_aminsga2"
initial_head = initial_head + ", th_baseline, f1_score_baseline, recall_baseline, runtime_baseline \n"
outfile.write(initial_head)
for ctr in range(arg.number_of_runs):
if arg.graph_generation == "guyondata":
G = Datasets.get_guyon_graph(ctr + 1)
else:
G = Datasets.get_scale_free_graph_edge(arg.network_size,
initial_module, nb_modules,
arg.module_size, arg.prob_p,
arg.prob_q,
arg.removed_edges, rng)
rate_conection = len(G.edges) / len(G.nodes)
average_shortest_paths = []
for _, cluster in Datasets.get_groups(G).items():
nodes = list(cluster)
average_shortest_paths.append(
Scores.average_shortest_path(G, nodes))
result = str(ctr) + "," + str(arg.network_size) + "," + str(
arg.module_size) + "," + str(rate_conection) + "," + str(
average_shortest_paths[0])
# save the network and the simulation transcripts in the file
network_path, weight_path = write_network_edge_list(
