def test_plot_sim_matrix(self):
g = nx.karate_club_graph()
coms = algorithms.louvain(g)
coms2 = algorithms.label_propagation(g)
viz.plot_sim_matrix([coms, coms2],
evaluation.adjusted_mutual_information)
plt.savefig("cluster.pdf")
os.remove("cluster.pdf")
# plot the network clusters
viz.plot_network_clusters(nx_g, pred_coms, pos, figsize=(5, 5))
plt.title(f'{name} algo of {graph_name}, AMI = {round(ami_score, 3)}')
plt.show()
# plot the graph
viz.plot_community_graph(nx_g, pred_coms, figsize=(5, 5))
plt.title(f'Communities for {name} algo of {graph_name}.')
plt.show()
#%% analysis plots
coms = [ground_truth_com]
for name, results in results_dict.items():
coms.append(results['pred_coms'])
#%%
viz.plot_sim_matrix(coms,evaluation.adjusted_mutual_information)
plt.show()
viz.plot_com_properties_relation(coms, evaluation.size, evaluation.internal_edge_density)
plt.title('Internal Edge Density vs. Size')
plt.show()
viz.plot_com_properties_relation(coms, evaluation.size, evaluation.average_internal_degree)
plt.title('Internal Average Degree vs. Size')
plt.show()
viz.plot_com_stat(coms, evaluation.internal_edge_density)
plt.show()
#%%
df_nodes.to_csv('ais/nodes_from_cd.csv', index=False)
def draw_cluster_heatmap(list_of_communities):
clustermap = viz.plot_sim_matrix(list_of_communities,
evaluation.adjusted_mutual_information)
plt.savefig("communities/clustermap.png")