.

cancer.py

@@ -26,14 +26,24 @@ def Cancer(df_cancer, y_cancer, alpha, beta, k, n_iter):
     A = graph['adjacency']
     G = nx.from_numpy_matrix(A)
     pos = nx.spring_layout(G)
+
+    # normalize edge weights to plot edges strength
+    all_weights = []
+    for (node1,node2,data) in G.edges(data=True):
+        all_weights.append(data['weight'])
+    max_weight = max(all_weights)
+    norm_weights = [3* w / max_weight for w in all_weights]
+    norm_weights = norm_weights
+
+
     fig = plt.figure(figsize=(12,12))
     # Color labels
     color_map = []
     color_dict = {'PRAD' : 'blue', 'LUAD' : 'red', 'BRCA' : 'green', 'KIRC' : 'orange', 'COAD' : 'purple'}
     for i in range(y_cancer.shape[0]):
         color_map.append(color_dict[y_cancer['Class'][i]])
     # Plot graph
-    nx.draw(G, node_color=color_map, with_labels=True, pos = pos, font_weight='bold')
+    nx.draw(G, node_color=color_map, width=norm_weights, pos = pos, font_weight='bold')
     plt.title("Learned graph for the cancer dataset k=%s n_iter=%s alpha=%.3f beta=%.3f" % (k , n_iter, alpha, beta))
     filename = os.path.join(plots_dir, 'cancer', 'graph')
     fig.savefig(filename)