mod update corresponding examples

diff_comparison.py

@@ -1,6 +1,7 @@
 import math
 
 import matplotlib.pyplot as plt
+from matplotlib.colors import TwoSlopeNorm
 import numpy as np
 from graph_tool.all import *
 
@@ -87,7 +88,7 @@ def plot_graph_diff(G, c, fig, ax, name, cmap=plt.cm.plasma):
                 x.append(ver[0])
                 y.append(ver[1])
 
-    sc = ax.scatter(x, y, s=1, cmap=cmap, c=c) # map closeness values as color mapping on the verticies
+    sc = ax.scatter(x, y, s=1, cmap=cmap, norm=TwoSlopeNorm(0), c=c) # map closeness values as color mapping on the verticies
     ax.set_title(name)
     fig.colorbar(sc, ax=ax)
 
@@ -95,8 +96,8 @@ def plot_graph_diff(G, c, fig, ax, name, cmap=plt.cm.plasma):
 def apply(g, seed, weight, convex_hull, ax, method, method_name):
     # calculate centrality values
     vp = None
-    if method_name == "Betweenness":
-        vp, ep = method(g, weight=weight)
+    if method_name == "Closeness":
+        vp = method(g, weight=weight)
     elif method_name == "Eigenvector":
         ep, vp = method(g, weight=weight)
     elif method_name == "Hits":
@@ -136,8 +137,8 @@ def apply_corrected(g, seed, weight, convex_hull, ax, method, method_name):
     # calculate centrality values
     vp = None
     ep = None
-    if method_name == "Betweenness":
-        vp, ep = method(g, weight=weight)
+    if method_name == "Closeness":
+        vp = method(g, weight=weight)
     elif method_name == "Eigenvector":
         ep, vp = method(g, weight=weight)
     elif method_name == "Hits":
@@ -183,7 +184,7 @@ def apply_corrected(g, seed, weight, convex_hull, ax, method, method_name):
 #   - apply centrality measure to the next axis
 # - Draw the corresponding resulting models into a grid
 #
-points, seed = random_graph(n=5000)
+points, seed = random_graph(n=5000, seed=303437129487698362622376224319354280305)
 g, weight = spatial_graph(points)
 g = GraphView(g)
 
@@ -193,15 +194,15 @@ convex_hull = centrality.convex_hull(g)
 # plot graph with convex_hull
 fig_graph, ax_graph = plt.subplots(figsize=(15, 12))
 # draw without any centrality measure `vp`
-vp, ep = betweenness(g, weight=weight)
+vp = closeness(g, weight=weight)
 
 plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"Pointcloud (seed: {seed})")
-fig_graph.savefig("model_prediction_graph_original_betweenness_5000.svg", format='svg')
+fig_graph.savefig("model_prediction_graph_original_closeness_5000.svg", format='svg')
 
 # normalization
 min_val, max_val = vp.a.min(), vp.a.max()
 vp.a = (vp.a - min_val) / (max_val - min_val)
-vp_betweenness_original = vp
+vp_closeness_original = vp
 
 for percentage in np.arange(0.1, 1, 0.1, dtype=float):
     print(f"Percentage: {percentage:.0%}")
@@ -211,15 +212,15 @@ for percentage in np.arange(0.1, 1, 0.1, dtype=float):
     # draw subgraph
     fig_sub = plt.figure(figsize=(25, 12))
     ax1, ax2 = fig_sub.subplots(1, 2)
-    vp, ep = betweenness(g_sub, weight=weight_sub)
+    vp = closeness(g_sub, weight=weight_sub)
     plot.graph_plot(fig_sub, ax1, g_sub, vp, convex_hull, f"{percentage:.0%} of Pointcloud (seed: {seed})")
 
     min_val, max_val = vp.a.min(), vp.a.max()
     vp.a = (vp.a - min_val) / (max_val - min_val)
 
-    vp_betweenness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, None, betweenness, "Betweenness")
-    plot.graph_plot(fig_sub, ax2, g_sub, vp_betweenness_corrected, convex_hull, f"{percentage:.0%} of Pointcloud with applied prediction")
-    fig_sub.savefig(f"model_prediction_subgraph_betweenness_5000_{percentage * 100:.0f}_percent.svg", format='svg')
+    vp_closeness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, None, closeness, "Closeness")
+    plot.graph_plot(fig_sub, ax2, g_sub, vp_closeness_corrected, convex_hull, f"{percentage:.0%} of Pointcloud with applied prediction")
+    fig_sub.savefig(f"model_prediction_subgraph_closeness_5000_{percentage * 100:.0f}_percent.svg", format='svg')
 
     distance_of_center = 0.5 * percentage
 
@@ -243,10 +244,10 @@ for percentage in np.arange(0.1, 1, 0.1, dtype=float):
         # sub_position = g_sub.vp["pos"][sub_key]
         # print(f"position: {position} | sub_position: {sub_position}")
 
-        # calculate for betweenness
-        value = vp_betweenness_original[key]
+        # calculate for closeness
+        value = vp_closeness_original[key]
         pre_prediction = vp[sub_key]
-        sub_value = vp_betweenness_corrected[sub_key]
+        sub_value = vp_closeness_corrected[sub_key]
 
         scores.append(value)
         raw_sub_scores.append(pre_prediction)
@@ -291,4 +292,4 @@ for percentage in np.arange(0.1, 1, 0.1, dtype=float):
     plot_graph_diff(g, diff_scores, fig, plot_sub_graph_ax, "Differences after correction of sub graph compared to original graph", plt.cm.seismic)
     plot_graph_diff(g, vp.a, fig, plot_sub_graph_before_ax, "Sub Graph (extracted region of original graph) without correction")
 
-    fig.savefig(f"model_prediction_subgraph_betweenness_5000_{percentage * 100:.0f}_percentage_diff.svg", format='svg')
+    fig.savefig(f"model_prediction_subgraph_closeness_5000_{percentage * 100:.0f}_percentage_diff.svg", format='svg')