Uni/boundary-aware-centrality
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fix: correct centrality comparison script

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Yves Biener
2026-03-09 07:56:41 +01:00
parent 32dd37feea
commit 4c87d4e7b0
1 changed files with 29 additions and 18 deletions
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43
comparison.py
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@@ -26,7 +26,7 @@ def leverage(g, weight):
neighbours = g.get_all_neighbours(v) neighbours = g.get_all_neighbours(v)
ki = len(neighbours) ki = len(neighbours)
# sum # sum
for nv, props in neighbours: for nv in neighbours:
other_neighbours = g.get_all_neighbours(nv) other_neighbours = g.get_all_neighbours(nv)
kj = len(other_neighbours) kj = len(other_neighbours)
li += (ki - kj) / (ki + kj) li += (ki - kj) / (ki + kj)
@@ -104,6 +104,14 @@ def spatial_graph(adata):
def apply(g, seed, weight, convex_hull, ax, method, method_name): def apply(g, seed, weight, convex_hull, ax, method, method_name):
# calculate centrality values # calculate centrality values
vp = None
if method_name == "Betweeness":
vp, ep = method(g, weight=weight)
elif method_name == "Eigenvector":
ep, vp = method(g, weight=weight)
elif method_name == "Hits":
ep, vp, hub_centrality = method(g, weight=weight)
else:
vp = method(g, weight=weight) vp = method(g, weight=weight)
vp.a = np.nan_to_num(vp.a) # correct floating point values vp.a = np.nan_to_num(vp.a) # correct floating point values
@@ -128,7 +136,7 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
[lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt] [lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
) )
# plot model containing modeled piece-wise linear function # plot model containing modeled piece-wise linear function
plot.quantification_plot(ax, quantification, d_curve, C_curve, 'Models', aic_opt) plot.quantification_plot(ax, quantification, d_curve, C_curve, method_name, aic_opt)
# #
@@ -139,30 +147,33 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
# - apply centrality measure to the next axis # - apply centrality measure to the next axis
# - Draw the corresponding resulting models into a grid # - Draw the corresponding resulting models into a grid
# #
points, seed = random_graph() points, seed = random_graph(n=5000)
g, weight = spatial_graph(points) g, weight = spatial_graph(points)
g = GraphView(g) g = GraphView(g)
# calculate convex hull # calculate convex hull
convex_hull = centrality.convex_hull(g) convex_hull = centrality.convex_hull(g)
# plot graph with convex_hull # plot graph with convex_hull
fig_graph, ax_graph = plt.subplots(figsize=(15, 5)) fig_graph, ax_graph = plt.subplots(figsize=(15, 12))
# draw without any centrality measure `vp` # draw without any centrality measure `vp`
plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"Pointcloud (seed: {seed}\n{method_name}") vp = g.new_vertex_property("double")
plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"Pointcloud (seed: {seed}")
fig_graph.savefig("Pointcloud_graph.svg", format='svg') fig_graph.savefig("Pointcloud_graph.svg", format='svg')
fig = plt.figure(figsize=(15, 10)) fig = plt.figure(figsize=(15, 12))
axs = fig.subplots(2, 4) row1, row2 = fig.subplots(2, 4)
for ax in axs: ax1, ax2, ax3, ax4 = row1
# TODO select corresponding centrality measure method # TODO select corresponding centrality measure method
apply(g, seed, weight, convex_hull, ax, closeness, "Closeness") apply(g, seed, weight, convex_hull, ax1, closeness, "Closeness")
apply(g, seed, weight, convex_hull, ax, pagerank, "PageRank") apply(g, seed, weight, convex_hull, ax2, pagerank, "PageRank")
apply(g, seed, weight, convex_hull, ax, betweeness, "Betweeness") apply(g, seed, weight, convex_hull, ax3, betweenness, "Betweeness")
apply(g, seed, weight, convex_hull, ax, eigenvector, "Eigenvector") apply(g, seed, weight, convex_hull, ax4, eigenvector, "Eigenvector")
apply(g, seed, weight, convex_hull, ax, katz, "Katz")
apply(g, seed, weight, convex_hull, ax, hits, "Hits") ax1, ax2, ax3, ax4 = row2
apply(g, seed, weight, convex_hull, ax, leverage, "Leverage") apply(g, seed, weight, convex_hull, ax1, katz, "Katz")
apply(g, seed, weight, convex_hull, ax, degree, "Degree") apply(g, seed, weight, convex_hull, ax2, hits, "Hits")
apply(g, seed, weight, convex_hull, ax3, leverage, "Leverage")
apply(g, seed, weight, convex_hull, ax4, degree, "Degree")
fig.savefig(f"Comparison_Pointcloud.svg", format='svg') fig.savefig(f"Comparison_Pointcloud.svg", format='svg')