WIP diff centrality scores
Check whether model correction is reliable in predicting the "expected" outcome.
This commit is contained in:
@@ -79,7 +79,7 @@ def spatial_graph(adata):
|
||||
return g, weight
|
||||
|
||||
|
||||
def plot_graph_diff(G, c, fig, ax, name):
|
||||
def plot_graph_diff(G, c, fig, ax, name, cmap=plt.cm.plasma):
|
||||
pos = G.vp["pos"]
|
||||
x = []
|
||||
y = []
|
||||
@@ -89,7 +89,7 @@ def plot_graph_diff(G, c, fig, ax, name):
|
||||
x.append(ver[0])
|
||||
y.append(ver[1])
|
||||
|
||||
sc = ax.scatter(x, y, s=1, cmap=plt.cm.plasma, c=c) # map closeness values as color mapping on the verticies
|
||||
sc = ax.scatter(x, y, s=1, cmap=cmap, c=c) # map closeness values as color mapping on the verticies
|
||||
ax.set_title(name)
|
||||
fig.colorbar(sc, ax=ax)
|
||||
|
||||
@@ -200,7 +200,7 @@ row1, row2 = fig.subplots(2, 2)
|
||||
ax1, ax2 = row1
|
||||
# TODO select corresponding centrality measure method
|
||||
vp_closeness = apply(g, seed, weight, convex_hull, ax1, closeness, "Closeness")
|
||||
# vp_betweenness = apply(g, seed, weight, convex_hull, ax2, betweenness, "Betweeness")
|
||||
vp_betweenness = apply(g, seed, weight, convex_hull, ax2, betweenness, "Betweeness")
|
||||
|
||||
# calculate convex hull
|
||||
convex_hull = centrality.convex_hull(g_sub)
|
||||
@@ -214,22 +214,21 @@ fig_graph.savefig("Diff_subgraph.svg", format='svg')
|
||||
|
||||
ax1, ax2 = row2
|
||||
vp_closeness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, ax1, closeness, "Closeness")
|
||||
# vp_betweeness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, ax2, betweenness, "Betweeness")
|
||||
vp_betweeness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, ax2, betweenness, "Betweeness")
|
||||
|
||||
fig.savefig(f"Diff_scores.svg", format='svg')
|
||||
|
||||
# TODO how can I match the two vp's such that I can actually create a diff?
|
||||
#
|
||||
print(f"Closeness: {vp_closeness}")
|
||||
print(f"Closeness corrected: {vp_closeness_corrected}")
|
||||
for type in ['closeness', 'betweenness']:
|
||||
print(type)
|
||||
|
||||
sub_keys = iter(g_sub.vertices())
|
||||
keys = iter(g.vertices())
|
||||
sub_keys = iter(g_sub.vertices())
|
||||
keys = iter(g.vertices())
|
||||
|
||||
scores = []
|
||||
sub_scores = []
|
||||
scores = []
|
||||
sub_scores = []
|
||||
diff_scores = []
|
||||
|
||||
for sub_key in sub_keys:
|
||||
for sub_key in sub_keys:
|
||||
key = next(keys)
|
||||
position = g.vp["pos"][key]
|
||||
while not (position[0] > 0.33 and position[0] <= 0.66 and position[1] > 0.33 and position[1] <= 0.66):
|
||||
@@ -240,49 +239,57 @@ for sub_key in sub_keys:
|
||||
# sub_position = g_sub.vp["pos"][sub_key]
|
||||
# print(f"position: {position} | sub_position: {sub_position}")
|
||||
|
||||
value = 0.0
|
||||
sub_value = 0.0
|
||||
if type == 'closeness':
|
||||
value = vp_closeness[key]
|
||||
sub_value = vp_closeness_corrected[sub_key]
|
||||
else:
|
||||
value = vp_betweenness[key]
|
||||
sub_value = vp_betweeness_corrected[sub_key]
|
||||
|
||||
scores.append(value)
|
||||
sub_scores.append(sub_value)
|
||||
# print(f"value: {value} | sub_value: {sub_value}")
|
||||
# TODO what do I want to know?
|
||||
# - median score comparison?
|
||||
# - max delta's between scores
|
||||
# - improvement compared to with and without correction?
|
||||
diff_scores.append(value - sub_value)
|
||||
|
||||
# TODO can I create the scatter graph with the points with their corresponding values?
|
||||
median_score = np.median(scores)
|
||||
median_sub_score = np.median(sub_scores)
|
||||
print(f"median score: {median_score}")
|
||||
print(f"median sub_score: {median_sub_score}")
|
||||
print(f"median delta: {(median_score - median_sub_score)}")
|
||||
print("")
|
||||
median_score = np.median(scores)
|
||||
median_sub_score = np.median(sub_scores)
|
||||
print(f"\tmedian score: {median_score}")
|
||||
print(f"\tmedian sub_score: {median_sub_score}")
|
||||
print(f"\tmedian delta: {(median_score - median_sub_score)}")
|
||||
print("")
|
||||
|
||||
max_value_score = np.max(scores)
|
||||
max_value_sub_score = np.max(sub_scores)
|
||||
print(f"max value score: {max_value_score}")
|
||||
print(f"max value sub_score: {max_value_sub_score}")
|
||||
print(f"max value delta: {(max_value_score - max_value_sub_score)}")
|
||||
print("")
|
||||
max_value_score = np.max(scores)
|
||||
max_value_sub_score = np.max(sub_scores)
|
||||
print(f"\tmax value score: {max_value_score}")
|
||||
print(f"\tmax value sub_score: {max_value_sub_score}")
|
||||
print(f"\tmax value delta: {(max_value_score - max_value_sub_score)}")
|
||||
print("")
|
||||
|
||||
min_value_score = np.min(scores)
|
||||
min_value_sub_score = np.min(sub_scores)
|
||||
print(f"min value score: {min_value_score}")
|
||||
print(f"min value sub_score: {min_value_sub_score}")
|
||||
print(f"min value delta: {(min_value_score - min_value_sub_score)}")
|
||||
min_value_score = np.min(scores)
|
||||
min_value_sub_score = np.min(sub_scores)
|
||||
print(f"\tmin value score: {min_value_score}")
|
||||
print(f"\tmin value sub_score: {min_value_sub_score}")
|
||||
print(f"\tmin value delta: {(min_value_score - min_value_sub_score)}")
|
||||
print("")
|
||||
|
||||
fig = plt.figure(figsize=(35, 10))
|
||||
plot_graph_ax, plot_sub_graph_ax, plot_sub_graph_before_ax = fig.subplots(1, 3)
|
||||
|
||||
fig = plt.figure(figsize=(35, 10))
|
||||
plot_graph_ax, plot_sub_graph_ax, plot_sub_graph_before_ax = fig.subplots(1, 3)
|
||||
plot_graph_diff(g, scores, fig, plot_graph_ax, "Original Graph (region of sub graph)")
|
||||
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, scores, fig, plot_graph_ax, "Original Graph (region of sub graph)")
|
||||
plot_graph_diff(g, sub_scores, fig, plot_sub_graph_ax, "Sub Graph (extracted region of original graph) with correction")
|
||||
vp = None
|
||||
ep = None
|
||||
if type == 'closeness':
|
||||
vp = closeness(g_sub, weight=weight_sub)
|
||||
vp.a = np.nan_to_num(vp.a) # correct floating point values
|
||||
else:
|
||||
vp, ep = betweenness(g_sub, weight=weight_sub)
|
||||
vp.a = np.nan_to_num(vp.a) # correct floating point values
|
||||
# normalization
|
||||
# min_val, max_val = vp.a.min(), vp.a.max()
|
||||
# vp.a = (vp.a - min_val) / (max_val - min_val)
|
||||
plot_graph_diff(g, vp.a, fig, plot_sub_graph_before_ax, "Sub Graph (extracted region of original graph) without correction")
|
||||
|
||||
vp = closeness(g_sub, weight=weight_sub)
|
||||
vp.a = np.nan_to_num(vp.a) # correct floating point values
|
||||
# normalization
|
||||
min_val, max_val = vp.a.min(), vp.a.max()
|
||||
vp.a = (vp.a - min_val) / (max_val - min_val)
|
||||
plot_graph_diff(g, vp.a, fig, plot_sub_graph_before_ax, "Sub Graph (extracted region of original graph) without correction")
|
||||
|
||||
fig.savefig(f"Diff_graph_scatter.svg", format='svg')
|
||||
fig.savefig(f"Diff_graph_scatter_{type}.svg", format='svg')
|
||||
|
||||
Reference in New Issue
Block a user