WIP diff centrality scores
Check whether model correction is reliable in predicting the "expected" outcome.
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@@ -79,7 +79,7 @@ def spatial_graph(adata):
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return g, weight
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def plot_graph_diff(G, c, fig, ax, name):
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def plot_graph_diff(G, c, fig, ax, name, cmap=plt.cm.plasma):
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pos = G.vp["pos"]
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x = []
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y = []
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@@ -89,7 +89,7 @@ def plot_graph_diff(G, c, fig, ax, name):
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x.append(ver[0])
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y.append(ver[1])
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sc = ax.scatter(x, y, s=1, cmap=plt.cm.plasma, c=c) # map closeness values as color mapping on the verticies
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sc = ax.scatter(x, y, s=1, cmap=cmap, c=c) # map closeness values as color mapping on the verticies
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ax.set_title(name)
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fig.colorbar(sc, ax=ax)
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@@ -200,7 +200,7 @@ row1, row2 = fig.subplots(2, 2)
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ax1, ax2 = row1
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# TODO select corresponding centrality measure method
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vp_closeness = apply(g, seed, weight, convex_hull, ax1, closeness, "Closeness")
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# vp_betweenness = apply(g, seed, weight, convex_hull, ax2, betweenness, "Betweeness")
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vp_betweenness = apply(g, seed, weight, convex_hull, ax2, betweenness, "Betweeness")
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# calculate convex hull
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convex_hull = centrality.convex_hull(g_sub)
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@@ -214,75 +214,82 @@ fig_graph.savefig("Diff_subgraph.svg", format='svg')
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ax1, ax2 = row2
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vp_closeness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, ax1, closeness, "Closeness")
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# vp_betweeness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, ax2, betweenness, "Betweeness")
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vp_betweeness_corrected = apply_corrected(g_sub, seed, weight_sub, convex_hull, ax2, betweenness, "Betweeness")
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fig.savefig(f"Diff_scores.svg", format='svg')
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# TODO how can I match the two vp's such that I can actually create a diff?
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#
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print(f"Closeness: {vp_closeness}")
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print(f"Closeness corrected: {vp_closeness_corrected}")
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for type in ['closeness', 'betweenness']:
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print(type)
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sub_keys = iter(g_sub.vertices())
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keys = iter(g.vertices())
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sub_keys = iter(g_sub.vertices())
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keys = iter(g.vertices())
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scores = []
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sub_scores = []
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scores = []
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sub_scores = []
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diff_scores = []
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for sub_key in sub_keys:
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key = next(keys)
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position = g.vp["pos"][key]
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while not (position[0] > 0.33 and position[0] <= 0.66 and position[1] > 0.33 and position[1] <= 0.66):
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for sub_key in sub_keys:
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key = next(keys)
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position = g.vp["pos"][key]
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# NOTE print corresponding position (which are identical)
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# position = g.vp["pos"][key]
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# sub_position = g_sub.vp["pos"][sub_key]
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# print(f"position: {position} | sub_position: {sub_position}")
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while not (position[0] > 0.33 and position[0] <= 0.66 and position[1] > 0.33 and position[1] <= 0.66):
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key = next(keys)
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position = g.vp["pos"][key]
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# NOTE print corresponding position (which are identical)
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# position = g.vp["pos"][key]
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# sub_position = g_sub.vp["pos"][sub_key]
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# print(f"position: {position} | sub_position: {sub_position}")
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value = vp_closeness[key]
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sub_value = vp_closeness_corrected[sub_key]
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scores.append(value)
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sub_scores.append(sub_value)
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# print(f"value: {value} | sub_value: {sub_value}")
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# TODO what do I want to know?
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# - median score comparison?
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# - max delta's between scores
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# - improvement compared to with and without correction?
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value = 0.0
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sub_value = 0.0
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if type == 'closeness':
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value = vp_closeness[key]
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sub_value = vp_closeness_corrected[sub_key]
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else:
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value = vp_betweenness[key]
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sub_value = vp_betweeness_corrected[sub_key]
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# TODO can I create the scatter graph with the points with their corresponding values?
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median_score = np.median(scores)
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median_sub_score = np.median(sub_scores)
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print(f"median score: {median_score}")
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print(f"median sub_score: {median_sub_score}")
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print(f"median delta: {(median_score - median_sub_score)}")
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print("")
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scores.append(value)
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sub_scores.append(sub_value)
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diff_scores.append(value - sub_value)
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max_value_score = np.max(scores)
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max_value_sub_score = np.max(sub_scores)
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print(f"max value score: {max_value_score}")
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print(f"max value sub_score: {max_value_sub_score}")
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print(f"max value delta: {(max_value_score - max_value_sub_score)}")
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print("")
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median_score = np.median(scores)
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median_sub_score = np.median(sub_scores)
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print(f"\tmedian score: {median_score}")
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print(f"\tmedian sub_score: {median_sub_score}")
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print(f"\tmedian delta: {(median_score - median_sub_score)}")
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print("")
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min_value_score = np.min(scores)
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min_value_sub_score = np.min(sub_scores)
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print(f"min value score: {min_value_score}")
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print(f"min value sub_score: {min_value_sub_score}")
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print(f"min value delta: {(min_value_score - min_value_sub_score)}")
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max_value_score = np.max(scores)
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max_value_sub_score = np.max(sub_scores)
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print(f"\tmax value score: {max_value_score}")
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print(f"\tmax value sub_score: {max_value_sub_score}")
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print(f"\tmax value delta: {(max_value_score - max_value_sub_score)}")
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print("")
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min_value_score = np.min(scores)
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min_value_sub_score = np.min(sub_scores)
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print(f"\tmin value score: {min_value_score}")
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print(f"\tmin value sub_score: {min_value_sub_score}")
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print(f"\tmin value delta: {(min_value_score - min_value_sub_score)}")
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print("")
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fig = plt.figure(figsize=(35, 10))
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plot_graph_ax, plot_sub_graph_ax, plot_sub_graph_before_ax = fig.subplots(1, 3)
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fig = plt.figure(figsize=(35, 10))
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plot_graph_ax, plot_sub_graph_ax, plot_sub_graph_before_ax = fig.subplots(1, 3)
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plot_graph_diff(g, scores, fig, plot_graph_ax, "Original Graph (region of sub graph)")
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plot_graph_diff(g, sub_scores, fig, plot_sub_graph_ax, "Sub Graph (extracted region of original graph) with correction")
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plot_graph_diff(g, scores, fig, plot_graph_ax, "Original Graph (region of sub graph)")
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plot_graph_diff(g, diff_scores, fig, plot_sub_graph_ax, "Differences after correction of sub graph compared to original graph", plt.cm.seismic)
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vp = closeness(g_sub, weight=weight_sub)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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# normalization
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min_val, max_val = vp.a.min(), vp.a.max()
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vp.a = (vp.a - min_val) / (max_val - min_val)
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plot_graph_diff(g, vp.a, fig, plot_sub_graph_before_ax, "Sub Graph (extracted region of original graph) without correction")
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vp = None
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ep = None
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if type == 'closeness':
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vp = closeness(g_sub, weight=weight_sub)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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else:
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vp, ep = betweenness(g_sub, weight=weight_sub)
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vp.a = np.nan_to_num(vp.a) # correct floating point values
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# normalization
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# min_val, max_val = vp.a.min(), vp.a.max()
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# vp.a = (vp.a - min_val) / (max_val - min_val)
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plot_graph_diff(g, vp.a, fig, plot_sub_graph_before_ax, "Sub Graph (extracted region of original graph) without correction")
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fig.savefig(f"Diff_graph_scatter.svg", format='svg')
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fig.savefig(f"Diff_graph_scatter_{type}.svg", format='svg')
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