WIP: compare prediction of sub graphs with original graph scorings
This commit is contained in:
@@ -48,17 +48,13 @@ def random_graph(n=5000, seed=None):
|
||||
return rng.random((n, 2)), seed
|
||||
|
||||
|
||||
def sub_spatial_graph(adata):
|
||||
"""
|
||||
Generate the spatial graph using delaunay for the given `adata`.
|
||||
`adata` will contain the calculated spatial graph contents in the keys
|
||||
adata.obsm['spatial']` in case the `adata` is created from a dataset of *squidpy*.
|
||||
@return [Graph] generated networkx graph from adata.obsp['spatial_distances']
|
||||
"""
|
||||
def sub_spatial_graph(adata, percentage):
|
||||
sub_adata = np.array([])
|
||||
distance_of_center = 0.5 * percentage
|
||||
for point in adata:
|
||||
if point[0] > 0.33 and point[0] <= 0.66 and point[1] > 0.33 and point[1] <= 0.66:
|
||||
sub_adata = np.append(sub_adata, [point[0], point[1]])
|
||||
if point[0] > 0.5 - distance_of_center and point[0] <= 0.5 + distance_of_center:
|
||||
if point[1] > 0.5 - distance_of_center and point[1] <= 0.5 + distance_of_center:
|
||||
sub_adata = np.append(sub_adata, [point[0], point[1]])
|
||||
|
||||
sub_adata = sub_adata.reshape(sub_adata.shape[0] // 2, 2)
|
||||
return spatial_graph(sub_adata)
|
||||
@@ -83,11 +79,13 @@ def plot_graph_diff(G, c, fig, ax, name, cmap=plt.cm.plasma):
|
||||
pos = G.vp["pos"]
|
||||
x = []
|
||||
y = []
|
||||
distance_of_center = 0.5 * percentage
|
||||
for v in G.vertices():
|
||||
ver = pos[v]
|
||||
if ver[0] > 0.33 and ver[0] <= 0.66 and ver[1] > 0.33 and ver[1] <= 0.66:
|
||||
x.append(ver[0])
|
||||
y.append(ver[1])
|
||||
if ver[0] > 0.5 - distance_of_center and ver[0] <= 0.5 + distance_of_center:
|
||||
if ver[1] > 0.5 - distance_of_center and ver[1] <= 0.5 + distance_of_center:
|
||||
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
|
||||
ax.set_title(name)
|
||||
@@ -97,7 +95,7 @@ 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 == "Betweeness":
|
||||
if method_name == "Betweenness":
|
||||
vp, ep = method(g, weight=weight)
|
||||
elif method_name == "Eigenvector":
|
||||
ep, vp = method(g, weight=weight)
|
||||
@@ -107,10 +105,6 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
|
||||
vp = method(g, weight=weight)
|
||||
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)
|
||||
|
||||
# generate model based on convex hull and associated centrality values
|
||||
quantification = plot.quantification_data(g, vp, convex_hull)
|
||||
|
||||
@@ -128,7 +122,12 @@ 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]
|
||||
)
|
||||
# plot model containing modeled piece-wise linear function
|
||||
plot.quantification_plot(ax, quantification, d_curve, C_curve, method_name, aic_opt)
|
||||
if ax is not None:
|
||||
plot.quantification_plot(ax, quantification, d_curve, C_curve, method_name, aic_opt)
|
||||
|
||||
# normalization
|
||||
min_val, max_val = vp.a.min(), vp.a.max()
|
||||
vp.a = (vp.a - min_val) / (max_val - min_val)
|
||||
|
||||
return vp
|
||||
|
||||
@@ -136,7 +135,8 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
|
||||
def apply_corrected(g, seed, weight, convex_hull, ax, method, method_name):
|
||||
# calculate centrality values
|
||||
vp = None
|
||||
if method_name == "Betweeness":
|
||||
ep = None
|
||||
if method_name == "Betweenness":
|
||||
vp, ep = method(g, weight=weight)
|
||||
elif method_name == "Eigenvector":
|
||||
ep, vp = method(g, weight=weight)
|
||||
@@ -165,9 +165,15 @@ def apply_corrected(g, seed, weight, convex_hull, ax, method, method_name):
|
||||
[lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
|
||||
)
|
||||
# plot model containing modeled piece-wise linear function
|
||||
plot.quantification_plot(ax, quantification, d_curve, C_curve, method_name, aic_opt)
|
||||
if ax is not None:
|
||||
plot.quantification_plot(ax, quantification, d_curve, C_curve, method_name, aic_opt)
|
||||
|
||||
return centrality.correct(g, vp, m_opt, c0_opt, b_opt)
|
||||
vp = centrality.correct(g, vp, m_opt, c0_opt, b_opt)
|
||||
# normalization
|
||||
min_val, max_val = vp.a.min(), vp.a.max()
|
||||
vp.a = (vp.a - min_val) / (max_val - min_val)
|
||||
|
||||
return vp
|
||||
|
||||
#
|
||||
# - Create a random point cloud and calculate a triangulation on it
|
||||
@@ -177,61 +183,59 @@ 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=3000)
|
||||
points, seed = random_graph(n=5000)
|
||||
g, weight = spatial_graph(points)
|
||||
g = GraphView(g)
|
||||
|
||||
g_sub, weight_sub = sub_spatial_graph(points)
|
||||
g_sub = GraphView(g_sub)
|
||||
|
||||
# calculate convex hull
|
||||
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 = g.new_vertex_property("double")
|
||||
vp, ep = betweenness(g, weight=weight)
|
||||
|
||||
plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"Pointcloud (seed: {seed})")
|
||||
fig_graph.savefig("Diff_graph.svg", format='svg')
|
||||
fig_graph.savefig("model_prediction_graph_original_betweenness_5000.svg", format='svg')
|
||||
|
||||
fig = plt.figure(figsize=(15, 12))
|
||||
row1, row2 = fig.subplots(2, 2)
|
||||
# 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
|
||||
|
||||
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")
|
||||
for percentage in np.arange(0.1, 1, 0.1, dtype=float):
|
||||
print(f"Percentage: {percentage:.0%}")
|
||||
g_sub, weight_sub = sub_spatial_graph(points, percentage)
|
||||
g_sub = GraphView(g_sub)
|
||||
convex_hull = centrality.convex_hull(g_sub)
|
||||
# draw subgraph
|
||||
fig_sub = plt.figure(figsize=(25, 12))
|
||||
ax1, ax2 = fig_sub.subplots(1, 2)
|
||||
vp, ep = betweenness(g_sub, weight=weight_sub)
|
||||
plot.graph_plot(fig_sub, ax1, g_sub, vp, convex_hull, f"{percentage:.0%} of Pointcloud (seed: {seed})")
|
||||
|
||||
# calculate convex hull
|
||||
convex_hull = centrality.convex_hull(g_sub)
|
||||
min_val, max_val = vp.a.min(), vp.a.max()
|
||||
vp.a = (vp.a - min_val) / (max_val - min_val)
|
||||
|
||||
# plot graph with convex_hull
|
||||
fig_graph, ax_graph = plt.subplots(figsize=(15, 12))
|
||||
# draw without any centrality measure `vp`
|
||||
vp = g_sub.new_vertex_property("double")
|
||||
plot.graph_plot(fig_graph, ax_graph, g_sub, vp, convex_hull, f"Pointcloud (seed: {seed})")
|
||||
fig_graph.savefig("Diff_subgraph.svg", format='svg')
|
||||
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')
|
||||
|
||||
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")
|
||||
|
||||
fig.savefig(f"Diff_scores.svg", format='svg')
|
||||
|
||||
for type in ['closeness', 'betweenness']:
|
||||
print(type)
|
||||
distance_of_center = 0.5 * percentage
|
||||
|
||||
sub_keys = iter(g_sub.vertices())
|
||||
keys = iter(g.vertices())
|
||||
|
||||
scores = []
|
||||
raw_sub_scores = []
|
||||
sub_scores = []
|
||||
raw_diff_scores = []
|
||||
diff_scores = []
|
||||
|
||||
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):
|
||||
while not (position[0] > 0.5 - distance_of_center and position[0] <= 0.5 + distance_of_center and position[1] > 0.5 - distance_of_center and position[1] <= 0.5 + distance_of_center):
|
||||
key = next(keys)
|
||||
position = g.vp["pos"][key]
|
||||
# NOTE print corresponding position (which are identical)
|
||||
@@ -239,38 +243,45 @@ for type in ['closeness', 'betweenness']:
|
||||
# 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]
|
||||
# calculate for betweenness
|
||||
value = vp_betweenness_original[key]
|
||||
pre_prediction = vp[sub_key]
|
||||
sub_value = vp_betweenness_corrected[sub_key]
|
||||
|
||||
scores.append(value)
|
||||
raw_sub_scores.append(pre_prediction)
|
||||
sub_scores.append(sub_value)
|
||||
raw_diff_scores.append(value - pre_prediction)
|
||||
diff_scores.append(value - sub_value)
|
||||
|
||||
median_score = np.median(scores)
|
||||
median_raw_sub_score = np.median(raw_sub_scores)
|
||||
median_sub_score = np.median(sub_scores)
|
||||
print(f"\tmedian score: {median_score}")
|
||||
print(f"\tmedian raw_sub_score: {median_raw_sub_score}")
|
||||
print(f"\tmedian sub_score: {median_sub_score}")
|
||||
print(f"\tmedian delta: {(median_score - median_sub_score)}")
|
||||
print(f"\tmedian delta (score - raw_sub_score): {(median_score - median_raw_sub_score)}")
|
||||
print(f"\tmedian delta (score - sub_score): {(median_score - median_sub_score)}")
|
||||
print("")
|
||||
|
||||
max_value_score = np.max(scores)
|
||||
max_value_raw_sub_score = np.max(raw_sub_scores)
|
||||
max_value_sub_score = np.max(sub_scores)
|
||||
print(f"\tmax value score: {max_value_score}")
|
||||
print(f"\tmax value raw_sub_score: {max_value_raw_sub_score}")
|
||||
print(f"\tmax value sub_score: {max_value_sub_score}")
|
||||
print(f"\tmax value delta: {(max_value_score - max_value_sub_score)}")
|
||||
print(f"\tmax value delta (score - raw_sub_score): {(max_value_score - max_value_raw_sub_score)}")
|
||||
print(f"\tmax value delta (score - sub_score): {(max_value_score - max_value_sub_score)}")
|
||||
print("")
|
||||
|
||||
min_value_score = np.min(scores)
|
||||
min_value_raw_sub_score = np.min(raw_sub_scores)
|
||||
min_value_sub_score = np.min(sub_scores)
|
||||
print(f"\tmin value score: {min_value_score}")
|
||||
print(f"\tmin value raw_sub_score: {min_value_raw_sub_score}")
|
||||
print(f"\tmin value sub_score: {min_value_sub_score}")
|
||||
print(f"\tmin value delta: {(min_value_score - min_value_sub_score)}")
|
||||
print(f"\tmin value delta (score - raw_sub_score): {(min_value_score - min_value_raw_sub_score)}")
|
||||
print(f"\tmin value delta (score - sub_score): {(min_value_score - min_value_sub_score)}")
|
||||
print("")
|
||||
|
||||
fig = plt.figure(figsize=(35, 10))
|
||||
@@ -278,18 +289,6 @@ for type in ['closeness', 'betweenness']:
|
||||
|
||||
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)
|
||||
|
||||
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")
|
||||
|
||||
fig.savefig(f"Diff_graph_scatter_{type}.svg", format='svg')
|
||||
fig.savefig(f"model_prediction_subgraph_betweenness_5000_{percentage * 100:.0f}_percentage_diff.svg", format='svg')
|
||||
|
||||
@@ -35,8 +35,9 @@ def fit_piece_wise_linear(d, C, M=1000):
|
||||
model.setObjective(gp.quicksum(epsilon[i] * epsilon[i] for i in range(n)), GRB.MINIMIZE)
|
||||
|
||||
# Setting solver parameters for precision
|
||||
model.setParam('OptimalityTol', 1e-4)
|
||||
model.setParam('MIPGap', 0.01)
|
||||
model.setParam('OptimalityTol', 1e-4)
|
||||
model.setParam('MIPGap', 0.01)
|
||||
model.setParam('OutputFlag', 0)
|
||||
|
||||
for i in range(n):
|
||||
# Constraints enforcing piecewise linear fit
|
||||
|
||||
Reference in New Issue
Block a user