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neighbors_preservation.py
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import pygraphviz as pgv
import networkx as nx
import math
# import numpy as np
# from numpy import random
# from scipy.spatial import distance
# import time
#
# def euclidean_closest_nodes(node, nodes, k_i):
# '''
# Computes the distance matrix between point.
# '''
#
# node = np.array(node)
# nodes = np.array(nodes)
#
# distances = distance.cdist(node, nodes)
# indices = np.argpartition(distances, 3)
#
# return indices
def euclidean_distance(source, target):
x_source1 = float(source['pos'].split(",")[0])
x_target1 = float(target['pos'].split(",")[0])
y_source1 = float(source['pos'].split(",")[1])
y_target1 = float(target['pos'].split(",")[1])
geomDistance = math.sqrt((x_source1 - x_target1)**2 + (y_source1 - y_target1)**2)
return geomDistance
def find_graph_closest_nodes(G, r_g, sourceStr, all_sp):
closest = []
# vertices = list(nx.nodes(G))
# source = G.nodes[sourceStr]
# for i in range(0, len(vertices)):
for target in nx.nodes(G):
if(target == sourceStr):
continue
graph_theoretic_distance = len(all_sp[sourceStr][target])-1
if(graph_theoretic_distance <= r_g):
closest.append(target)
return closest
def find_space_closest_nodes(Gnx, k_i, sourceStr):
closest = []
vertices = list(nx.nodes(Gnx))
source = Gnx.nodes[sourceStr]
closest_dict = dict()
for i in range(0, len(vertices)):
targetStr = vertices[i]
target = Gnx.nodes[targetStr]
if(target == source):
continue
space_distance = euclidean_distance(source, target)
closest_dict[targetStr] = space_distance
res = list(sorted(closest_dict, key=closest_dict.__getitem__, reverse=False))
closest = res[:k_i+1]
return closest
def compute_neig_preservation(G, weighted=True, all_sp=None):
G = nx.Graph(G)
if all_sp is None:
if(weighted):
# converting weights in float
all_weights_n = nx.get_node_attributes(G, "weight")
for nk in all_weights_n.keys():
all_weights_n[nk] = float(all_weights_n[nk])
nx.set_node_attributes(G, all_weights_n, "weight")
all_weights_e = nx.get_edge_attributes(G, "weight")
for ek in all_weights_e.keys():
all_weights_e[ek] = float(all_weights_e[ek])
nx.set_edge_attributes(G, all_weights_e, "weight")
all_sp = nx.shortest_path(G, weight="weight")
else:
all_sp = nx.shortest_path(G)
r_g = 3
vertices = list(nx.nodes(G))
sum = 0
# all_pos = nx.get_node_attributes(G, "pos")
# nodes_positions = {}
# for v in all_pos.keys():
# x = float(all_pos[v].split(",")[0])
# y = float(all_pos[v].split(",")[1])
# nodes_positions[v] = (x, y)
for i in range(0, len(vertices)):
sourceStr = vertices[i]
source = G.nodes[sourceStr]
graph_neighbors = find_graph_closest_nodes(G, r_g, sourceStr, all_sp)
k_i = len(graph_neighbors)
# x = float(all_pos[sourceStr].split(",")[0])
# y = float(all_pos[sourceStr].split(",")[1])
# curr_node_pos = [(x, y)]
# space_neigobors_new = euclidean_closest_nodes([curr_node_pos], list(nodes_positions.values()), k_i)
space_neigobors = find_space_closest_nodes(G, k_i, sourceStr)
vertices_intersection = set(graph_neighbors).intersection(set(space_neigobors))
vertices_union = set(graph_neighbors).union(set(space_neigobors))
sum += len(vertices_intersection)/len(vertices_union)
pres = (1/len(vertices))*sum
pres = round(pres, 3)
return pres