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bigraph_heuristic.py
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bigraph_heuristic.py
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import argparse
import time
import numpy as np
import torch
import random
from colorama import Back, Fore, Style, init
from model import DQN, Bigraph, ElecGraph, TraGraph
from utils import (calculate_pairwise_connectivity, influenced_tl_by_elec,
init_env, nodes_ranked_by_CI, nodes_ranked_by_Degree)
FILE = './data/e10kv2tl.json'
EFILE = './data/electricity/all_dict_correct.json'
TFILE1 = './data/road/road_junc_map.json'
TFILE2 = './data/road/road_type_map.json'
TFILE3 = './data/road/tl_id_road2elec_map.json'
ept = './embedding/elec_feat.pt'
tpt = './embedding/tra_feat.pt'
bpt = ('./embedding/bifeatures/bi_elec_feat.pt', './embedding/bifeatures/bi_tra_feat.pt')
EMBED_DIM = 64
HID_DIM = 128
FEAT_DIM = 64
KHOP=5
MAX_DP = 660225576
BASE = 100000000
if __name__ == "__main__":
egraph = ElecGraph(file=EFILE,
embed_dim=EMBED_DIM,
hid_dim=HID_DIM,
feat_dim=FEAT_DIM,
khop=KHOP,
epochs=500,
pt_path=ept)
tgraph = TraGraph(file1=TFILE1, file2=TFILE2, file3=TFILE3,
embed_dim=EMBED_DIM,
hid_dim=HID_DIM,
feat_dim=FEAT_DIM,
khop=KHOP,
epochs=300,
pt_path=tpt)
bigraph = Bigraph(efile=EFILE, tfile1=TFILE1, tfile2=TFILE2, tfile3=TFILE3, file=FILE,
embed_dim=EMBED_DIM,
hid_dim=HID_DIM,
feat_dim=FEAT_DIM,
subgraph = (egraph, tgraph),
khop=KHOP,
epochs=600,
pt_path=bpt)
num_elec = 10
num_road = 10
bigraph_CI = nodes_ranked_by_CI(bigraph.nxgraph)
bigraph_degree = nodes_ranked_by_Degree(bigraph.nxgraph)
egraph_CI = nodes_ranked_by_CI(egraph.nxgraph)
egraph_degree = nodes_ranked_by_Degree(egraph.nxgraph)
tgraph_CI = nodes_ranked_by_CI(tgraph.nxgraph)
tgraph_degree = nodes_ranked_by_Degree(tgraph.nxgraph)
attack_nodes = egraph_degree[:num_elec] + tgraph_degree[:num_road]
random.shuffle(attack_nodes)
tgc = tgraph.nxgraph.copy()
result = [0]
elec_env = init_env()
origin_val = calculate_pairwise_connectivity(tgc)
t_val = 1
tpower = elec_env.ruin([])
total_reward = 0
choosen_road = []
choosen_elec = []
for node in attack_nodes:
h_val = t_val
hpower = tpower
if node //BASE == 9:
choosen_road.append(node)
else:
choosen_elec.append(node)
tpower,elec_state = elec_env.ruin(choosen_elec,flag=0)
choosen_road += influenced_tl_by_elec(elec_state, bigraph.elec2road, tgc)
tgc.remove_nodes_from(choosen_road)
t_val = calculate_pairwise_connectivity(tgc) / origin_val
reward_elec = (hpower - tpower) / MAX_DP
reward_road = (h_val - t_val)
reward = (reward_road + reward_elec) * 1e4
total_reward += reward
result.append(total_reward)
# np.savetxt('./result/bi_degree_{}_{}.txt'.format(num_elec,num_road),np.array(result))
# np.savetxt('./result/heuristic/bi_CI_reward_{}_{}.txt'.format(num_elec,num_road),np.array(result))
# np.savetxt('./result/heuristic/bi_CI_nodes_{}_{}.txt'.format(num_elec,num_road),np.array(attack_nodes))
np.savetxt('./result/heuristic/bi_degree_reward_{}_{}.txt'.format(num_elec,num_road),np.array(result))
np.savetxt('./result/heuristic/bi_degree_nodes_{}_{}.txt'.format(num_elec,num_road),np.array(attack_nodes))
# tgc = tgraph.nxgraph.copy()
# reward = []
# elec_env = init_env()
# original_power = elec_env.ruin([])
# origin_val = calculate_pairwise_connectivity(tgc)
# t_val = 1
# tpower = original_power
# total_reward = 0
# # for i in range(num_road):
# # h_val = t_val
# # if road_nodes_CI[i] in tgc.nodes():
# # tgc.remove_node(road_nodes_CI[i])
# # t_val = calculate_pairwise_connectivity(tgc) / origin_val
# # total_reward += (0.5*(h_val - t_val)*1e4)
# # reward.append(total_reward)
# # tgc = tgraph.nxgraph.copy()
# # for i in range(num_elec):
# # h_val = t_val
# # hpower = tpower
# # tpower,elec_state = elec_env.ruin([elec_nodes_CI[i]],flag=0)
# # nodes = influenced_tl_by_elec(elec_state, bigraph.elec2road, tgc)
# # tgc.remove_nodes_from(nodes)
# # t_val = calculate_pairwise_connectivity(tgc) / origin_val
# # total_reward += (0.5*(hpower - tpower)/1e5)
# # total_reward += (0.5*(h_val - t_val)*1e4)
# # reward.append(total_reward)
# for i in range(num_elec):
# h_val = t_val
# hpower = tpower
# tpower,elec_state = elec_env.ruin([elec_nodes_rdn[i]],flag=0)
# nodes = influenced_tl_by_elec(elec_state, bigraph.elec2road, tgc)
# tgc.remove_nodes_from(nodes)
# t_val = calculate_pairwise_connectivity(tgc) / origin_val
# # total_reward += (0.5*(hpower - tpower)/1e5)
# # total_reward += (0.5*(h_val - t_val)*1e4)
# reward.append(t_val)
# np.savetxt('./result/elec_CI_{}.txt'.format(num_elec),np.array(reward))
# # np.savetxt('./result/CI_{}_{}.txt'.format(num_elec,num_road),np.array(reward))
# val = []
# tgc = tgraph.nxgraph.copy()
# origin_val = calculate_pairwise_connectivity(tgc)
# num = 0
# elec_env = init_env()
# nodes3 = [node for node in egraph.nxgraph.nodes() if node//100000000 == 3]
# for node in nodes3:
# tgc = tgraph.nxgraph.copy()
# reward = []
# elec_env = init_env()
# original_power = elec_env.ruin([])
# origin_val = calculate_pairwise_connectivity(tgc)
# t_val = 1
# tpower = original_power
# total_reward = 0
# print(node)
# tpower,elec_state = elec_env.ruin([node],flag=0)
# elec_env.reset()
# # nodes = influenced_tl_by_elec(elec_state, bigraph.elec2road, tgc)
# # tgc.remove_nodes_from(nodes)
# # result = calculate_pairwise_connectivity(tgc) / origin_val
# val.append((node,tpower))
# # val.append((node,result))
# val = sorted(val,key =lambda x:x[1],reverse = True)
# # np.savetxt('best_cascade_node.txt',np.array(val))
# np.savetxt('best_cascade_node_power.txt',np.array(val))