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imm_create_train_datasets_one_only.py
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import os
import random
import evaluate
import json
from networkx.readwrite import json_graph
import time
import sys
import pickle
model = sys.argv[1]
base_path = sys.argv[2]
train_or_test = sys.argv[3]
num_k=10#500#
graph_path = base_path + '/' + model + '/' + train_or_test + '/'
print(graph_path)
G_data = json.load(open(graph_path + "large_graph" + "-G.json"))
G = json_graph.node_link_graph(G_data)
total_nodes = len(G.nodes())
epsilon = 0.5
#total_nodes = 1079712
print(" total nodes ", total_nodes)
#epsilon = 0.5
print(os.getcwd())
print(graph_path)
os.chdir("./imm_baseline/im_benchmarking-master/sidm029_im_benchmark/Codes/IMM")
#total_nodes = len(G.nodes())
#print("total nodes ", total_nodes)
dataset_path = "../../../../../" + graph_path +"sorted_edges.txt"
print(dataset_path)
num_of_iterations_imm =1#30
print(os.getcwd())
class_map_file = "../../../../../" + graph_path + "large_graph" +"-class_map.json"
class_map = {}
f2 = open(class_map_file, 'w')
#for num_k in [5,10,15,20,25,30,50,100,200]:
for num_k in [10,20,50,100,150,200]:
int_selected_nodes=[]
gain_dict={}
total_gain = 0
print(" GENERATING IMM REG ")
for iter in range(0, num_of_iterations_imm):
print(" imm iteratin #", iter)
seed_random = random.randint(1, 100000000)
out_file_path = "../../../../../" + graph_path +'/multi_iter/'+ "large_graph_ic_imm_sol_eps" + str(epsilon) + "_num_k_" + str(
num_k) +"_iter_"+str(iter)
print(out_file_path)
result_time_file_name = "../../../../../" + graph_path + '/multi_iter/'+ "large_graph_ic_imm_time_eps" + str(
epsilon) + "_num_k_" + str(num_k) +"_iter_"+str(iter) + ".txt"
print(result_time_file_name)
solution_file_name = out_file_path + ".txt"
print(solution_file_name)
#
command = "time ./imm_discrete -dataset " + dataset_path + " -k " + str(
num_k) + " -model IC -epsilon " + str(epsilon)+" -output " + out_file_path + " -seed_random " + str(seed_random)+ " -training_for_gain 1"
print("command imm ", command)
os.system(command)
solution_file_name = out_file_path + ".txt"
file_node_gain_name = out_file_path + "_dict_node_gain.txt"
file_node_gain = open(file_node_gain_name,'r')
# solution_file = open(solution_file_name, "r")
#
# # optimal_nodes = solution_file.readlines()
# #print("optimal nodes ", optimal_nodes)
# #print("len optimal nodes ", len(optimal_nodes))
# pass
# node_gain_list_tuples = file_node_gain.readlines()
# for line in node_gain_list_tuples:
# (node,gain ) = line.split()
# gain= int(gain)
# node = int(node)
# total_gain+= int(gain)
# if( node not in gain_dict):
# gain_dict[node] = 0
# gain_dict[node] += gain
#
# print(node, gain)
#
#
#
#
# for node in range(0, total_nodes):
# node = int(node)
# # if node in int_selected_nodes:
# if node not in gain_dict:
# class_map[str(node)] = [0]
# else:
# class_map[str(node)] = [gain_dict[node]*1.0/(total_gain*1.0)] # [1,0]
#
# classdata = json.dumps(class_map)
# f2.write(classdata)
# f2.close()
# print(class_map['283812'])
# #
# # print(class_map['18718'])
# print(dict)
# for node in optimal_nodes:
# int_node = int(node)
# if int_node not in dict_togetherness:
# dict_togetherness[int_node] ={}
#
# for node_dict in optimal_nodes:
# int_node_dict = int(node_dict)
#
# if(int_node_dict == int_node):
# continue
#
# if int_node_dict not in dict_togetherness[int_node]:
# dict_togetherness[int_node][int_node_dict]=0
#
#
# dict_togetherness[int_node][int_node_dict]+=1
# print(dict_togetherness)
#
print("len dict", len(gain_dict))
#
# for i in range(0, num_k):
# int_selected_nodes.append(int(optimal_nodes[i]))
# if int(optimal_nodes[i]) not in appeared_count:
# appeared_count[int(optimal_nodes[i])] = 0
#
# appeared_count[int(optimal_nodes[i])] += 1
#
# if appeared_count[int(optimal_nodes[i])] > max_appear_count:
# max_appear_count = appeared_count[int(optimal_nodes[i])]
#
# # print( appeared_count)
#
# appeared_count_normalized = {}
# for node_id, node_freq in appeared_count.items():
# appeared_count_normalized[node_id] = node_freq / (max_appear_count * 1.0)
# # print( appeared_count_normalized)
#
# os.chdir("../../../../../")
#
# # print(appeared_count_normalized)
# print("Writing to class map ")
# print(appeared_count_normalized)
#
# for node in range(0, total_nodes):
# # if node in int_selected_nodes:
# if node not in appeared_count_normalized:
# class_map[str(node)] = [0]
# else:
# class_map[str(node)] = [appeared_count_normalized[node]] # [1,0]
#
#
#
#
# # else:
# ## 3 class_map[str(node)] = # [0,1]
# # print(class_map)
#
# # num_mc_simulation = 100
#
# # spread = 0
# print(" loading graph")
# G_data = json.load(open("GraphSAGE-master/real_data/large_youtube/large_graph" + "-G.json"))
# G = json_graph.node_link_graph(G_data)
# print(" running mc simulation")
# for i in range(0, num_mc_simulation):
# UG_Copy = G.copy()
# temp_spread = evaluate.evaluate(UG_Copy, int_selected_nodes)
# print(" iter {} spread {}".format(i, temp_spread))
# spread = spread + temp_spread
# spread = spread * 1.0 / num_mc_simulation
# print('Spread = ', spread)
# reward_file_name = "GraphSAGE-master/real_data/large_youtube/large_graph"+"_reward_imm_eps"+ str(epsilon)
# reward_file = open(reward_file_name, 'w')
# reward_file.write(str(spread))
# reward_file.close()