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evaluate_spread.py
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import networkx as nx
import random
from functools import partial
import time
import json
import multiprocessing as mp
from networkx.readwrite import json_graph
def calculate_spread(sampled_G, seed_nodes,new_node):
beg_time = time.time()
if len(seed_nodes)==0:
return 0
for node in seed_nodes:
sampled_G.add_edge(new_node,node,weight=1.0)
total_nodes = len(nx.descendants(sampled_G,new_node))
sampled_G.remove_node(new_node)
end_time = time.time()
print("spread time ", end_time- beg_time)
return total_nodes
def evaluate(sampled_G, num_nodes, selected_nodes):
# num_nodes = main_graph.number_of_nodes()
new_node_to_be_added = num_nodes + 1
spread = calculate_spread(sampled_G, selected_nodes,new_node_to_be_added)
print(" graph spread ", spread)
#del main_graph
return spread
#
def mp_pool_format(mc_sim_graphs_dir, num_nodes , selected_nodes, mc_id ):
print("selected_nodes", selected_nodes)
sampled_graph_json_path = mc_sim_graphs_dir + str(mc_id) +"-G.json"
print("sampled_graph_json_path ", sampled_graph_json_path )
sampled_G_data = json.load(open(sampled_graph_json_path))
sampled_G = json_graph.node_link_graph(sampled_G_data)
num_nodes=999999999999
print("loaded",num_nodes)
spread = evaluate(sampled_G, num_nodes, selected_nodes)
print("SPREAD VAL", spread, sampled_graph_json_path)
return spread
def evaluate_helper_mp(graph_dir, main_graph, selected_nodes, num_mc_sim):
mc_sim_graphs_dir = graph_dir+'/mc_sim_graphs/'
print(" mc_sim_graphs dir ", mc_sim_graphs_dir )
result_list = []
mc_sim = [x for x in range(0, num_mc_sim)]
print(mc_sim)
pool = mp.Pool(processes=12)
pool_args = partial(mp_pool_format,mc_sim_graphs_dir, main_graph, selected_nodes)
print(pool_args)
for iter, res in enumerate(pool.map(pool_args,mc_sim, chunksize=25
)):
print("iter, res", iter, res)
result_list.append(res)
avg_spread = round(sum(result_list) * 1.0 / len(result_list) * 1.0, 4)
print("len result list", len(result_list))
print("Avg_spread", avg_spread)
print("final results")
avg_spread = round(sum(result_list) * 1.0 / len(result_list) * 1.0, 4)
print("len result list", len(result_list))
print("Avg_spread", avg_spread)
return avg_spread
def evaluate_helper_without_mp(graph_dir, main_graph, selected_nodes, num_mc_sim):
mc_sim_graphs_dir = graph_dir+'/mc_sim_graphs/'
print(" mc_sim_graphs dir ", mc_sim_graphs_dir )
result_list = []
mc_sim = [x for x in range(0, num_mc_sim)]
num_nodes = 999999999999
for sim in mc_sim:
res = mp_pool_format(mc_sim_graphs_dir, num_nodes, selected_nodes, sim)
result_list.append(res)
avg_spread = round(sum(result_list) * 1.0 / len(result_list) * 1.0, 4)
print("len result list", len(result_list))
print("Avg_spread", avg_spread)
# for iter, res in enumerate(pool.map(pool_args,mc_sim, chunksize=1)):
# print("iter, res", iter, res)
# result_list.append(res)
# avg_spread = round(sum(result_list) * 1.0 / len(result_list) * 1.0, 4)
# print("len result list", len(result_list))
# print("Avg_spread", avg_spread)
print("final results")
avg_spread = round(sum(result_list) * 1.0 / len(result_list) * 1.0, 4)
print("len result list", len(result_list))
print("Avg_spread", avg_spread)
return avg_spread