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lp.py
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lp.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Oct 21 17:00:14 2016
for Link Prediction
@author: CNNVD
"""
import networkx as nx
import numpy as np
import math
import random
import datetime
from sim2 import pair
from sim2 import similarities
'''
Link Prediction
@param graph_file 网络文件
@param out_file 输出文件,文件格式,已打开
@param sim_method 计算相似性的方法
@param t 独立运行的实验的次数
@param p 测试集的比例
'''
def LP(graph_file, out_file, sim_method, t, p):
G = nx.read_edgelist(graph_file, nodetype=int)
#G = G.to_undirected()
#G = nx.convert_node_labels_to_integers(G)
# for debug
# print(nx.nodes(G))
node_num = nx.number_of_nodes(G)
edge_num = nx.number_of_edges(G)
# 列出所有不存在的链接,存放到non_edge_list中
# non_edge_num = (node_num * (node_num - 1)) / 2 - edge_num
non_edge_list = [pair(u, v) for u, v in nx.non_edges(G)]
non_edge_num = len(non_edge_list)
# for debug
print("V: %d\tE: %d\tNon: %d" % (node_num, edge_num, non_edge_num))
# for debug
# print(len(non_edge_list))
# print(non_edge_list)
# 执行t次独立的实验,每次从G中选择p*100%的链接作为测试集,剩余的链接作为训练集
test_num = int(edge_num * p)
pre_num = 0
for l in range(2, 101, 2):
if l < 20:
pre_num += 1
else:
break
# end if
# end for
pre_num += 1
# for debug
print('test_edge_num: %d' % test_num)
# 定义数组存放性能值
auc_list = []
rs_list = []
time_list = []
pre_matrix = [[0 for it in range(t)] for num in range(pre_num)]
# 迭代t次进行测试
for it in range(t):
if it % 10 == 0:
print('turn: %d' % it)
# end if
# 首先产生一批随机数
seed = math.sqrt(edge_num * node_num) + math.pow((1 + it) * 10, 3) # 随机数种子
random.seed(seed)
rand_set = set(random.sample(range(edge_num), test_num))
# rand_set = set()
# i = 0
# while (i < test_num):
# r = random.randint(0, edge_num - 1)
# if (r not in rand_set):
# rand_set.add(r)
# i += 1
# # end if
# # end while
# for debug
# print(rand_set)
# print(len(rand_set))
# 遍历G中链接,根据rand_set中的值分成训练集和测试集
training_graph = nx.Graph()
training_graph.add_nodes_from(range(node_num))
test_edge_list = []
r = 0
for u, v in nx.edges_iter(G):
u, v = pair(u, v)
# for debug
# print(u, v)
if r in rand_set: # 测试链接
test_edge_list.append((u, v))
else:
training_graph.add_edge(u, v) # 训练网络
# end if
r += 1
# end for
training_graph.to_undirected()
# for debug
# print(len(test_edge_list))
# print(test_edge_list)
# print(nx.number_of_edges(training_graph))
# print(nx.number_of_nodes(training_graph))
# print(nx.nodes(training_graph))
# print(nx.edges(training_graph))
# 计算相似度
# if (it % 10 == 0):
# print('计算相似度')
start = datetime.datetime.now()
sim_dict = similarities(training_graph, sim_method)
end = datetime.datetime.now()
# 0. 计算时间
time_list.append((end - start).microseconds)
# 1. 计算AUC
auc_value = AUC(sim_dict, test_edge_list, non_edge_list)
auc_list.append(auc_value)
# for debug
# print(auc_value)
# 创建一个数组,存放顶点对的相似度
sim_list = [((u, v), s) for (u, v), s in sim_dict.items()]
# sim_dict不在需要
sim_dict.clear()
# 对sim_list按照相似度降序排列
sim_list.sort(key=lambda x: (x[1], x[0]), reverse=True)
# 2. 计算Ranking Score
rank_score = Ranking_score(sim_list, test_edge_list, non_edge_num)
rs_list.append(rank_score)
# for debug
# print(rank_score)
# 3. 计算精度列表
pre_list = Precision(sim_list, test_edge_list, test_num)
for num in range(pre_num):
pre_matrix[num][it] = pre_list[num]
# end for
# end for
# 计算平均值和方差,并将结果输出到文件
auc_avg, auc_std = stats(auc_list)
print('AUC: %.4f(%.4f)' % (auc_avg, auc_std))
out_file.write('%.4f(%.4f)\t' % (auc_avg, auc_std))
rs_avg, rs_std = stats(rs_list)
print('Ranking_Score: %.4f(%.4f)' % (rs_avg, rs_std))
out_file.write('%.4f(%.4f)\t' % (rs_avg, rs_std))
time_avg, time_std = stats(time_list)
print('Time: %.4f(%.4f)' % (time_avg, time_std))
out_file.write('%.4f(%.4f)\t' % (time_avg, time_std))
pre_avg_list = []
pre_std_list = []
for num in range(pre_num):
pre_avg, pre_std = stats(pre_matrix[num])
pre_avg_list.append(pre_avg)
pre_std_list.append(pre_std)
# end for
print('Precision: ')
# out_file.write('\nPrecision: ')
for num in range(pre_num):
print('%.4f(%.4f)\t' % (pre_avg_list[num], pre_std_list[num]))
out_file.write('%.4f(%.4f)\t' % (pre_avg_list[num], pre_std_list[num]))
# end for
out_file.write('%d\n' % test_num)
# end def
# 输入列表,计算平均值和方差
def stats(value_list):
value_array = np.array(value_list)
avg = np.mean(value_array)
std = np.std(value_array)
return avg, std
# end def
###############################################################################
"""
精度计算的函数
"""
# @param sim_dict 存放顶点对相似度的字典
# @param node_num 顶点个数
# @param missing_edge_list 测试集,丢失的链接 $E^p$
# @param non_edge_list 不存在的链接 $U - E$
def AUC(sim_dict, missing_edge_list, non_edge_list):
if len(missing_edge_list) * len(non_edge_list) <= 10000:
return auc1(sim_dict, missing_edge_list, non_edge_list)
else:
return auc2(sim_dict, missing_edge_list, non_edge_list)
# end if
# end AUC
###############################################################################
# 计算ACU值,该方法中将测试集中的边与不存在的边进行两两比较
# @param sim_dict 存放顶点对的相似度,字典
# @param missing_edge_list 测试集,丢失的链接
# @param non_edge_list 不存在的链接
# @return auc值
def auc1(sim_dict, missing_edge_list, non_edge_list):
n1 = 0
n2 = 0
for (u, v) in missing_edge_list:
try:
m_s = int(sim_dict[(u, v)] * 1000000)
except KeyError:
m_s = 0
# end try
for (x, y) in non_edge_list:
try:
n_s = int(sim_dict[(x, y)] * 1000000)
except KeyError:
n_s = 0
# end try
if m_s > n_s:
n1 += 1
elif m_s == n_s:
n2 += 1
# end if
# end for
# end for
n = len(missing_edge_list) * len(non_edge_list)
return (n1 + 0.5 * n2) / n
# end def
# 计算ACU值,该方法进行10000次比较
def auc2(sim_dict, missing_edge_list, non_edge_list):
n = 10000
n1 = 0
n2 = 0
m_num = len(missing_edge_list)
n_num = len(non_edge_list)
for i in range(n):
r1 = random.randint(0, m_num - 1)
r2 = random.randint(0, n_num - 1)
(u, v) = missing_edge_list[r1]
(x, y) = non_edge_list[r2]
try:
m_s = int(sim_dict[(u, v)] * 1000000)
except KeyError:
m_s = 0
# end try
try:
n_s = int(sim_dict[(x, y)] * 1000000)
except KeyError:
n_s = 0
# end try
if m_s > n_s:
n1 += 1
elif m_s == n_s:
n2 += 1
# end if
# end for
return (n1 + 0.5 * n2) / n
# end def
###############################################################################
#计算Precision
def Precision(sim_list, missing_edge_list, missing_edge_num):
# 计算不同的l
# l_list =[]
# for l in range(10, 101, 10):
# if l < missing_edge_num:
# l_list.append(l)
# else:
# break
# # end if
# # end for
# l_list.append(missing_edge_num)
# 将missing_edge_list转换成set
missing_edge_set = set(missing_edge_list)
pre_list = []
count = 0
ll = len(sim_list)
for l in range(200):
if l < ll:
(u, v) = sim_list[l][0]
if (u, v) in missing_edge_set:
# (u, v) 是一条丢失的边
count += 1
# end if
# end if
if (l + 1) % 1 == 0 and l < 21: # 输出top-(l+1)
pre_list.append(count / (l + 1))
# end if
# end for
pre_list.append(count / missing_edge_num)
# for l in l_list:
# count = 0
# for i in range(l):
# (u, v) = sim_list[i][0]
# if (u, v) in missing_edge_set:
# # (u, v) 是一条丢失的边
# count += 1
# # end if
# # end for
# pre_list.append(count / l)
# # end for
return pre_list
# end def
###############################################################################
def Ranking_score(sim_list, missing_edge_list, non_edge_num):
"""
@article{dai2016link,
author="Caiyan, Dai and Chen, Ling and Li, Bin",
title="Link prediction in complex network based on modularity",
journal="Soft Computing",
year="2016",
pages="1--18",
issn="1433-7479",
doi="10.1007/s00500-016-2030-4",
url="http://dx.doi.org/10.1007/s00500-016-2030-4"
}
@article{chen2014link,
title={A link prediction algorithm based on ant colony optimization},
author={Chen, Bolun and Chen, Ling},
journal={Applied Intelligence},
volume={41},
number={3},
pages={694-708},
year={2014},
}
"""
missing_edge_num = len(missing_edge_list)
H = missing_edge_num + non_edge_num
# 定义rank_dict,存放预测的每条链接的rank
rank_dict = {}
# 变量sim_list,得到rank值
for r in range(len(sim_list)):
(u, v) = sim_list[r][0]
rank_dict[(u, v)] = r + 1
# end for
rr = H - 1
sum_rank = 0
for (u, v) in missing_edge_list:
try:
rank = rank_dict[(u, v)]
except KeyError:
rank = rr # 没有相似度的边
# end try
sum_rank += rank
# end for
return sum_rank / (missing_edge_num * H)
# end ranking_score