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QueryDesigned.py
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QueryDesigned.py
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# -*- coding:utf-8 -*-
"""
@author:SamanthaChen
@file:QueryDesigned.py
@time:2017/3/222:08
@Function:根据文件,生成相应的查询文件
"""
import networkx as nx
from collections import defaultdict
import random
def selectQueryLimitedW(G,dataName,outputFile):
'筛选查询节点和查询属性'
'1. 实验的一些条件'
qNumbers=[2,4,8,16,32] ##查询的节点数目
cNumbers=[1,2,4,8,16,32] ##查询的coreness大小
samples=30 ###
# samplePerCore=samples/len(cNumbers) ##每个core执行的次数
###创建一个二维数组
sampledNodes=[['' for j in range(len(qNumbers))] for i in range(len(cNumbers))]
attrs=[['' for j in range(len(qNumbers))] for i in range(len(cNumbers))] ##属性
'2. 从最大连通分量里面选'
Gc=max(nx.connected_component_subgraphs(G),key=len)
'3. 计算这个连通分量的k-core分解'
coreIndex=nx.core_number(Gc)
'4. 将节点按照core值分组'
#将节点按照core number进行分组
Vk=defaultdict(list)
for key,value in coreIndex.items():
Vk[value].append(key)
# tmpVk=sorted(Vk.iteritems(), key=lambda d:d[0],reverse=False)#Vk按照core值从大到小排序
mxCore=max(Vk.keys()) ##这个图的最大core
'5. 按照core值和query数量生成查询节点集合'
for i in range(len(cNumbers)):
curCore=cNumbers[i] ##当前的core值
if curCore>mxCore: ###当前的core达不到要求啊,就不往下找了
break
'5.1:获取core>curCore的节点'
coreNodes=[]
for key in Vk.keys():
if(key>=curCore):
coreNodes.extend(Vk[key])
'5.2:从候选节点里面获得最大连通分量'
subGraph=Gc.subgraph(coreNodes)
giantCC=max(nx.connected_component_subgraphs(subGraph),key=len)
N=nx.number_of_nodes(giantCC)
for j in range(len(qNumbers)):
qn=qNumbers[j] ##查询节点数目
if nx.number_of_nodes(giantCC)>=qn:
sample=randomSelectNode(giantCC,qn) ###随机选qn个节点
s1='\t'.join([str(val) for val in sample])
sampledNodes[i][j]=s1##列表变成string
attr=selectFrequentAttrs(giantCC,sample) ###选择几个出现频率最高的属性
attrs[i][j]='\t'.join([val for val in attr])
'6. 输出查询节点'
f=open(outputFile,'w')
for i in range(len(cNumbers)):
core=cNumbers[i]
for j in range(len(qNumbers)):
qn=qNumbers[j]
string='core:\t'+str(core)+'\tqn:\t'+str(qn)+'\tnode:\t'+sampledNodes[i][j]+'\tattrs:\t'+attrs[i][j]
f.write(string+'\n')
f.close()
def selectQueryAllW(G,dataName,outputFile):
'筛选查询节点和查询属性'
'1. 实验的一些条件'
qNumbers=[2,4,8,16,32] ##查询的节点数目
cNumbers=[1,2,4,8,16,32] ##查询的coreness大小
samples=30 ###
# samplePerCore=samples/len(cNumbers) ##每个core执行的次数
###创建一个二维数组
sampledNodes=[['' for j in range(len(qNumbers))] for i in range(len(cNumbers))]
attrs=[['' for j in range(len(qNumbers))] for i in range(len(cNumbers))] ##属性
'2. 从最大连通分量里面选'
Gc=max(nx.connected_component_subgraphs(G),key=len)
'3. 计算这个连通分量的k-core分解'
coreIndex=nx.core_number(Gc)
'4. 将节点按照core值分组'
#将节点按照core number进行分组
Vk=defaultdict(list)
for key,value in coreIndex.items():
Vk[value].append(key)
# tmpVk=sorted(Vk.iteritems(), key=lambda d:d[0],reverse=False)#Vk按照core值从大到小排序
mxCore=max(Vk.keys()) ##这个图的最大core
'5. 按照core值和query数量生成查询节点集合'
for i in range(len(cNumbers)):
curCore=cNumbers[i] ##当前的core值
if curCore>mxCore: ###当前的core达不到要求啊,就不往下找了
break
'5.1:获取core>curCore的节点'
coreNodes=[]
for key in Vk.keys():
if(key>=curCore):
coreNodes.extend(Vk[key])
'5.2:从候选节点里面获得最大连通分量'
subGraph=Gc.subgraph(coreNodes)
giantCC=max(nx.connected_component_subgraphs(subGraph),key=len)
N=nx.number_of_nodes(giantCC)
for j in range(len(qNumbers)):
qn=qNumbers[j] ##查询节点数目
if nx.number_of_nodes(giantCC)>=qn:
sample=randomSelectNode(giantCC,qn) ###随机选qn个节点
s1='\t'.join([str(val) for val in sample])
sampledNodes[i][j]=s1##列表变成string
attr=selectAllAttrs(giantCC,sample) ###选择几个出现频率最高的属性
attrs[i][j]='\t'.join([val for val in attr])
'6. 输出查询节点'
f=open(outputFile,'w')
for i in range(len(cNumbers)):
core=cNumbers[i]
for j in range(len(qNumbers)):
qn=qNumbers[j]
string='core:\t'+str(core)+'\tqn:\t'+str(qn)+'\tnode:\t'+sampledNodes[i][j]+'\tattrs:\t'+attrs[i][j]
f.write(string+'\n')
f.close()
def randomSelectNode(giantCC,qn):
res=set() ###节点集合
attrs=[] ###属性集合
nodeList=giantCC.nodes()
N=nx.number_of_nodes(giantCC)
while(len(res)<qn):
rnd=random.randint(0,N-1)###注意这个随机数包括区间的两个范围
x=nodeList[rnd]
res.add(x)
return res
def selectFrequentAttrs(giantCC,sample):
'这里选的属性默认是6个'
words={}
res=[]
for node in sample:
for attr in giantCC.node[node]['attr']:
if attr not in words:
words[attr]=0
words[attr]+=1
###选择6个出现频率最高的
sortedWords= sorted(words.iteritems(), key=lambda d:d[1], reverse = True)
count=0
for tuple in sortedWords:
if count>=12: #####这里设置选几个属性
break
res.append(tuple[0])
count+=1
return res
def selectAllAttrs(giantCC,sample):
'选择所有查询节点的属性合集'
allSet=set()
for node in sample:
if giantCC.node[node].has_key('attr'):
for attr in giantCC.node[node]['attr']:
allSet.add(attr)
return list(allSet)
def dataReader2(edgefile,attrfile):
'读的是delicious类型的数据'
G=nx.read_edgelist(edgefile,nodetype=int) #从邻接表读取图数据
#读取节点的属性文件(一行只有一个节点一个属性)
f=open(attrfile)
for line in f.readlines():
words=line.split() ##第一个是id,后面跟着的都是属性
id=int(words[0])
attr=words[1]
if G.has_node(id):
if G.node[id].has_key('attr'):
G.node[id]['attr'].append(attr)
else:
G.node[id]['attr']=[] #新加一个列表
return G
def dataReader(adjlistFile,attrFile):
G=nx.read_adjlist(adjlistFile,nodetype=int)
###读取属性文件,一行是一个节点和所有的属性
f=open(attrFile)
for line in f.readlines():
words=line.split()
id=int(words[0])
attrs=words[1:]
if G.has_node(id):##还得先判断有没有这个节点
G.node[id]['attr']=attrs
return G
def RunRandomSelectQuery():
dataName='wisconsin'
edgefile='L:/ACQData/inputfile/'+dataName+'_graph'
labelfile='L:/ACQData/inputfile/'+dataName+'_nodelabel'
outputFile='L:/ACQData/'+dataName+'_Query_wall.txt';
G=dataReader(edgefile,labelfile)
print 'readed graph...'
selectQueryAllW(G,dataName,outputFile)
def selectFromGroudTruthData():
'从groud-truth data里面筛选查询节点,和查询属性'
path='L:/ACQData/groundTruthData/'
data='citeseer'
dataName=data+'/'+data
# edgeFile=open(path+dataName+' _graph','r')
classFile=open(path+dataName+'_class','r')
labelFile=open(path+dataName+'_nodelabel','r')
queryFile=open(path+dataName+'_query_w2_1','w')
wordNum=2 ###指定的筛选属性的个数
'读社团分组'
communityGroup = defaultdict(list) ##社团分组
for line in classFile.readlines():
line=line.strip()
words=line.split()
communityGroup[words[1]].append(int(words[0]))
classFile.close()
'读节点标签'
labelDict={}
for line in labelFile.readlines():
line=line.strip()
words=line.split()
labelDict[int(words[0])]=words[1:] ##属性还是str格式
labelFile.close()
'统计社团中属性出现频率'
comWordFrequents={}
comWordFrequents=comWordFrequents.fromkeys(communityGroup.keys(),{})##初始化
comWordNodeGroup={}
comWordNodeGroup=comWordNodeGroup.fromkeys(communityGroup.keys(),{})
for className,nodes in communityGroup.items():
wordFre={}
wordNode=defaultdict(list)
###对于每个社团,统计一下词频
for node in nodes:
for label in labelDict[node]:
if wordFre.has_key(label):
wordFre[label]+=1
else:
wordFre[label]=1
wordNode[label].append(node)
####
comWordFrequents[className]=wordFre
comWordNodeGroup[className]=wordNode
'在同一个社团中,出现频率最大的k个关键词的查询组,随机选择[1,2,4,8,16]个查询节点'
for className,wordNodeGroup in comWordNodeGroup.items():
attrFreDict=comWordFrequents[className]
attrNodeDict=comWordNodeGroup[className]
'选择前两个属性'
selectAttrs=[]
tmp=sorted(attrFreDict.items(),key=lambda d:d[1],reverse=True)[0:wordNum] ##选择前wordNum个
for tuple in tmp:
selectAttrs.append(tuple[0]) ##只选择属性
'在包含这些属性的节点里面选节点'
nodeSet=set()
for label in selectAttrs:
for node in attrNodeDict[label]:
nodeSet.add(node)
'随机选节点'
nodeList=list(nodeSet)
countList=[1,2,4,8,16]
for count in countList:
selectNodeSet = set()
while len(selectNodeSet)<count and len(nodeList)>=count:
randIndex=random.randint(0,len(nodeList)-1)
selectNodeSet.add(nodeList[randIndex])
'选好节点,输出文件'
string='qn:'+'\t'+str(count)+'\tnode:'
for n in selectNodeSet:
string+='\t'+str(n)
string+='\t'+'attr:'
for a in selectAttrs:
string+='\t'+a
queryFile.write(string+'\n')
def analyzeGroundTruthData():
'分析一下数据'
'从groud-truth data里面筛选查询节点,和查询属性'
path = 'L:/ACQData/groundTruthData/'
data = 'citeseer'
dataName = data + '/' + data
# edgeFile=open(path+dataName+' _graph','r')
classFile = open(path + dataName + '_class', 'r')
labelFile = open(path + dataName + '_nodelabel', 'r')
analyzedFile = open(path + dataName + '_analyzeKeyWord', 'w')
wordNum = 10 ###指定的筛选属性的个数
'读社团分组'
communityGroup = defaultdict(list) ##社团分组
for line in classFile.readlines():
line = line.strip()
words = line.split()
communityGroup[words[1]].append(int(words[0]))
classFile.close()
'读节点标签'
labelDict = {}
for line in labelFile.readlines():
line = line.strip()
words = line.split()
labelDict[int(words[0])] = words[1:] ##属性还是str格式
labelFile.close()
'统计社团中属性出现频率'
comWordFrequents = {} ##{社团:{关键词:频率}}
comWordFrequents = comWordFrequents.fromkeys(communityGroup.keys(), {}) ##初始化
comWordNodeGroup = {} ##{社团:{关键词:包含的社团中的节点集}}
comWordNodeGroup = comWordNodeGroup.fromkeys(communityGroup.keys(), {})
for className, nodes in communityGroup.items():
wordFre = {}
wordNode = defaultdict(list)
###对于每个社团,统计一下词频
for node in nodes:
for label in labelDict[node]:
if wordFre.has_key(label):
wordFre[label] += 1
else:
wordFre[label] = 1
wordNode[label].append(node)
####
comWordFrequents[className] = wordFre
comWordNodeGroup[className] = wordNode
'输出文件'
for className in comWordFrequents.keys():
attrFreDict = comWordFrequents[className]
attrNodeDict = comWordNodeGroup[className]
'选择前wordnum个属性'
selectAttrs = []
tmp = sorted(attrFreDict.items(), key=lambda d: d[1], reverse=True)[0:wordNum] ##选择前wordNum个
for tuple in tmp:
selectAttrs.append(tuple[0]) ##只选择属性
'输出分析结果'
outstr='class: '+className+'\n'
printStr='class: '+className+'\tkeywords:'
for a in selectAttrs:
outstr+='keyword: '+str(a)+'\tFrequent: '+str(attrFreDict[a])+'\tNodes: '
printStr+=str(a)+' '
print printStr
# for n in attrNodeDict[a]:
# outstr+=str(n)+', '
# outstr+='\n'
# analyzedFile.write(outstr+'\n')
# analyzedFile.close()
def selectFromGroudTruthDataFrom2Nei():
'从groud-truth data里面筛选查询节点,和查询属性'
path='L:/ACQData/groundTruthData/'
data='washington'
dataName=data+'/'+data
edgePath=path+dataName+'_graph'
classFile=open(path+dataName+'_class','r')
labelFile=open(path+dataName+'_nodelabel','r')
queryTimes = 100
queryFile=open(path+dataName+'_query_2Nei_w3_'+str(queryTimes),'w')
wordNum=3 ###指定的筛选属性的个数
'读图'
G=nx.read_adjlist(edgePath,nodetype=int)
##获取度
degreeDict=nx.degree(G)
'读社团分组'
communityGroup = defaultdict(list) ##社团分组
for line in classFile.readlines():
line=line.strip()
words=line.split()
communityGroup[words[1]].append(int(words[0]))
classFile.close()
'读节点标签'
labelDict={}
for line in labelFile.readlines():
line=line.strip()
words=line.split()
labelDict[int(words[0])]=words[1:] ##属性还是str格式
labelFile.close()
'统计社团中属性出现频率'
comWordFrequents={}
comWordFrequents=comWordFrequents.fromkeys(communityGroup.keys(),{})##初始化
comWordNodeGroup={}
comWordNodeGroup=comWordNodeGroup.fromkeys(communityGroup.keys(),{})
for className,nodes in communityGroup.items():
wordFre={}
wordNode=defaultdict(list)
###对于每个社团,统计一下词频
for node in nodes:
for label in labelDict[node]:
if wordFre.has_key(label):
wordFre[label]+=1
else:
wordFre[label]=1
wordNode[label].append(node)
####
comWordFrequents[className]=wordFre
comWordNodeGroup[className]=wordNode
'在同一个社团中,出现频率最大的k个关键词的查询组,随机选择[1,2,4,8,16]个查询节点'
for className,wordNodeGroup in comWordNodeGroup.items():
attrFreDict=comWordFrequents[className]
attrNodeDict=comWordNodeGroup[className] ###这个社团里面的
'选择前两个属性'
selectAttrs=[]
tmp=sorted(attrFreDict.items(),key=lambda d:d[1],reverse=True)[0:wordNum] ##选择前wordNum个
for tuple in tmp:
selectAttrs.append(tuple[0]) ##只选择属性
'在包含这些属性的节点里面选节点'
nodeSet=set()
for label in selectAttrs:
for node in attrNodeDict[label]:
nodeSet.add(node)
'选择社团里面度最大的节点以及他的邻居'
maxDNode=nodeSet.pop()
maxD=degreeDict[maxDNode]
for node in nodeSet:
if degreeDict[node]>maxD:
maxDNode=node
maxD=degreeDict[node]
'随机选度最大的节点的邻居,一个社团生成十次查询文件'
for i in range(queryTimes):
count=random.randint(1,maxD-1)
'在度最大的节点的2度邻居里面选'
oneHopNeis=nx.neighbors(G,maxDNode)
twoHopneis=[]
for n in oneHopNeis:
twoHopneis.extend(nx.neighbors(G,n)) ##2度邻居
'节点筛选范围是nodeList'
nodeList=[]
nodeList.extend(oneHopNeis)
nodeList.extend(twoHopneis)
selectNodeSet = set() ##最终入选的节点集
while len(selectNodeSet) < count and len(nodeList) >= count:
randIndex = random.randint(0, len(nodeList) - 1)
selectNodeSet.add(nodeList[randIndex])
'选好节点,输出文件'
string = 'qn:' + '\t' + str(count) + '\tnode:'
for n in selectNodeSet:
string += '\t' + str(n)
string += '\t' + 'attr:'
for a in selectAttrs:
string += '\t' + a
queryFile.write(string + '\n')
if __name__=="__main__":
# selectFromGroudTruthData()
# RunRandomSelectQuery()
# analyzeGroundTruthData()
selectFromGroudTruthDataFrom2Nei()