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mainThreading3.py
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mainThreading3.py
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import CSVRead
import sparqlQuerypy
import Neo4jDrive
import random
from threading import Thread, Lock
import datetime
log=open("log.log",'a')
log.write("\n--------------------------------------\n")
log.write(str(datetime.datetime.now()))
log.write("\n")
hypothesisSet=set()
stype=[]
sample=5
data=[]
csvitems=[]
def main():
csvitems=[]
data=[]
tables=["StatesandCapitals.csv","RiversandSourceState.csv"]
size=[]
for nameOfFile in tables:
Neo4jDrive.insertNode(nameOfFile)
node=Neo4jDrive.findNodeByName(nameOfFile)
node.properties['type']='table'
node.push()
csvitems+=[CSVRead.readCSV(nameOfFile,firstRow=False, choice=[0,1])[1:]]
size+=[len(csvitems[-1])]
random.shuffle(csvitems[-1])
i=k=0
while len(csvitems)>0:
for l,item in enumerate(csvitems):
end=k+sample
s=sample
if k+sample>len(item):
s=sample-(end-len(item))
end=len(item)
data[i:i+s]=[[it,l] for it in item[k:end]]
i+=s
if k+sample>len(item):
csvitems.remove(item)
k+=sample
run(data,tables,size)
def run(data,tables,size):
support=[[]]
columnNames=[]
for i,nameOfFile in enumerate(tables):
columnNames+=[CSVRead.readCSV(nameOfFile,firstRow=True, choice=[0,1])]
columnNames[i]=[c.strip() for c in columnNames[i]]
for j,name in enumerate(columnNames[i]):
z=Neo4jDrive.insertNodeAndRelationship(nameOfFile,"Column",name)[0]
node=Neo4jDrive.findNodeByName(name)
node.properties['type']='Column'
node.push()
z.properties['type']="Column"
z.push()
support[i]+=[CSVRead.getSupport(nameOfFile,j)]
support+=[[]]
support=support[:-1]
totalNumberOfValues=CSVRead.getSize(nameOfFile,0)
hyplock=Lock()
stypelock=Lock()
for itemPiece in data:
indexOfFile=itemPiece[1]
item=itemPiece[0]
for column in range(len(columnNames[indexOfFile])):
#support=CSVRead.getSupport(nameOfFile,column)
#totalNumberOfValues=CSVRead.numberOfItems(support)
k=ccThread(item[column],columnNames[indexOfFile],column,support[indexOfFile],size[indexOfFile])
k.start()
k.join()
for itemPiece in data:
indexOfFile=itemPiece[1]
item=itemPiece[0]
for column in range(len(columnNames[indexOfFile])):
#support=CSVRead.getSupport(nameOfFile,column)
#totalNumberOfValues=CSVRead.numberOfItems(support)
for perm_column in range(len(columnNames[indexOfFile])):
if perm_column!=column:
k=dmsThread(item[column],item[perm_column],size[indexOfFile],columnNames[indexOfFile],column,perm_column)
k.start()
k.join()
allCC=set(Neo4jDrive.findAllCCNodes())
for s,c in enumerate(columnNames):
for column in c:
k=topDownThread(column,hyplock,stypelock,allCC,size[s])
k.start()
k.join()
class ccThread(Thread):
def __init__(self,item,columnNames,column,support,totalNumberOfValues):
Thread.__init__(self)
self.item=item
self.columnNames=columnNames
self.column=column
self.support=support
self.totalNumberOfValues=totalNumberOfValues
def run(self):
support=self.support
totalNumberOfValues=self.totalNumberOfValues*1.0
column=self.column
columnNames=self.columnNames
item=self.item
rlist=sparqlQuerypy.findBottomUp(item.strip())
print 'number of nodes for', item.strip(), " is ", len(rlist)
log.write('number of nodes for'+str( item.strip())+ " is "+ str(len(rlist))+'\n')
flag=0
for r in rlist:
rel_data=Neo4jDrive.insertNodeAndRelationship(columnNames[column],"cc",r[2])
rel_data=rel_data[0]
node=Neo4jDrive.findNodeByName(r[2])
if r[2]=='http://dbpedia.org/ontology/PopulatedPlace':
print columnNames[column], 'Happening'
print 'potato',rel_data
if rel_data.properties['incoming']==None: #find out why this is not happenings
rel_data.properties['incoming']=1
rel_data.properties['ccs']=1/totalNumberOfValues
rel_data.push()
#print 'tomato',rel_data
else:
if flag==0:
rel_data.properties['incoming']+=1
rel_data.push()
rel_data.properties['ccs']=node.properties['incoming']/totalNumberOfValues
flag=1
node.properties['type']='cc'
node.properties['ccs']=0
numberOfLinks=0
for link in Neo4jDrive.findIncomingCCLinks(r[2]):
node.properties['ccs']+=link[0].properties['ccs']
numberOfLinks+=1
if numberOfLinks>0: node.properties['ccs']/=numberOfLinks
node.push()
rel_data.properties['rel_class'] = 'cc'
#rel_data.properties['ccs']=node.proper/(totalNumberOfValues*1.0)
rel_data.push()
class dmsThread(Thread):
def __init__(self,label1,label2,size,columnNames,column,perm_column):
Thread.__init__(self)
self.label1=label1.strip()
self.label2=label2.strip()
self.size=size
self.columnNames=columnNames
self.column=column
self.perm_column=perm_column
def run(self):
rlist=sparqlQuerypy.findProperty2(self.label1,self.label2)
print '------------------'
log.write('----------------\n')
log.write(str(datetime.datetime.now())+'\n')
log.write(self.label1+self.label2)
print self.label1,self.label2#,rlist
cache=[]
propertyUsage=[1]
for r in rlist:
if u'd' in r.keys():
self.addProperty(r['p']['value'])
rel_data=Neo4jDrive.insertNodeAndRelationship(r['p']['value'],"domain",r['d']['value'])[0]
rel_data['name']='domain'
rel_data.push()
else:
ccClasses=Neo4jDrive.findCCNodes(self.columnNames[self.perm_column])
buildString="("
for i in ccClasses:
buildString+='<'+i+'>,'
buildString=buildString[:-1]
buildString+=")"
if r['p']['value'] not in cache:
propertyUsage=sparqlQuerypy.findPropertyClassesSecond(r['p']['value'],buildString)
cache+=[r['p']['value']]
print len(propertyUsage),r['p']['value']
if len(propertyUsage)<15000:
for item in (set([k['r']['value'] for k in propertyUsage]) & set(ccClasses)):
self.addProperty(r['p']['value'])
rel_data=Neo4jDrive.insertNodeAndRelationship(r['p']['value'],"domain",item)[0]
rel_data['name']="domain"
rel_data.push()
node=Neo4jDrive.findNodeByName(item)
node.properties['hyp']='yes'
node.properties['type']='cc'
node.push()
self.incrementDms(rel_data) #for each table we have to put a score on the link between the what and what? The property and its domain? But then how is the score calculated? Is it number of columns in the table by total in that table or is it completely unique?
def incrementDms(self,rel_data):
if rel_data.properties['DCSinc']==None:
rel_data.properties['DCSinc']=1
rel_data.properties['DCS']=1.0/self.size
else:
rel_data.properties['DCSinc']+=1
rel_data.properties['DCS']=node.properties['DCSinc']*1.0/self.size
rel_data.push()
def addProperty(self,p):
rel_data=Neo4jDrive.insertNodeAndRelationship(self.columnNames[self.column],"property",p)
hypothesisSet.add(p)
node=Neo4jDrive.findNodeByName(p)
if node.properties['dcsincoming']==None:
node.properties['dcsincoming']=1
node.properties['dcs']=1/(self.size*1.0)
else:
node.properties['dcsincoming']+=1
node.properties['dcs']=node.properties['dcsincoming']/(self.size*1.0)
node.properties['type']='property'
node.push()
rel=Neo4jDrive.insertRelationship(self.columnNames[self.column], p, self.columnNames[self.perm_column])[0]
if rel.properties['propCount']==None:
rel.properties['type']='property_rel'
rel.properties['name']=p
rel.properties['count']=1
rel.properties['dms']=rel.properties['count']/(self.size*1.0)
else:
rel.properties['count']+=1
rel.properties['dms']=rel.properties['count']/(self.size*1.0)
rel.push()
class topDownThread(Thread):
def __init__(self, item1, hyplock, stypelock, allCC,size):
Thread.__init__(self)
self.a=item1.strip()
self.hyplock=hyplock
self.stypelock=stypelock
self.allCC=allCC
self.size=size
def run(self):
count=0
objtypes=[]
rlist=sparqlQuerypy.findPropertyClassesFirst(self.a)
for r in rlist:
if u'r' not in r.keys():
ccClasses=Neo4jDrive.findCCNodes(self.a)
buildString="("
for i in ccClasses:
buildString+='<'+i+'>,'
buildString=buildString[:-1]
buildString+=")"
propertyUsage=sparqlQuerypy.findPropertyClassesSecond(r['p']['value'],buildString)
for item in (set([k['d']['value'] for k in propertyUsage]) & hypothesisSet):
#rel=Neo4jDrive.insertNodeAndRelationship(self.a ,'cp', r['p']['value'])
#self.hyplock.acquire()
#hypothesisSet.add(r['p']['value'])
#self.hyplock.release()
#temp=Neo4jDrive.findNodeByName(r['p']['value'])
#temp.properties['hyp']='yes'
#temp.push()
self.addProperty(r['p']['value'])
rel=Neo4jDrive.insertNodeAndRelationship(r['p']['value'], 'd', item)
for item in (set([k['d']['value'] for k in propertyUsage]) & set(self.allCC)):
#rel=Neo4jDrive.insertNodeAndRelationship(self.a, 'cp', r['p']['value'])
#self.hyplock.acquire()
#hypothesisSet.add(r['p']['value'])
#self.hyplock.release()
#temp=Neo4jDrive.findNodeByName(r['p']['value'])
#temp.properties['hyp']='yes'
#temp.push()
self.addProperty(r['p']['value'])
rel=Neo4jDrive.insertNodeAndRelationship(r['p']['value'], 'd', item)
def levenshtein(self,s1, s2):
if len(s1) < len(s2):
return levenshtein(s2, s1)
# len(s1) >= len(s2)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[j + 1] + 1 # j+1 instead of j since previous_row and current_row are one character longer
deletions = current_row[j] + 1 # than s2
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]
def addProperty(self,p):
print self.a, p
rel_data=Neo4jDrive.insertNodeAndRelationship(self.a,"cp",p)[0]
rel_data.properties['type']='cp'
self.hyplock.acquire()
hypothesisSet.add(p)
self.hyplock.release()
node=Neo4jDrive.findNodeByName(p)
if rel_data.properties['incoming']==None:
rel_data.properties['incoming']=1
rel_data.properties['dms']=1/(self.size*1.0)
pr=p
for j in range(len(pr)-1,0,-1):
if pr[j]=='/':
pr=pr[j+1:]
break
rel_data.properties['lms']=self.levenshtein(self.a,pr)
else:
rel_data.properties['incoming']+=1
rel_data.properties['dms']=node.properties['incoming']/(self.size*1.0)
rel_data.push()
node.properties['type']='property'
node.properties['hyp']='yes'
node.push()
if __name__=='__main__':
main()