def run(self):
support = self.support
totalNumberOfValues = self.totalNumberOfValues
column = self.column
columnNames = self.columnNames
item = self.item
rlist = sparqlQuerypy.findBottomUp(item)
for r in rlist:
rel_data = Neo4jDrive.insertNodeAndRelationship(
columnNames[column], "cc", r[2])
node = Neo4jDrive.findNodeByName(r[2])
if node.properties['incoming'] == None:
node.properties['incoming'] = 1
else:
node.properties['incoming'] += 1
node.properties['type'] = 'type'
node.push()
rel_data = rel_data[0]
rel_data.properties['rel_class'] = 'cc'
rel_data.properties['support'] = support[item] / (
totalNumberOfValues * 1.0)
rel_data.push()
def run(self):
support=self.support
totalNumberOfValues=self.totalNumberOfValues
column=self.column
columnNames=self.columnNames
item=self.item
node=Neo4jDrive.findNodeByName(item)
if node== None:
Neo4jDrive.insertNodeAndRelationship(columnNames[column],'dataItems',item)
node=Neo4jDrive.findNodeByName(item)
node.properties['fvalue']=support[item]
node.push()
rlist=sparqlQuerypy.findBottomUp(item)
for r in rlist:
try:
rel_data=Neo4jDrive.insertNodeAndRelationship(item,"cc",r[0])
rel_data1=Neo4jDrive.insertNodeAndRelationship(r[0],"dd",r[2])
node=node=Neo4jDrive.findNodeByName(r[2])
if node.properties['incoming']==None:
node.properties['incoming']=1
else:
node.properties['incoming']+=1
node.properties['type']='type'
node.push()
except :
print columnNames[column],'cc',r[0]
rel_data=rel_data[0]
rel_data.properties['rel_class'] = 'cc'
rel_data.properties['support']=support[item]/(totalNumberOfValues*1.0)
rel_data.push()
def ccScores(self):
data=self.data
columnNames=self.columnNames
totalSize=self.totalSize
relationships=self.relationships
size=len(data)
bitmap={}
for i,column in enumerate(columnNames):
relationships[column]={}
bitmap[column]={} #this is a dictionary which is a set of flags per data value remembering if the increment already happened.
for element in data:
item=element[i]
rlist=sparqlQuerypy.findBottomUp(item.strip())
print 'number of nodes for', item.strip(), " is ", len(rlist)
bitmap[column][item]={}
for r in rlist:
if r[0] not in bitmap[column][item].keys():
bitmap[column][item][r[0]]=0
if r[0] not in relationships[column].keys():
relationships[column][r[0]]={}
relationships[column][r[0]]['name']='cc'
if 'incoming' not in relationships[column][r[0]].keys():
relationships[column][r[0]]['incoming']=1
relationships[column][r[0]]['cc']=1.0/totalSize
else:
relationships[column][r[0]]['incoming']+=1
relationships[column][r[0]]['cc']=relationships[column][r[0]]['incoming']*1.0/totalSize
bitmap[column][item][r[0]]=1
classSet=set() # A set to save all the possible cc classes for ease of retrieval later and to streamline it.
for column in columnNames: #Loop to push the relations and nodes to Neo4j
for classes in relationships[column].keys():
classSet.add(classes)
rel_data=Neo4jDrive.insertNodeAndRelationship(column,'cc',classes)[0]
rel_data.properties['rel_class']='cc'
rel_data.properties['fk']=relationships[column][classes]['cc']
rel_data.push()
for classes in classSet: #Loop to update the CCS score for each class after the previous loop is over. CCS=sum(fk)/no(fk) for the node.
print classes
cummulative=0 # The accumulator
linkNumbers=0 # The denominator
for link in Neo4jDrive.findIncomingCCLinks(classes): #loop to find incoming cc edges.
cummulative+=link[0].properties['fk']
linkNumbers+=1
node=Neo4jDrive.findNodeByName(classes)
node.properties['ccs']=cummulative*1.0/linkNumbers
node.properties['type']='cc'
node.push()
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()
def main():
Neo4jDrive.insertNode(nameOfFile)
columnNames=CSVRead.readCSV(nameOfFile,firstRow=True, choice=[0,1,2,3,4])
for name in columnNames:
Neo4jDrive.insertNodeAndRelationship(nameOfFile,"Column",name)
#support=CSVRead.getSupport(nameOfFile,0)
#totalNumberOfValues=CSVRead.numberOfItems(support)
for column in range(sum([1 for _ in Neo4jDrive.findRelationshipsOfNode(nameOfFile,"Column")])):
support=CSVRead.getSupport(nameOfFile,column)
totalNumberOfValues=CSVRead.numberOfItems(support)
#print i.end_node
#cNode=Neo4jDrive.findNodeByName(columnNames[column])
for item in support.keys():
node=Neo4jDrive.findNodeByName(item)
if node== None:
Neo4jDrive.insertNodeAndRelationship(columnNames[column],'dataItems',item)
node=Neo4jDrive.findNodeByName(item)
node.properties['fvalue']=support[item]
node.push()
rlist=sparqlQuerypy.findBottomUp(item)
for r in rlist:
try:
rel_data=Neo4jDrive.insertNodeAndRelationship(item,"cc",r[0])
rel_data1=Neo4jDrive.insertNodeAndRelationship(r[0],"dd",r[2])
node=node=Neo4jDrive.findNodeByName(r[2])
if node.properties['incoming']==None:
node.properties['incoming']=1
else:
node.properties['incoming']+=1
node.properties['type']='type'
node.push()
except :
print columnNames[column],'cc',r[0]
rel_data=rel_data[0]
rel_data.properties['rel_class'] = 'cc'
rel_data.properties['support']=support[item]/(totalNumberOfValues*1.0)
rel_data.push()
def main():
Neo4jDrive.insertNode(nameOfFile)
columnNames = CSVRead.readCSV(nameOfFile, firstRow=True, choice=[0, 1, 2, 3, 4])
for name in columnNames:
Neo4jDrive.insertNodeAndRelationship(nameOfFile, "Column", name)
# support=CSVRead.getSupport(nameOfFile,0)
# totalNumberOfValues=CSVRead.numberOfItems(support)
for column in range(sum([1 for _ in Neo4jDrive.findRelationshipsOfNode(nameOfFile, "Column")])):
support = CSVRead.getSupport(nameOfFile, column)
totalNumberOfValues = CSVRead.numberOfItems(support)
# print i.end_node
# cNode=Neo4jDrive.findNodeByName(columnNames[column])
for item in support.keys():
node = Neo4jDrive.findNodeByName(item)
if node == None:
Neo4jDrive.insertNodeAndRelationship(columnNames[column], "dataItems", item)
node = Neo4jDrive.findNodeByName(item)
node.properties["fvalue"] = support[item]
node.push()
rlist = sparqlQuerypy.findBottomUp(item)
for r in rlist:
try:
rel_data = Neo4jDrive.insertNodeAndRelationship(item, "cc", r[0])
rel_data1 = Neo4jDrive.insertNodeAndRelationship(r[0], "dd", r[2])
node = node = Neo4jDrive.findNodeByName(r[2])
if node.properties["incoming"] == None:
node.properties["incoming"] = 1
else:
node.properties["incoming"] += 1
node.properties["type"] = "type"
node.push()
except:
print columnNames[column], "cc", r[0]
rel_data = rel_data[0]
rel_data.properties["rel_class"] = "cc"
rel_data.properties["support"] = support[item] / (totalNumberOfValues * 1.0)
rel_data.push()
def run(self):
support = self.support
totalNumberOfValues = self.totalNumberOfValues
column = self.column
columnNames = self.columnNames
item = self.item
rlist = sparqlQuerypy.findBottomUp(item)
for r in rlist:
rel_data = Neo4jDrive.insertNodeAndRelationship(columnNames[column], "cc", r[2])
node = Neo4jDrive.findNodeByName(r[2])
if node.properties["incoming"] == None:
node.properties["incoming"] = 1
else:
node.properties["incoming"] += 1
node.properties["type"] = "type"
node.push()
rel_data = rel_data[0]
rel_data.properties["rel_class"] = "cc"
rel_data.properties["support"] = support[item] / (totalNumberOfValues * 1.0)
rel_data.push()
