linkpred.addLinkToNetwork(testnet,node2, node1)
#weights = linkpred.computeRankedList(wholenet, wholenet.keys(), {}, func=rootedPagerank)
#print weights
#ranks= linkpred.pagerank(wholenet,weights=weights)
#print sorted(ranks.items(),key=lambda x:x[1])
testnet = linkpred.getDifference(testnet,trainnet)
#core = set(trainnet.keys()).intersection(testnet)
core = linkpred.getCoreNodes(trainnet,testnet,1,1)
testnet_pruned = linkpred.prune(testnet,core)
nLinks=linkpred.getNumberOfLinks(testnet_pruned)
print "trainnet :", linkpred.getNumberOfLinks(trainnet)
print "testnet :",nLinks
#rankedList = linkpred.computeRankedList(trainnet, core, trainnet)
rankedList = linkpred.computeRankedList(trainnet, core, trainnet, func=linkpred.graphDistance)
ranking=OrderedDict(sorted(rankedList.items(),key=operator.itemgetter(1),reverse=True))
#print ranking
auc = linkpred.computeAUCBySampling(testnet_pruned,ranking)
intersect =linkpred.computeTruePositive(ranking, testnet_pruned, nLinks)
prec = intersect/float(nLinks)
print "Correctly predicted :", intersect
print "AUC :",auc
print "Precision :" , prec
linkpred.addLinkToNetwork(testnet,node2, node1)
#weights = linkpred.computeRankedList(wholenet, wholenet.keys(), {}, func=rootedPagerank)
#print weights
#ranks= linkpred.pagerank(wholenet,weights=weights)
#print sorted(ranks.items(),key=lambda x:x[1])
testnet = linkpred.getDifference(testnet,trainnet)
#core = set(trainnet.keys()).intersection(testnet)
core = linkpred.getCoreNodes(trainnet,testnet,1,1)
testnet_pruned = linkpred.prune(testnet,core)
nLinks=linkpred.getNumberOfLinks(testnet_pruned)
print "trainnet :", linkpred.getNumberOfLinks(trainnet)
print "testnet :",nLinks
#rankedList = linkpred.computeRankedList(trainnet, core, trainnet)
rankedList = linkpred.computeRankedList(trainnet, core, trainnet, func=linkpred.getNumberOfCommonNeighbors)
ranking=OrderedDict(sorted(rankedList.items(),key=operator.itemgetter(1),reverse=True))
#print ranking
auc = linkpred.computeAUCBySampling(testnet_pruned,ranking)
intersect =linkpred.computeTruePositive(ranking, testnet_pruned, nLinks)
prec = intersect/float(nLinks)
print "Correctly predicted :", intersect
print "AUC :",auc
print "Precision :" , prec
linkpred.addLinkToNetwork(testnet,node2, node1)
#weights = linkpred.computeRankedList(wholenet, wholenet.keys(), {}, func=rootedPagerank)
#print weights
#ranks= linkpred.pagerank(wholenet,weights=weights)
#print sorted(ranks.items(),key=lambda x:x[1])
testnet = linkpred.getDifference(testnet,trainnet)
#core = set(trainnet.keys()).intersection(testnet)
core = linkpred.getCoreNodes(trainnet,testnet,1,1)
testnet_pruned = linkpred.prune(testnet,core)
nLinks=linkpred.getNumberOfLinks(testnet_pruned)
print "trainnet :", linkpred.getNumberOfLinks(trainnet)
print "testnet :",nLinks
#rankedList = linkpred.computeRankedList(trainnet, core, trainnet)
rankedList = linkpred.computeRankedList(trainnet, core, trainnet, func=linkpred.rootedPagerank)
ranking=OrderedDict(sorted(rankedList.items(),key=operator.itemgetter(1),reverse=True))
#print ranking
auc = linkpred.computeAUCBySampling(testnet_pruned,ranking)
intersect =linkpred.computeTruePositive(ranking, testnet_pruned, nLinks)
prec = intersect/float(nLinks)
print "Correctly predicted :", intersect
print "AUC :",auc
print "Precision :" , prec
linkpred.addLinkToNetwork(testnet,node2, node1)
#weights = linkpred.computeRankedList(wholenet, wholenet.keys(), {}, func=rootedPagerank)
#print weights
#ranks= linkpred.pagerank(wholenet,weights=weights)
#print sorted(ranks.items(),key=lambda x:x[1])
testnet = linkpred.getDifference(testnet,trainnet)
#core = set(trainnet.keys()).intersection(testnet)
core = linkpred.getCoreNodes(trainnet,testnet,1,1)
testnet_pruned = linkpred.prune(testnet,core)
nLinks=linkpred.getNumberOfLinks(testnet_pruned)
print "trainnet :", linkpred.getNumberOfLinks(trainnet)
print "testnet :",nLinks
#rankedList = linkpred.computeRankedList(trainnet, core, trainnet)
rankedList = linkpred.computeRankedList(trainnet, core, trainnet, func=linkpred.adamic)
ranking=OrderedDict(sorted(rankedList.items(),key=operator.itemgetter(1),reverse=True))
#print ranking
auc = linkpred.computeAUCBySampling(testnet_pruned,ranking)
intersect =linkpred.computeTruePositive(ranking, testnet_pruned, nLinks)
prec = intersect/float(nLinks)
print "Correctly predicted :", intersect
print "AUC :",auc
print "Precision :" , prec
linkpred.addLinkToNetwork(testnet,node2, node1)
#weights = linkpred.computeRankedList(wholenet, wholenet.keys(), {}, func=rootedPagerank)
#print weights
#ranks= linkpred.pagerank(wholenet,weights=weights)
#print sorted(ranks.items(),key=lambda x:x[1])
testnet = linkpred.getDifference(testnet,trainnet)
#core = set(trainnet.keys()).intersection(testnet)
core = linkpred.getCoreNodes(trainnet,testnet,1,1)
testnet_pruned = linkpred.prune(testnet,core)
nLinks=linkpred.getNumberOfLinks(testnet_pruned)
print "trainnet :", linkpred.getNumberOfLinks(trainnet)
print "testnet :",nLinks
#rankedList = linkpred.computeRankedList(trainnet, core, trainnet)
rankedList = linkpred.computeRankedList(trainnet, core, trainnet, func=linkpred.jaccard)
ranking=OrderedDict(sorted(rankedList.items(),key=operator.itemgetter(1),reverse=True))
#print ranking
auc = linkpred.computeAUCBySampling(testnet_pruned,ranking)
intersect =linkpred.computeTruePositive(ranking, testnet_pruned, nLinks)
prec = intersect/float(nLinks)
print "Correctly predicted :", intersect
print "AUC :",auc
print "Precision :" , prec
