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