def calc_aproc_s(embedding, X1, X2, train_digraph, train_digraph1,
test_digraph, sample_edges, trp, trn, had):
if (had == 1):
func = hadamard1
elif (had == 0):
func = hadamard2
elif (had == -1):
func = hadamard3
trd, trl = create_vector_dataset(trp, trn, func, X1)
mean = np.mean(trd, axis=0)
std = np.std(trd, axis=0)
trd = (trd - mean) / std
clasifier = train_classifier(trd, trl)
filtered_edge_list = getscore5(train_digraph, sample_edges, clasifier,
func, X1, mean, std)
AP1, ROC1 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP1, ROC1)
for (st, ed) in train_digraph1.edges():
train_digraph.add_edge(st, ed)
filtered_edge_list = getscore5(train_digraph, sample_edges, clasifier,
func, X2, mean, std)
AP2, ROC2 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP2, ROC2)
return AP1, AP2, ROC1, ROC2
def evaluate_unsupervised_embedding(di_graph,
graph_embedding,
is_undirected=True):
train_digraph, test_digraph = train_test_split.splitDiGraphToTrainTest2(
di_graph, train_ratio=0.8, is_undirected=True)
X, _ = graph_embedding.learn_embedding(graph=train_digraph,
no_python=False)
sample_edges = sample_edge_new(train_digraph, test_digraph, 0.5)
filtered_edge_list = getscore4(train_digraph, graph_embedding,
sample_edges)
AP, ROC = scores.computeAP_ROC(filtered_edge_list, test_digraph)
test_digraph1, node_l = graph_util.sample_graph(test_digraph, 1024)
X = X[node_l]
estimated_adj = graph_embedding.get_reconstructed_adj(X, node_l)
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj, is_undirected=True)
filtered_edge_list = [
e for e in predicted_edge_list
if not (train_digraph.has_edge(node_l[e[0]], node_l[e[1]]))
]
MAP = scores.computeMAP(filtered_edge_list, test_digraph1)
print(AP, ROC, MAP)
return AP, ROC, MAP
def calc_aproc_us(embedding, X, train_digraph, test_digraph, sample_edges):
filtered_edge_list = getscore4(train_digraph, embedding, sample_edges)
AP, ROC = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP, ROC)
return AP, ROC
def evaluate_unsupervised_all(di_graph, is_undirected=True):
train_digraph, test_digraph = train_test_split.splitDiGraphToTrainTest2(
di_graph, train_ratio=0.8, is_undirected=True)
sample_edges = sample_edge_new(train_digraph, test_digraph)
test_digraph1, node_l = graph_util.sample_graph(test_digraph, 1024)
AP = []
ROC = []
MAP = []
heurestics = [cn, jc, pa, aa]
for x in heurestics:
estimated_adj = getscore1(train_digraph, node_l, x)
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj, is_undirected=True)
filtered_edge_list = [
e for e in predicted_edge_list
if not train_digraph.has_edge(node_l[e[0]], node_l[e[1]])
]
MAP1 = scores.computeMAP(filtered_edge_list, test_digraph1)
MAP.append(MAP1)
filtered_edge_list = getscore3(train_digraph, sample_edges, x)
AP1, ROC1 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
AP.append(AP1)
ROC.append(ROC1)
print(AP1, ROC1, MAP1)
return AP, ROC, MAP
def calc_aproc_heu_s(train_digraph, train_digraph1, test_digraph, trp, trn,
sample_edges):
trd, trl = create_score_dataset(trp, trn, allh, train_digraph)
mean = np.mean(trd, axis=0)
std = np.std(trd, axis=0)
trd = (trd - mean) / std
clasifier = train_classifier(trd, trl)
filtered_edge_list = getscore7(train_digraph, sample_edges, clasifier,
allh, mean, std)
AP1, ROC1 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP1, ROC1)
for (st, ed) in train_digraph1.edges():
train_digraph.add_edge(st, ed)
filtered_edge_list = getscore7(train_digraph, sample_edges, clasifier,
allh, mean, std)
AP2, ROC2 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP2, ROC2)
return AP1, AP2, ROC1, ROC2
def calc_aproc_heu(train_digraph, test_digraph, sample_edges):
AP = []
ROC = []
heurestics = [cn, jc, pa, aa]
for x in heurestics:
filtered_edge_list = getscore3(train_digraph, sample_edges, x)
AP1, ROC1 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
AP.append(AP1)
ROC.append(ROC1)
print(AP, ROC)
return AP, ROC
def evaluate_supervised(di_graph, graph_embedding, is_undirected=True):
train_digraph, test_digraph = train_test_split.splitDiGraphToTrainTest2(
di_graph, train_ratio=0.6, is_undirected=True)
train_digraph1, test_digraph = evaluation_util.splitDiGraphToTrainTest(
test_digraph, train_ratio=0.5, is_undirected=is_undirected)
X, _ = graph_embedding.learn_embedding(graph=train_digraph,
no_python=False)
trp, trn = create_edge_dataset(train_digraph, train_digraph1)
trd, trl = create_vector_dataset(trp, trn, hadamard2, X)
mean = np.mean(trd, axis=0)
std = np.std(trd, axis=0)
trd = (trd - mean) / std
clasifier = train_classifier(trd, trl)
for (st, ed) in train_digraph1.edges():
train_digraph.add_edge(st, ed)
sample_edges = sample_edge_new(train_digraph, test_digraph, 0.5)
X, _ = graph_embedding.learn_embedding(graph=train_digraph,
no_python=False)
filtered_edge_list = getscore5(train_digraph, sample_edges, clasifier,
hadamard2, X, mean, std)
AP, ROC = scores.computeAP_ROC(filtered_edge_list, test_digraph)
test_digraph, node_l = graph_util.sample_graph(test_digraph, 1024)
X = X[node_l]
estimated_adj = getscore2(train_digraph, node_l, clasifier, hadamard2, X,
mean, std)
predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(
estimated_adj, is_undirected=True)
filtered_edge_list = [
e for e in predicted_edge_list
if not train_digraph.has_edge(node_l[e[0]], node_l[e[1]])
]
MAP = scores.computeMAP(filtered_edge_list, test_digraph)
print(MAP)
return AP, ROC, MAP
def evaluate_unsupervised_all(di_graph, is_undirected=True):
train_digraph, test_digraph = train_test_split.splitDiGraphToTrainTest2(
di_graph, train_ratio=0.8, is_undirected=True)
# train_digraph, test_digraph = train_test_split.splitDiGraphToTrainTest2(di_graph, train_ratio = 0.75, is_undirected=True)
# train_digraph1, test_digraph = evaluation_util.splitDiGraphToTrainTest(
# test_digraph,
# train_ratio=0.2,
# is_undirected=is_undirected
# )
# train_digraph_temp=train_digraph.copy()
# for (st,ed) in train_digraph1.edges():
# train_digraph_temp.add_edge(st,ed)
# sample_edges = sample_edge_new(train_digraph_temp,test_digraph, -1)
sample_edges = sample_edge_new(train_digraph, test_digraph, -1)
filtered_edge_list = getscore3(train_digraph, sample_edges, aa)
AP1, ROC1 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP1, ROC1)
return AP1, ROC1
def evaluate_supervised_new(train_digraph,
embeddings,
hads,
is_undirected=True):
train_digraph, test_digraph = train_test_split.splitDiGraphToTrainTest2(
train_digraph, train_ratio=0.6, is_undirected=True)
for (st, ed) in train_digraph.edges():
if (test_digraph.has_edge(st, ed)):
test_digraph.remove_edge(st, ed)
train_digraph1, test_digraph = evaluation_util.splitDiGraphToTrainTest(
test_digraph, train_ratio=0.5, is_undirected=is_undirected)
l_emb = []
combine = []
for emb in embeddings:
X, _ = emb.learn_embedding(graph=train_digraph, no_python=False)
l_emb.append(X)
for had in hads:
if (had == 1):
combine.append(hadamard1)
elif (had == 0):
combine.append(hadamard2)
# combine.append(dotp1)
print("embeddings learned")
trp, trn = create_edge_dataset(train_digraph, train_digraph1)
trd, trl = create_mix_dataset(trp, trn, train_digraph, l_emb, combine)
mean = np.mean(trd, axis=0)
std = np.std(trd, axis=0)
trd = (trd - mean) / std
clasifier = train_classifier(trd, trl)
# print (clasifier.coef_)
# print (clasifier.intercept_)
train_digraph_temp = train_digraph.copy()
for (st, ed) in train_digraph1.edges():
train_digraph_temp.add_edge(st, ed)
sample_edges = sample_edge_new(train_digraph_temp,
test_digraph,
-1,
num_edges=500000)
# co=0
# for (st,ed) in sample_edges:
# for (st1,ed1) in trn:
# if(st==st1 and ed==ed1):
# if(test_digraph.has_edge(st,ed)):
# print ("1")
# for (st1,ed1) in trp:
# if(st==st1 and ed==ed1):
# if(test_digraph.has_edge(st,ed)):
# print ("2")
# else:
# print ("3")
# l_emb1 = []
# for emb in embeddings:
# X, _ = emb.learn_embedding(graph=train_digraph_temp, no_python=False)
# l_emb1.append(X)
# break
# l_emb1.append(l_emb[1])
print("embeddings learned")
# filtered_edge_list = getscore9(train_digraph, sample_edges, clasifier, l_emb1, combine, mean, std)
# AP, ROC = scores.computeAP_ROC(filtered_edge_list, test_digraph)
# print (AP,ROC)
filtered_edge_list = getscore9(train_digraph_temp, sample_edges, clasifier,
l_emb, combine, mean, std)
AP, ROC = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP, ROC)
trd, trl = create_score_dataset(trp, trn, allh, train_digraph)
mean = np.mean(trd, axis=0)
std = np.std(trd, axis=0)
trd = (trd - mean) / std
clasifier = train_classifier(trd, trl)
filtered_edge_list = getscore7(train_digraph_temp, sample_edges, clasifier,
allh, mean, std)
AP2, ROC2 = scores.computeAP_ROC(filtered_edge_list, test_digraph)
print(AP2, ROC2)
# G11 = train_digraph.to_undirected()
# f1=[]
# f2=[]
# for (st,ed,w) in filtered_edge_list:
# f1.append(w)
# f2.append(cn(G11,st,ed))
# f1=np.array(f1)
# f2=np.array(f2)
# ind1 = np.argsort(-1*f1)
# ind2 = np.argsort(-1*f2)
# print (ind1[:1000])
# print (ind2[:1000])
# print (f1[ind1[:1000]])
# print (f2[ind1[:1000]])
# filtered_edge_list = getscore3(train_digraph_temp, sample_edges, aa)
# AP, ROC = scores.computeAP_ROC(filtered_edge_list, test_digraph)
# print (AP,ROC)
return AP, ROC
# labels=[]
# score=[]
# dist=[]
# G=train_digraph.to_undirected()
# print (len(filtered_edge_list))
# for (st,ed,w) in filtered_edge_list:
# # if not(nx.shortest_path_length(G,source=st,target=ed)==2):
# # continue
# if(test_digraph.has_edge(st,ed)):
# labels.append(1)
# else:
# labels.append(0)
# score.append(w)
# ap = average_precision_score(labels, score)
# print (ap)
# ind = np.argsort(-1*np.asarray(score))
# labels = np.array(labels)
# print (labels[ind[:1000]])
# labels=[]
# score=[]
# dist=[]
# G=train_digraph.to_undirected()
# for (st,ed,w) in filtered_edge_list:
# if (nx.shortest_path_length(G,source=st,target=ed)==2):
# continue
# if(test_digraph.has_edge(st,ed)):
# labels.append(1)
# else:
# labels.append(0)
# score.append(w)
# ap = average_precision_score(labels, score)
# print (ap)
# ind = np.argsort(-1*np.asarray(score))
# labels = np.array(labels)
# print (labels[ind[:1000]])
# test_digraph, node_l = graph_util.sample_graph(test_digraph, 1024)
# estimated_adj = getscore8(train_digraph, node_l, clasifier, l_emb1, combine)
# predicted_edge_list = evaluation_util.getEdgeListFromAdjMtx(estimated_adj,is_undirected=True)
# filtered_edge_list = [e for e in predicted_edge_list if not train_digraph.has_edge(node_l[e[0]], node_l[e[1]])]
# MAP = scores.computeMAP(filtered_edge_list, test_digraph)
# print (MAP)
MAP = 0
return AP, ROC, MAP
def evaluate_supervised_new1(train_digraph,
train_digraph1,
test_digraph,
trp1,
trn1,
trp2,
trn2,
trp3,
trn3,
sample_edges,
l_emb,
hads,
is_undirected=True):
combine = []
for had in hads:
if (had == 1):
combine.append(hadamard1)
elif (had == 0):
combine.append(hadamard2)
# create dataset for training the classifier with appropriate combination of embeddings and heuristics.
# only heuristics
trd1, trl1 = create_mix_dataset(trp1, trn1, train_digraph, [], combine,
func_new)
# combination of embeddings
trd2, trl2 = create_mix_dataset(trp2, trn2, train_digraph, l_emb, combine,
func_new1)
# combination of embeddings and heuristics
trd3, trl3 = create_mix_dataset(trp3, trn3, train_digraph, l_emb, combine,
func_new)
# learn classifiers for the three types of featre combination.
mean1 = np.mean(trd1, axis=0)
std1 = np.std(trd1, axis=0)
for x in xrange(len(std1)):
std1[x] = 1
trd1 = (trd1 - mean1) / std1
clasifier1 = train_classifier(trd1, trl1)
mean2 = np.mean(trd2, axis=0)
std2 = np.std(trd2, axis=0)
for x in xrange(len(std2)):
std2[x] = 1
trd2 = (trd2 - mean2) / std2
clasifier2 = train_classifier(trd2, trl2)
mean3 = np.mean(trd3, axis=0)
std3 = np.std(trd3, axis=0)
for x in xrange(len(std3)):
std3[x] = 1
trd3 = (trd3 - mean3) / std3
clasifier3 = train_classifier(trd3, trl3)
train_digraph_temp = train_digraph.copy()
for (st, ed) in train_digraph1.edges():
train_digraph_temp.add_edge(st, ed)
f2 = []
f3 = []
G = train_digraph_temp.to_undirected()
for (st, ed) in sample_edges:
dis = nx.shortest_path_length(G, source=st, target=ed)
if (dis == 2):
f2.append((st, ed))
else:
f3.append((st, ed))
# use train_graph_temp=train_graph + train_graph1 for calculating heuristic scores.
# use this section for evaluating on distance 2 edges
# filtered_edge_list1 = getscore9(train_digraph_temp, f2, clasifier1, [], combine, func_new, mean1, std1)
# filtered_edge_list2 = getscore9(train_digraph_temp, f2, clasifier2, l_emb, combine, func_new1, mean2, std2)
# filtered_edge_list3 = getscore9(train_digraph_temp, f2, clasifier3, l_emb, combine, func_new, mean3, std3)
# use this section for evaluating on distance 2+ edges
# filtered_edge_list1 = getscore9(train_digraph_temp, f3, clasifier1, [], combine, func_new, mean1, std1)
# filtered_edge_list2 = getscore9(train_digraph_temp, f3, clasifier2, l_emb, combine, func_new1, mean2, std2)
# filtered_edge_list3 = getscore9(train_digraph_temp, f3, clasifier3, l_emb, combine, func_new, mean3, std3)
# use this section for evaluating on all edges
filtered_edge_list1 = getscore9(train_digraph_temp, sample_edges,
clasifier1, [], combine, func_new, mean1,
std1)
filtered_edge_list2 = getscore9(train_digraph_temp, sample_edges,
clasifier2, l_emb, combine, func_new1,
mean2, std2)
filtered_edge_list3 = getscore9(train_digraph_temp, sample_edges,
clasifier3, l_emb, combine, func_new,
mean3, std3)
AP11, ROC11 = scores.computeAP_ROC(filtered_edge_list1, test_digraph)
print(AP11, ROC11)
AP12, ROC12 = scores.computeAP_ROC(filtered_edge_list2, test_digraph)
print(AP12, ROC12)
AP13, ROC13 = scores.computeAP_ROC(filtered_edge_list3, test_digraph)
print(AP13, ROC13)
return AP11, ROC11, AP12, ROC12, AP13, ROC13