-
Notifications
You must be signed in to change notification settings - Fork 12
Expand file tree
/
Copy pathpreprocessing.py
More file actions
166 lines (138 loc) · 5.79 KB
/
Copy pathpreprocessing.py
File metadata and controls
166 lines (138 loc) · 5.79 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
import numpy as np
import scipy.sparse as sp
def sparse_to_tuple(sparse_mx):
if not sp.isspmatrix_coo(sparse_mx):
sparse_mx = sparse_mx.tocoo()
coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
values = sparse_mx.data
shape = sparse_mx.shape
return coords, values, shape
def get_multi_sampled_index(adjs, size, center_num=3):
numView = len(adjs)
nodes = adjs[0].shape[0]
indices = set(np.random.choice(nodes, center_num, False))
indice = []
for n in range(numView):
indice.append(get_sampled_index(adjs[n], size, center_num, indices))
indice_set = set(indice[0])
for in_set in indice:
indice_set = indice_set & set(in_set)
return sorted(indice_set)
def get_sampled_index(adj, size, center_num, indices):
n = adj.shape[0]
pre_indices = set()
#indices = set(np.random.choice(n, center_num, False))
while len(indices) < size:
if len(pre_indices) != len(indices):
new_add = indices - pre_indices
pre_indices = indices
candidates = get_candidates(adj, new_add) - indices
else:
candidates = random_num(n, center_num, indices)
sample_size = min(len(candidates), size-len(indices))
if not sample_size:
break
if len(candidates) > size - len(indices):
candidates = set(np.random.choice(list(candidates), size-len(indices), False))
indices.update(candidates)
return sorted(indices)
def get_candidates(adj, new_add):
tmp = adj[sorted(new_add)].sum(axis=0)
tmp1 = adj[sorted(new_add)].sum(axis=0).nonzero()[0]
return set(adj[sorted(new_add)].sum(axis=0).nonzero()[0])
def random_num(n, num, indices):
cans = set(np.arange(n)) - indices
num = min(num, len(cans))
if len(cans) == 0:
return set()
new_add = set(np.random.choice(list(cans), num, replace=False))
return new_add
def preprocess_graph(adjs):
numView = len(adjs)
adjs_normarlized = []
for v in range(numView):
adj = sp.coo_matrix(adjs[v])
adj_ = adj + sp.eye(adj.shape[0])
rowsum = np.array(adj_.sum(1))
degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten())
adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).toarray()
adjs_normarlized.append(adj_normalized.tolist())
return np.array(adjs_normarlized)
def construct_feed_dict(adj_normalized, adj, features, placeholders):
# construct feed dictionary
feed_dict = dict()
feed_dict.update({placeholders['features']: features})
feed_dict.update({placeholders['adjs']: adj_normalized})
feed_dict.update({placeholders['adjs_orig']: adj})
return feed_dict
def mask_test_edges(adj):
# Function to build test set with 10% positive links
# NOTE: Splits are randomized and results might slightly deviate from reported numbers in the paper.
# TODO: Clean up.
# Remove diagonal elements
adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape)
adj.eliminate_zeros()
# Check that diag is zero:
assert np.diag(adj.todense()).sum() == 0
adj_triu = sp.triu(adj)
adj_tuple = sparse_to_tuple(adj_triu)
edges = adj_tuple[0]
edges_all = sparse_to_tuple(adj)[0]
num_test = int(np.floor(edges.shape[0] / 10.))
num_val = int(np.floor(edges.shape[0] / 20.))
all_edge_idx = range(edges.shape[0])
np.random.shuffle(all_edge_idx)
val_edge_idx = all_edge_idx[:num_val]
test_edge_idx = all_edge_idx[num_val:(num_val + num_test)]
test_edges = edges[test_edge_idx]
val_edges = edges[val_edge_idx]
train_edges = np.delete(edges, np.hstack([test_edge_idx, val_edge_idx]), axis=0)
def ismember(a, b, tol=5):
rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1)
return (np.all(np.any(rows_close, axis=-1), axis=-1) and
np.all(np.any(rows_close, axis=0), axis=0))
test_edges_false = []
while len(test_edges_false) < len(test_edges):
idx_i = np.random.randint(0, adj.shape[0])
idx_j = np.random.randint(0, adj.shape[0])
if idx_i == idx_j:
continue
if ismember([idx_i, idx_j], edges_all):
continue
if test_edges_false:
if ismember([idx_j, idx_i], np.array(test_edges_false)):
continue
if ismember([idx_i, idx_j], np.array(test_edges_false)):
continue
test_edges_false.append([idx_i, idx_j])
val_edges_false = []
while len(val_edges_false) < len(val_edges):
idx_i = np.random.randint(0, adj.shape[0])
idx_j = np.random.randint(0, adj.shape[0])
if idx_i == idx_j:
continue
if ismember([idx_i, idx_j], train_edges):
continue
if ismember([idx_j, idx_i], train_edges):
continue
if ismember([idx_i, idx_j], val_edges):
continue
if ismember([idx_j, idx_i], val_edges):
continue
if val_edges_false:
if ismember([idx_j, idx_i], np.array(val_edges_false)):
continue
if ismember([idx_i, idx_j], np.array(val_edges_false)):
continue
val_edges_false.append([idx_i, idx_j])
assert ~ismember(test_edges_false, edges_all)
assert ~ismember(val_edges_false, edges_all)
assert ~ismember(val_edges, train_edges)
assert ~ismember(test_edges, train_edges)
assert ~ismember(val_edges, test_edges)
data = np.ones(train_edges.shape[0])
# Re-build adj matrix
adj_train = sp.csr_matrix((data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape)
adj_train = adj_train + adj_train.T
# NOTE: these edge lists only contain single direction of edge!
return adj_train, train_edges, val_edges, val_edges_false, test_edges, test_edges_false