This repository was archived by the owner on Feb 21, 2025. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy pathpreprocess.py
195 lines (171 loc) · 6.37 KB
/
preprocess.py
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
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import numpy as np
import scipy.sparse as sp
import torch
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] / 10.0))
num_val = int(np.floor(edges.shape[0] / 20.0))
all_edge_idx = list(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.any(rows_close)
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,
)
def mask_test_edges_dgl(graph, adj):
src, dst = graph.edges()
edges_all = torch.stack([src, dst], dim=0)
edges_all = edges_all.t().cpu().numpy()
num_test = int(np.floor(edges_all.shape[0] / 10.0))
num_val = int(np.floor(edges_all.shape[0] / 20.0))
all_edge_idx = list(range(edges_all.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)]
train_edge_idx = all_edge_idx[(num_val + num_test) :]
test_edges = edges_all[test_edge_idx]
val_edges = edges_all[val_edge_idx]
train_edges = np.delete(
edges_all, 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.any(rows_close)
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)
# NOTE: these edge lists only contain single direction of edge!
return (
train_edge_idx,
val_edges,
val_edges_false,
test_edges,
test_edges_false,
)
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 preprocess_graph(adj):
adj = sp.coo_matrix(adj)
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)
.tocoo()
)
return adj_normalized, sparse_to_tuple(adj_normalized)