-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathgengraphfewshot.py
282 lines (259 loc) · 15.7 KB
/
gengraphfewshot.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
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
import numpy as np
import scipy.sparse as sp
import torch
import torch.nn as nn
import os
import random
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader
import process
from process import process_tu
dataset = 'pubmed'
# training params
batch_size = 300
nb_epochs = 10000
patience = 20
lr = 0.001
l2_coef = 0.0
drop_prob = 0.0
hid_units = 512
sparse = False
nonlinearity = 'prelu' # special name to separate parameters
i = 0
class_num = 2
node_class_num = 3
dataset = TUDataset(root='data', name='BZR', use_node_attr=True)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
shotnumlist = [40,50]
# random.seed(39)
for step, data in enumerate(loader):
if step == 0:
for i in range(0, 100):
cnt = 0
cnt0 = 0
cnt1 = 0
cnt2 = 0
cnt3 = 0
cnt4 = 0
cnt5 = 0
cnt6 = 0
for shotnum in shotnumlist:
if shotnum == shotnumlist[0]:
graphlen = [0] * (shotnum * class_num)
graph_list = [0] * (shotnum * class_num)
# graph_labels = torch.Tensor(shotnum * class_num)
else:
graphlen = pastlen + [0] * class_num * (shotnum - pastshotnum)
graph_list = pastlist + [0] * class_num * (shotnum - pastshotnum)
# graph_labels = torch.Tensor(shotnum * class_num)
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}".format(shotnum,i))
# for n1 in range(0, 3):
# for n2 in range(0, 3):
# trainfeature[n1][n2] = 0
for m in range(0, 10000):
j = random.randint(0, 199)
trainfeature = data[j].x
# print(type(trainfeature))
trainfeature = trainfeature[:, node_class_num]
if cnt == class_num * shotnum:
pastshotnum = shotnum
pastlist = graph_list
pastlen = graphlen
if not os.path.isdir("data/fewshot_BZR_graph/{}shot_BZR_graph/{}".format(shotnum, i)):
os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}".format(shotnum, i))
else:
break
minidataset = data[graph_list]
feature, adj, lbls, lens = process_tu(minidataset, node_class_num, shotnum * class_num)
adj = process.normalize_adj(adj + sp.eye(adj.shape[0]))
adj = (adj + sp.eye(adj.shape[0])).todense()
adj = torch.FloatTensor(adj)
feature = torch.FloatTensor(feature)
graphlen = torch.Tensor(graphlen)
torch.save(feature,
"data/fewshot_BZR_graph/{}shot_BZR_graph/{}/feature.pt".format(shotnum,
i))
torch.save(adj,
"data/fewshot_BZR_graph/{}shot_BZR_graph/{}/adj.pt".format(shotnum, i))
torch.save(lbls,
"data/fewshot_BZR_graph/{}shot_BZR_graph/{}/labels.pt".format(shotnum, i))
torch.save(lens,
"data/fewshot_BZR_graph/{}shot_BZR_graph/{}/graph_len.pt".format(shotnum,
i))
# # os.removedir("fewshot/{}".format(i))
#
# torch.save(trainlal, "fewshot_BZR/{}/nodelabels.pt".format(i))
#
# torch.save(trainidx, "fewshot_BZR/{}/nodeidx.pt".format(i))
# for n1 in range(0, 3):
# for n2 in range(0, 3):
# trainadj[n1][n2] = adj[0][mark[n1]][mark[n2]]
#
# print('number', i, 'trainlal', trainlal, '\n')
#
# print('trainfeature', trainfeature, '\n')
#
# print("adj", trainadj)
break
if data[j].y[0].item() == 0 and cnt0 != shotnum:
graphlen[cnt] = trainfeature.shape[0]
graph_list[cnt] = j
# graph_labels[cnt] = 0
# trainlal[0][cnt] = 0
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(shotnum,i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(shotnum,i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(shotnum,i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(shotnum,i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(shotnum,i, cnt))
print(j, ',0')
cnt = cnt + 1
cnt0 += 1
if data[j].y[0].item() == 1 and cnt1 != shotnum:
graphlen[cnt] = trainfeature.shape[0]
graph_list[cnt] = j
# graph_labels[cnt] = 1
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(shotnum,i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(shotnum,i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(shotnum,i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(shotnum,i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(shotnum,i, cnt))
print(j, ',1')
# mark[cnt] = j
cnt = cnt + 1
cnt1 += 1
if data[j].y[0].item() == 2 and cnt2 != shotnum:
graphlen[cnt] = trainfeature.shape[0]
graph_list[cnt] = j
# graph_labels[cnt] = 2
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(shotnum,i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(shotnum,i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(shotnum,i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(shotnum,i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(shotnum,i, cnt))
print(j, ',2')
# mark[cnt] = j
cnt = cnt + 1
cnt2 += 1
if data[j].y[0].item() == 3 and cnt3 != shotnum:
graphlen[cnt] = trainfeature.shape[0]
graph_list[cnt] = j
# graph_labels[cnt] = 3
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(shotnum,i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(shotnum,i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(shotnum,i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(shotnum,i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(shotnum,i, cnt))
print(j, ',3')
# mark[cnt] = j
cnt = cnt + 1
cnt3 += 1
if data[j].y[0].item() == 4 and cnt4 != shotnum:
graphlen[cnt] = trainfeature.shape[0]
graph_list[cnt] = j
# graph_labels[cnt] = 4
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(shotnum,i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(shotnum,i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(shotnum,i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(shotnum,i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(shotnum,i, cnt))
print(j, ',4')
# mark[cnt] = j
cnt = cnt + 1
cnt4 += 1
if data[j].y[0].item() == 5 and cnt5 != shotnum:
graphlen[cnt] = trainfeature.shape[0]
graph_list[cnt] = j
# graph_labels[cnt] = 5
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(shotnum,i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(shotnum,i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(shotnum,i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(shotnum,i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(shotnum,i, cnt))
print(j, ',5')
# mark[cnt] = j
cnt = cnt + 1
cnt5 += 1
# if data[j].y[0].item() == 2 and cnt2 != shotnum:
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(i, cnt))
# print(j, ',2')
# # mark[cnt] = j
# cnt = cnt + 1
# cnt2 += 1
# if data[j].y[0].item() == 3 and cnt3 != shotnum:
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(i, cnt))
# # mark[cnt] = j
# cnt = cnt + 1
# cnt3 += 1
# if data[j].y[0].item() == 4 and cnt4 != shotnum:
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(i, cnt))
# print(j, ',4')
# # mark[cnt] = j
# cnt = cnt + 1
# cnt4 += 1
# if data[j].y[0].item() == 5 and cnt5 != shotnum:
# os.makedirs("data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}".format(i, cnt))
# torch.save(e_ind, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/e_ind.pt".format(i, cnt))
# # trainidx[cnt] = j
# # list[j] = 1
# torch.save(trainfeature, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphemb.pt".format(i, cnt))
# torch.save(trainadj, "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphadj.pt".format(i, cnt))
# torch.save(data[j].y[0], "data/fewshot_BZR_graph/{}shot_BZR_graph/{}/{}/graphlabel.pt".format(i, cnt))
# print(j, ',5')
# # mark[cnt] = j
# cnt = cnt + 1
# cnt5 += 1
# if lals[0][j].item() == 6 and cnt6 == 0:
# trainlal[0][cnt] = 6
# trainfeature[cnt][2] = 1
# # list[j] = 1
# # print(j, ' ,')
# cnt = cnt + 1
# cnt6 = 1
break
else:
os.makedirs("data/fewshot_BZR_graph/testset")
testset = data[range(100)]
feature, adj, labels, graphlen = process_tu(testset, node_class_num, 100)
adj = process.normalize_adj(adj + sp.eye(adj.shape[0]))
adj = (adj + sp.eye(adj.shape[0])).todense()
adj = torch.FloatTensor(adj)
feature = torch.FloatTensor(feature)
graphlen = torch.Tensor(graphlen)
torch.save(feature, "data/fewshot_BZR_graph/testset/feature.pt")
torch.save(adj,
"data/fewshot_BZR_graph/testset/adj.pt")
torch.save(labels,
"data/fewshot_BZR_graph/testset/labels.pt")
torch.save(graphlen,
"data/fewshot_BZR_graph/testset/graph_len.pt")
print("end")