-
Notifications
You must be signed in to change notification settings - Fork 1
/
guild.yml
351 lines (309 loc) · 23.2 KB
/
guild.yml
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
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
########## Planetoid ##########
- model: Planetoid|planetoid
description: Revisiting Semi-Supervised Learning with Graph Embeddings (Yang et al., 2016)
operations:
train:
description: Train Planetoid-T with hyperparameters reported in the paper
main: Planetoid/train
sourcecode: 'nothing'
flags-dest: args
flags:
modality:
default: "I"
dataset:
choices: ["cora", "citeseer", "pubmed"]
train-cora:
description: Train Planetoid-T with hyperparameters reported in the paper on cora
main: Planetoid/train
sourcecode: 'nothing'
flags-dest: args
flags:
modality:
default: "T"
dataset:
default: "cora"
learning-rate-unsupervised:
default: 0.01
pretrain-batch:
default: 2070
unsupervised-batch:
default: 0
unsupervised-batch-size:
default: 200
train-citeseer:
description: Train Planetoid-T with hyperparameters reported in the paper on citeseer
main: Planetoid/train
sourcecode: 'nothing'
flags-dest: args
flags:
modality:
default: "T"
dataset:
default: "citeseer"
learning-rate-unsupervised:
default: 0.01
pretrain-batch:
default: 2070
unsupervised-batch:
default: 0
unsupervised-batch-size:
default: 200
train-pubmed:
description: Train Planetoid-I with hyperparameters reported in the paper on pubmed
main: Planetoid/train
sourcecode: 'nothing'
flags-dest: args
flags:
modality:
default: "I"
dataset:
default: "pubmed"
test-cora:
description: Test Planetoid; insert the checkpoint to the model (checkpoint-path)
main: Planetoid/test
sourcecode: 'nothing'
flags-dest: args
flags:
modality:
default: "T"
dataset:
default: "cora"
checkpoint-path:
required: True
test-citeseer:
description: Test Planetoid; insert the checkpoint to the model (checkpoint-path)
main: Planetoid/test
sourcecode: 'nothing'
flags-dest: args
flags:
modality:
default: "T"
dataset:
default: "citeseer"
checkpoint-path:
required: True
test-pubmed:
description: Test Planetoid; insert the checkpoint to the model (checkpoint-path)
main: Planetoid/test
sourcecode: 'nothing'
flags-dest: args
flags:
modality:
default: "I"
dataset:
default: "pubmed"
checkpoint-path:
required: True
evaluate10:
description: ... # TODO:
sourcecode: 'Planetoid/*.py'
steps:
- run: train-citeseer epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-cora epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-pubmed epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-citeseer epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-cora epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-pubmed epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
evaluate100:
description: ... # TODO:
sourcecode: 'Planetoid/*.py'
steps:
- run: train-citeseer epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-cora epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-pubmed epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-citeseer epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-cora epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-pubmed epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
########## ChebNet ##########
- model: ChebNet|chebnet
description: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (Deferrard et al, 2016)
operations:
train:
description:
main: ChebNet/train
sourcecode: 'nothing'
flags-dest: args
train-citeseer:
description: Train ChebNet on citeseer dataset with hyperparameters reported in GCN paper
main: ChebNet/train
sourcecode: 'nothing'
flags-dest: args
flags:
dataset:
default: citeseer
num-polynomials:
default: 4
train-cora:
description: Train ChebNet on cora dataset with hyperparameters reported in GCN paper
main: ChebNet/train
sourcecode: 'nothing'
flags-dest: args
flags:
dataset:
default: cora
num-polynomials:
default: 3
train-pubmed:
description: Train ChebNet on pubmed dataset with hyperparameters reported in GCN paper
main: ChebNet/train
sourcecode: 'nothing'
flags-dest: args
flags:
dataset:
default: pubmed
num-polynomials:
default: 4
test: # TODO: finish
description:
main: ChebNet/test
sourcecode: 'nothing'
flags-dest: args
evaluate10:
description: ... # TODO:
sourcecode: 'ChebNet/*.py'
steps:
- run: train-citeseer verbose=0 baseline=1.20 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-cora verbose=0 baseline=0.95 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-pubmed verbose=0 baseline=0.75 patience=15 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-citeseer verbose=0 baseline=1.20 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-cora verbose=0 baseline=0.95 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-pubmed verbose=0 baseline=0.75 patience=15 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
evaluate100:
description: ... # TODO:
sourcecode: 'ChebNet/*.py'
steps:
- run: train-citeseer verbose=0 baseline=1.20 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-cora verbose=0 baseline=0.95 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-pubmed verbose=0 baseline=0.75 patience=15 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-citeseer verbose=0 baseline=1.20 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-cora verbose=0 baseline=0.95 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-pubmed verbose=0 baseline=0.75 patience=15 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
########## GCN ##########
- model: GCN|gcn
description: Semi-Supervised Classification with Graph Convolutional Netowrks (Kipf and welling, 2017)
operations:
train:
description:
main: GCN/train
sourcecode: 'nothing'
flags-dest: args
test: # TODO: finish
description:
main: GCN/test
sourcecode: 'nothing'
flags-dest: args
evaluate10:
description: ... # TODO:
sourcecode: 'GCN/*.py'
steps:
- run: train dataset=[citeseer,cora,pubmed] verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train dataset=[citeseer,cora,pubmed] verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
evaluate100:
description: ... # TODO:
sourcecode: 'GCN/*.py'
steps:
- run: train dataset=citeseer baseline=1.55 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train dataset=citeseer baseline=1.55 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train dataset=cora baseline=1.3 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train dataset=cora baseline=1.3 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train dataset=pubmed baseline=0.85 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train dataset=pubmed baseline=0.85 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
########## GAT ##########
- model: GAT|gat
description: Graph Attention Networks ( Velickovic et al., 2018)
operations:
train:
description:
main: GAT/train
sourcecode: 'nothing'
flags-dest: args
train-citeseer:
description: Train GAT on citeseer dataset with hyperparameters reported in the paper
main: GAT/train
sourcecode: 'nothing'
flags-dest: args
flags:
dataset:
default: citeseer
train-cora:
description: Train GAT on cora dataset with hyperparameters reported in the paper
main: GAT/train
sourcecode: 'nothing'
flags-dest: args
flags:
dataset:
default: cora
train-pubmed:
description: Train GAT on pubmed dataset with hyperparameters reported in the paper
main: GAT/train
sourcecode: 'nothing'
flags-dest: args
flags:
dataset:
default: pubmed
nheads:
default: 8,8
learning-rate:
default: 0.01
l2-weight:
default: 0.001
test:
description:
main: GAT/test
sourcecode: 'nothing'
flags-dest: args
flags:
checkpoint-path:
required: True
test-cora:
description:
main: GAT/test
sourcecode: 'nothing'
flags-dest: args
flags:
checkpoint-path:
required: True
dataset:
default: "cora"
test-citeseer:
description:
main: GAT/test
sourcecode: 'nothing'
flags-dest: args
flags:
checkpoint-path:
required: True
dataset:
default: "citeseer"
test-pubmed:
description:
main: GAT/test
sourcecode: 'nothing'
flags-dest: args
flags:
checkpoint-path:
required: True
nheads:
default: 8,8
dataset:
default: "pubmed"
evaluate10:
description: ... # TODO:
sourcecode: 'GAT/*.py'
steps:
- run: train-citeseer epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-cora epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-pubmed epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-citeseer epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-cora epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
- run: train-pubmed epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473]
evaluate100:
description: ... # TODO:
sourcecode: 'GAT/*.py'
steps:
- run: train-citeseer epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-cora epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-pubmed epochs=1000 verbose=0 net-seed=5687 data-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-citeseer epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-cora epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]
- run: train-pubmed epochs=1000 verbose=0 yang-splits=True data-seed=5687 net-seed=[9516,1353,551,4975,9399,6608,5688,8964,872,6473,3747,7920,8089,3603,8960,516,2616,2674,7135,728,9493,5414,828,1356,8175,2107,9099,7075,4848,5154,5041,5605,2232,191,2668,7086,204,193,4687,7362,865,494,4560,5790,6891,4139,8860,9002,7870,5800,6503,8179,6603,9024,1850,4274,1062,1896,4636,9313,1312,7659,3732,193,6422,9636,6511,9123,3255,8690,1227,2315,8810,7120,2641,9166,4129,7445,4676,162,1514,4088,5130,4182,4215,9841,2140,5012,1868,3082,7932,819,5152,6991,8591,1598,2042,649,5556,6083]