-
Notifications
You must be signed in to change notification settings - Fork 1
/
run_experiments_DBGNN.py
55 lines (44 loc) · 1.28 KB
/
run_experiments_DBGNN.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
import os
from timeit import repeat
os.environ['OPENBLAS_NUM_THREADS'] = '1'
from manage_experiments import *
from multiprocessing import Pool
parameters = {
"hidden_dims":[32,64,16],
"dataset":None,
"method": "DBGNN",
"fraction_test":.3,
"n_epochs":5000,
"learning_rate":0.001,
'weighted':True,
'directed':True,
'weight_decay':1e-3,
'opt_order':3,
'p_dropout':0.4
}
datasets = [
# "temporal_clusters.ngram",
"temporal_clusters_3.ngram",
# "sms.ngram",
# "highschool2011_delta4_ts900_full.ngram",
# "highschool2012_delta4_ts900_full.ngram",
# "workplace2016_delta4_ts900_full.ngram",
# "workplace2018_delta4_ts900_full.ngram",
# "hospital_delta4_ts900_full.ngram",
]
# opt_order_dict = {}
repeat_experiment = 10
n_parallel = 4
fold = "res_DBGNN/"
if __name__ == '__main__':
for dataset in datasets:
parameters["dataset"] = dataset
create_tasks(
parameters,
repeat_experiment = repeat_experiment,
folder = fold
)
list_unfinished = find_unfinished(fold)
with Pool(n_parallel) as p:
list(p.map(experiment_node_classification, list_unfinished))
# select keyworkds in creation of run