-
Notifications
You must be signed in to change notification settings - Fork 1
/
preTransfer.py
133 lines (107 loc) · 5.19 KB
/
preTransfer.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
import numpy as np
from utils import build_dataset
import torch
from torch.optim import Adam
from torch.utils.data import DataLoader
from utils.MY_GNN import collate_molgraphs, EarlyStopping, run_a_train_epoch_heterogeneous, \
run_an_eval_epoch_heterogeneous, set_random_seed, MGA, pos_weight, run_an_eval_predmerge, run_an_eval_epoch_heterogeneous2
import time
import pandas as pd
start = time.time()
# fix parameters of model
args = {}
args['device'] = "cuda" if torch.cuda.is_available() else "cpu"
args['atom_data_field'] = 'atom'
args['bond_data_field'] = 'etype'
args['classification_metric_name'] = 'roc_auc'
args['regression_metric_name'] = 'r2'
# model parameter
args['num_epochs'] = 500
args['patience'] = 50
args['batch_size'] = 128
args['mode'] = 'higher'
args['in_feats'] = 40
args['rgcn_hidden_feats'] = [512, 512]
args['classifier_hidden_feats'] = 512
args['rgcn_drop_out'] = 0.2
args['drop_out'] = 0.2
args['lr'] = 3
args['weight_decay'] = 5
args['loop'] = True
# task name (model name)
args['task_name'] = 'Transfer_BCFBAFBMF' # change
args['data_name'] = 'KOWall3' # change
args['times'] = 1
# selected task, generate select task index, task class, and classification_num
args['select_task_list'] = ['BCF','BAF','BMF'] # change
args['select_task_index'] = []
args['classification_num'] = 0
args['regression_num'] = 0
args['all_task_list'] = ['KOW','BCF','BAF','BMF'] # change
# generate select task index
for index, task in enumerate(args['all_task_list']):
if task in args['select_task_list']:
args['select_task_index'].append(index)
# generate classification_num
for task in args['select_task_list']:
if task in []:
args['classification_num'] = args['classification_num'] + 1
if task in ['KOW','BCF','BMF','BAF']:
args['regression_num'] = args['regression_num'] + 1
# generate classification_num
if args['classification_num'] != 0 and args['regression_num'] != 0:
args['task_class'] = 'classification_regression'
if args['classification_num'] != 0 and args['regression_num'] == 0:
args['task_class'] = 'classification'
if args['classification_num'] == 0 and args['regression_num'] != 0:
args['task_class'] = 'regression'
args['bin_path'] = 'data/' + args['data_name'] + '.bin'
args['group_path'] = 'data/' + args['data_name'] + '_group.csv'
result_pd = pd.DataFrame(columns=args['select_task_list']+['group'] + args['select_task_list']+['group']
+ args['select_task_list']+['group'])
all_times_train_result = []
all_times_val_result = []
all_times_test_result = []
for time_id in range(args['times']):
set_random_seed(2020+time_id)
one_time_train_result = []
one_time_val_result = []
one_time_test_result = []
print('***************************************************************************************************')
print('{}, {}/{} time'.format(args['task_name'], time_id+1, args['times']))
print('***************************************************************************************************')
train_set, val_set, test_set, task_number = build_dataset.load_graph_from_csv_bin_for_splited(
bin_path=args['bin_path'],
group_path=args['group_path'],
select_task_index=args['select_task_index']
)
print("Molecule graph generation is complete !")
train_loader = DataLoader(dataset=train_set,
batch_size=args['batch_size'],
shuffle=True,
collate_fn=collate_molgraphs)
val_loader = DataLoader(dataset=val_set,
batch_size=args['batch_size'],
shuffle=True,
collate_fn=collate_molgraphs)
test_loader = DataLoader(dataset=test_set,
batch_size=args['batch_size'],
collate_fn=collate_molgraphs)
pos_weight_np = pos_weight(train_set, classification_num=args['classification_num'])
loss_criterion_c = torch.nn.BCEWithLogitsLoss(reduction='none', pos_weight=pos_weight_np.to(args['device']))
loss_criterion_r = torch.nn.MSELoss(reduction='none')
model = MGA(in_feats=args['in_feats'], rgcn_hidden_feats=args['rgcn_hidden_feats'],
n_tasks=3, rgcn_drop_out=args['rgcn_drop_out'],
classifier_hidden_feats=args['classifier_hidden_feats'], dropout=args['drop_out'],
loop=args['loop'], return_weight=True)
optimizer = Adam(model.parameters(), lr=10 ** -args['lr'], weight_decay=10 ** -args['weight_decay'])
stopper = EarlyStopping(patience=args['patience'],
task_name=args['task_name'], mode=args['mode'])
model.to(args['device'])
stopper.load_checkpoint(model)
# visual test set
#run_an_eval_epoch_heterogeneous_return_weight_py(args, model, test_loader, vis_list=selected_mol_list)
#run_an_eval_epoch_pih(args, model, test_loader, output_path='try2.csv')
run_an_eval_predmerge(args, model, val_loader, output_path='jieguo/'+args['task_name']+'val.csv')
run_an_eval_predmerge(args, model, train_loader, output_path='jieguo/'+args['task_name']+'train.csv')
run_an_eval_predmerge(args, model, test_loader, output_path='jieguo/'+args['task_name']+'test.csv')