-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathtrain.py
181 lines (157 loc) · 7.68 KB
/
train.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
import os
import numpy as np
import pickle
import pandas as pd
import scipy.sparse as sp
import argparse
import torch
import torch.nn.functional as F
from sklearn.metrics import roc_auc_score, average_precision_score
from node2vec import node2Vec_main
from dataset import generate_data
from model import Model
from preprocess import normalize_sym, normalize_row, sparse_mx_to_torch_sparse_tensor
import train_search
parser = argparse.ArgumentParser()
parser.add_argument('--lr', type=float, default=0.006, help='learning rate')
parser.add_argument('--wd', type=float, default=0.09, help='weight decay')
parser.add_argument('--n_hid', type=int, default=64, help='hidden dimension')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--epochs', type=int, default=100, help='number of training epochs')
parser.add_argument('--dropou'
''
't', type=float, default=0.2)
parser.add_argument('--seed', type=int, default=1)
args = parser.parse_args()
prefix = "lr" + str(args.lr) + "_wd" + str(args.wd) + "_h" + str(args.n_hid) + \
"_drop" + str(args.dropout) + "_epoch" + str(args.epochs) + "_cuda" + str(args.gpu)
#Luo_AMG
archs = {
"source":([[6, 1, 0]], [[9, 0, 0]]),
"target": ([[4, 5, 1]], [[7, 1, 13]])
}
#Zheng_AMG
# archs = {
# "source": ([[12, 12, 0]], [[12, 0, 13]]),
# "target": ([[7, 7, 1]], [[12, 1, 13]])
# }
def main():
torch.cuda.set_device(args.gpu)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
archs["source"], archs["target"] = train_search.search_main()
steps_s = [len(meta) for meta in archs["source"][0]]
steps_t = [len(meta) for meta in archs["target"][0]]
datadir = "preprocessed"
prefix = os.path.join(datadir)
#* load data
node_types = np.load(os.path.join(prefix, "node_types.npy"))
num_node_types = node_types.max() + 1
node_types = torch.from_numpy(node_types).cuda()
adjs_offset = pickle.load(open(os.path.join(prefix, "adjs_offset.pkl"), "rb"))
adjs_pt = []
# Luo
for i in range(0, 4):
adjs_pt.append(sparse_mx_to_torch_sparse_tensor(
normalize_row(adjs_offset[str(i)] + sp.eye(adjs_offset[str(i)].shape[0], dtype=np.float32))).cuda())
adjs_pt.append(sparse_mx_to_torch_sparse_tensor(
normalize_row(adjs_offset[str(i)].T + sp.eye(adjs_offset[str(i)].shape[0], dtype=np.float32))).cuda())
for i in range(4, 8):
adjs_pt.append(sparse_mx_to_torch_sparse_tensor(
normalize_sym(adjs_offset[str(i)] + sp.eye(adjs_offset[str(i)].shape[0], dtype=np.float32))).cuda())
# Zheng
# for i in range(0, 5):
# adjs_pt.append(sparse_mx_to_torch_sparse_tensor(
# normalize_row(adjs_offset[str(i)] + sp.eye(adjs_offset[str(i)].shape[0], dtype=np.float32))).cuda())
# adjs_pt.append(sparse_mx_to_torch_sparse_tensor(
# normalize_row(adjs_offset[str(i)].T + sp.eye(adjs_offset[str(i)].shape[0], dtype=np.float32))).cuda())
# for i in range(5, 7):
# adjs_pt.append(sparse_mx_to_torch_sparse_tensor(
# normalize_sym(adjs_offset[str(i)] + sp.eye(adjs_offset[str(i)].shape[0], dtype=np.float32))).cuda())
adjs_pt.append(sparse_mx_to_torch_sparse_tensor(sp.eye(adjs_offset['1'].shape[0], dtype=np.float32).tocoo()).cuda())
adjs_pt.append(torch.sparse.FloatTensor(size=adjs_offset['1'].shape).cuda())
print("Loading {} adjs...".format(len(adjs_pt)))
# embedding
in_dims = []
num_drug = 0
num_target = 0
for k in range(num_node_types):
in_dims.append((node_types == k).sum().item())
if(k == 0):
num_drug = in_dims[-1]
elif (k == 1):
num_target = in_dims[-1]
node_feat = []
data = np.load('./preprocessed/combined_matrices.npz')
for k, d in enumerate(data):
matrix = data[d]
features = node2Vec_main(matrix)
node_feat.append(torch.FloatTensor(features[:matrix.shape[0]]).cuda())
avg_auc = []
avg_aupr = []
#* fold
data_path = './data/Luo/drug_target.dat'
pos_train_fold, pos_val_fold, pos_test_fold, neg_train_fold, neg_val_fold, neg_test_fold = generate_data(2, num_drug, data_path)
for pos_train, pos_val, pos_test, neg_train, neg_val, neg_test in zip(pos_train_fold, pos_val_fold, pos_test_fold, neg_train_fold, neg_val_fold, neg_test_fold):
node_feats = []
dp_matrix = np.zeros((num_drug, num_target), dtype=int)
dp_matrix[pos_train[:, 0], pos_train[:, 1] - num_drug] = 1
dp_matrix[pos_val[:, 0], pos_val[:, 1] - num_drug] = 1
features = node2Vec_main(dp_matrix)
node_feats.append(torch.FloatTensor(features[:dp_matrix.shape[0]]).cuda())
node_feats.append(torch.FloatTensor(features[dp_matrix.shape[0]:]).cuda())
for fea in node_feat:
node_feats.append(fea)
model_s = Model(in_dims, args.n_hid, steps_s, dropout = args.dropout).cuda()
model_t = Model(in_dims, args.n_hid, steps_t, dropout = args.dropout).cuda()
optimizer = torch.optim.Adam(
list(model_s.parameters()) + list(model_t.parameters()),
lr=args.lr,
weight_decay=args.wd
)
auc_best = None
aupr_best = None
for epoch in range(args.epochs):
train_loss = train(node_feats, node_types, adjs_pt, pos_train, neg_train, model_s, model_t, optimizer)
val_loss, auc_test, aupr = infer(node_feats, node_types, adjs_pt, pos_val, neg_val, pos_test, neg_test, model_s, model_t)
if auc_best is None or auc_test > auc_best:
auc_best = auc_test
aupr_best = aupr
avg_auc.append(auc_best)
avg_aupr.append(aupr_best)
print("AUC：{:.3f}".format(auc_best))
print("AUPR：{:.3f}".format(aupr_best))
print("AVG_AUC：{:.3f}".format(np.mean(avg_auc)))
print("AVG_AUPR：{:.3f}".format(np.mean(avg_aupr)))
def train(node_feats, node_types, adjs, pos_train, neg_train, model_s, model_t, optimizer):
model_s.train()
model_t.train()
optimizer.zero_grad()
out_s = model_s(node_feats, node_types, adjs, archs["source"][0], archs["source"][1])
out_t = model_t(node_feats, node_types, adjs, archs["target"][0], archs["target"][1])
loss = - torch.mean(F.logsigmoid(torch.mul(out_s[pos_train[:, 0]], out_t[pos_train[:, 1]]).sum(dim=-1)) + \
F.logsigmoid(- torch.mul(out_s[neg_train[:, 0]], out_t[neg_train[:, 1]]).sum(dim=-1)))
loss.backward()
optimizer.step()
return loss.item()
def infer(node_feats, node_types, adjs, pos_val, neg_val, pos_test, neg_test, model_s, model_t):
model_s.eval()
model_t.eval()
with torch.no_grad():
out_s = model_s(node_feats, node_types, adjs, archs["source"][0], archs["source"][1])
out_t = model_t(node_feats, node_types, adjs, archs["target"][0], archs["target"][1])
pos_val_prod = torch.mul(out_s[pos_val[:, 0]], out_t[pos_val[:, 1]]).sum(dim=-1)
neg_val_prod = torch.mul(out_s[neg_val[:, 0]], out_t[neg_val[:, 1]]).sum(dim=-1)
loss = - torch.mean(F.logsigmoid(pos_val_prod) + F.logsigmoid(- neg_val_prod))
pos_test_prod = torch.mul(out_s[pos_test[:, 0]], out_t[pos_test[:, 1]]).sum(dim=-1)
neg_test_prod = torch.mul(out_s[neg_test[:, 0]], out_t[neg_test[:, 1]]).sum(dim=-1)
y_true_test = np.zeros((pos_test.shape[0] + neg_test.shape[0]), dtype=np.int64)
y_true_test[:pos_test.shape[0]] = 1
y_pred_test = np.concatenate(
(torch.sigmoid(pos_test_prod).cpu().numpy(), torch.sigmoid(neg_test_prod).cpu().numpy()))
auc_test = roc_auc_score(y_true_test, y_pred_test)
aupr = average_precision_score(y_true_test, y_pred_test)
return loss.item(), auc_test, aupr
if __name__ == '__main__':
main()