-
Notifications
You must be signed in to change notification settings - Fork 1
/
train_sep.py
153 lines (126 loc) · 4.54 KB
/
train_sep.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
from my_model import NeiVar,Recon
from torch.optim import Adam
import torch
import torch_geometric.utils as utils
from sklearn.metrics import roc_auc_score
from transform import NormalizeToOne,standScale,minMaxScale
import numpy as np
from torch_geometric.transforms import NormalizeFeatures
from sklearn.preprocessing import StandardScaler
import argparse
from load_data import load_mat,load_weibo
from torch_geometric.nn import GIN,GAT
import os
os.environ['PYTORCH_CUDA_ALLOC_CONF']="max_split_size_mb:1000"
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default='cora')
# parser.add_argument('--y', type=int, default=1)
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--str-epoch', type=int, default=10)
# parser.add_argument('--lr', type=float, default=0.005)
# learning rate for datasets : weibo 0.01, else 0.005
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if args.data == 'weibo':
data = load_weibo()
lr = 0.01
else:
data = load_mat(args.data)
lr = 0.005
y = data.y.numpy()
str_y = data.str_y.numpy()
attr_y = data.attr_y.numpy()
if args.data == 'weibo':
trans = NormalizeFeatures()
data = trans(data)
# pass
# data = NormalizeToOne(data)
# data = standScale(data)
print('norm attr score',roc_auc_score(data.attr_y.numpy(),torch.norm(data.x,dim=-1).numpy()))
print('norm score',roc_auc_score(data.y.numpy(),torch.norm(data.x,dim=-1).numpy()))
data = data.to(device)
print(f'finish load {args.data}')
# y = (data.y==1).cpu().numpy() # binary labels (inl
edge_index = data.edge_index
# edge_index = utils.add_self_loops(edge_index)[0]
if args.data != 'Flickr':
edge_index = utils.add_self_loops(edge_index)[0]
else:
data = standScale(data.to('cpu')).to(device)
input_dim = data.x.size(1)
emb_dim = 128
lr =lr
num_epoch = args.epoch
alpha = 1
# model = CooTrain(input_dim,emb_dim).to(device)
struct_model = NeiVar(input_dim,emb_dim).to(device)
if args.data == 'weibo':
GNN = GAT
else:
GNN = GIN
context_model = Recon(input_dim,emb_dim,GNN).to(device)
str_opt = Adam(struct_model.parameters(),lr=lr,weight_decay=0.0001)
attr_opt =Adam(context_model.parameters(),lr=lr,weight_decay=0.0001)
def add_two_score(score1,score2):
score1 = score1/np.sum(score1)
score2 = score2/np.sum(score2)
return score1 + score2
def std_scale(x):
x = (x - np.mean(x))/np.std(x)
return x
def add_two_score_std(score1,score2):
score1 = std_scale(score1)
score2 = std_scale(score2)
return score1 + score2 * alpha
@torch.no_grad()
def eval_model():
global y
struct_model.eval()
context_model.eval()
score_recon= context_model(data.x,edge_index)
score_var = struct_model(data.x,edge_index)
score_recon,score_var =score_recon.cpu().detach().numpy(),score_var.cpu().detach().numpy()
y = y.reshape(score_recon.shape)
score = add_two_score_std(score_recon,score_var)
return roc_auc_score(y,score),roc_auc_score(str_y,score), \
roc_auc_score(attr_y,score)
def loss_recon_fn(recon_loss):
return torch.mean(recon_loss)
def loss_var_fn(pos_loss,neg_loss):
return torch.mean(pos_loss) - torch.mean(neg_loss)
def train(e):
var_loss = 0
if args.str_epoch > e:
struct_model.train()
neg_edge = utils.negative_sampling(edge_index,num_neg_samples=edge_index.size(1) )
pos_loss = struct_model(data.x,edge_index)
neg_loss= struct_model(data.x,neg_edge)
var_loss = loss_var_fn(pos_loss,neg_loss)
# else:
# print(list(struct_model.parameters()))
context_model.train()
recon_loss = context_model(data.x,edge_index)
recon_loss = loss_recon_fn(recon_loss)
# loss = recon_loss + alpha * var_loss
attr_opt.zero_grad()
recon_loss.backward()
attr_opt.step()
if args.str_epoch > e:
str_opt.zero_grad()
var_loss.backward()
str_opt.step()
return float(recon_loss + alpha * var_loss)
print('begin train')
for e in range(num_epoch):
train_loss = train(e)
test_auc = eval_model()
print(f'Epoch: {e}, trainLoss: {train_loss}, auc: {test_auc[0]:.3f}'
f', str_auc: {test_auc[1]:.3f}, attr_auc: {test_auc[-1]:.3f}')
# with torch.no_grad():
# struct_model.eval()
# context_model.eval()
# score_recon= context_model(data.x,edge_index)
# score_var = struct_model(data.x,edge_index)
# score_recon,score_var =score_recon.cpu().detach().numpy(),score_var.cpu().detach().numpy()
# score = add_two_score_std(score_recon,score_var)
# np.save(f'./results/VGOD_{args.data}',score)