-
Notifications
You must be signed in to change notification settings - Fork 148
/
Copy pathslice_trainer.py
388 lines (370 loc) · 16.6 KB
/
slice_trainer.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
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
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
import copy
from dgl._ffi.base import DGLError
from openhgnn import sampler
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.sampler import BatchSampler
from torch.utils.data import TensorDataset
from openhgnn.trainerflow.base_flow import BaseFlow
import dgl
from networkx.algorithms.centrality.betweenness import edge_betweenness_centrality
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
from tqdm import tqdm, trange
from sklearn.model_selection import train_test_split
import random
import os
import pickle
import json
import time
from typing import List
import shutil
import copy
import pandas as pd
import math
from sklearn.metrics import (
accuracy_score,
auc,
f1_score,
mean_squared_error,
precision_recall_curve,
precision_recall_fscore_support,
roc_auc_score,
roc_curve,
)
from . import BaseFlow, register_flow
from ..tasks import build_task
from openhgnn.models import build_model
from openhgnn.models.SLiCE import SLiCE, SLiCEFinetuneLayer
from ..utils import extract_embed, EarlyStopping
from ..sampler.SLiCE_sampler import SLiCESampler
@register_flow("slicetrainer")
class SLiCETrainer(BaseFlow):
def __init__(self,args):
super(SLiCETrainer, self).__init__(args)
self.out_dir=args.outdir
self.pretrain_path=os.path.join(self.out_dir,'pretrain/')
self.pretrain_save_path=os.path.join(self.pretrain_path,'best_pretrain_model.pt')
self.finetune_path=os.path.join(self.out_dir,'finetune/')
self.finetune_save_path=os.path.join(self.finetune_path,'best_finetune_model.pt')
self.g=self.task.dataset.g
self.g=dgl.to_homogeneous(self.g,edata=['train_mask','valid_mask','test_mask','label'])
self.model=dict()
self.model['pretrain']=SLiCE.build_model_from_args(self.args,self.g)
self.model['finetune']=SLiCEFinetuneLayer.build_model_from_args(args)
#loss function
self.loss_fn=torch.nn.CrossEntropyLoss()
#optimizer
self.optimizer=dict()
self.optimizer['pretrain']=optim.Adam(self.model['pretrain'].parameters(), lr=args.lr)
self.optimizer['finetune']=optim.Adam(self.model['finetune'].parameters(), lr=args.ft_lr)
self.patience=5 #for early stopping
#number of epochs
self.n_epochs=dict()
self.n_epochs['pretrain']=args.n_epochs
self.n_epochs['finetune']=args.ft_n_epochs
#batch size
self.batch_size=dict()
self.batch_size['pretrain']=args.batch_size
self.batch_size['finetune']=args.ft_batch_size
self.labels = self.g.edata['label']
self.idx=dict()
#pretrain
self.node_subgraphs=dict()
#finetune
self.edges=dict()
self.edges_label=dict()
self.graphs=dict()
self.best_epoch=dict()
self.is_pretrained=False
self.is_finetuned=False
self.threshold=None
def preprocess(self):
if not os.path.exists(self.pretrain_path):
os.makedirs(self.pretrain_path)
if not os.path.exists(self.finetune_path):
os.makedirs(self.finetune_path)
for task in ['train','valid','test']:
#make directories
if not os.path.exists(os.path.join(self.finetune_path,task)):
os.makedirs(os.path.join(self.finetune_path,task))
mask=self.g.edata[task+'_mask']
index = torch.nonzero(mask.squeeze()).squeeze()
if task=='train':
self.idx['train']=index
elif task=='valid':
self.idx['valid']=index
else:
self.idx['test']=index
self.edges[task]=self.g.find_edges(index)
#finally, g should be a graph containing just train_edges
self.graphs[task]=dgl.edge_subgraph(self.g,index)
#sample walks
self.sampler=SLiCESampler(self.g,self.graphs['train'],num_walks_per_node=self.args.n_pred,beam_width=self.args.beam_width,
max_num_edges=self.args.max_length,walk_type=self.args.walk_type,
path_option=self.args.path_option,save_path=self.out_dir)#full graph
sampler=self.sampler
#get dataloader for pretrain and finetune evaluation on link prediction
g=self.g
#pretrain
node_walk_path=self.pretrain_path+'node_walks.bin'
if os.path.exists(node_walk_path):
node_walks,_=dgl.load_graphs(node_walk_path)
else:
node_walks=sampler.get_node_subgraph(g.nodes())
dgl.save_graphs(node_walk_path,node_walks)
random.shuffle(node_walks)
total_len=len(node_walks)
train_size=int(0.8*total_len)
valid_size=int(0.1*total_len)
self.node_subgraphs['train']=node_walks[:train_size]
self.node_subgraphs['valid']=node_walks[train_size:train_size+valid_size]
self.node_subgraphs['test']=node_walks[train_size+valid_size:]
#finetune
src,dst=g.find_edges(self.idx['train'])
self.edges['train']=list(zip(src.tolist(),dst.tolist()))
src,dst=g.find_edges(self.idx['valid'])
self.edges['valid']=list(zip(src.tolist(),dst.tolist()))
src,dst=g.find_edges(self.idx['test'])
self.edges['test']=list(zip(src.tolist(),dst.tolist()))
self.edges_label['train']=list()
self.edges_label['valid']=[int(self.labels[x]) for x in self.idx['valid']]
self.edges_label['test']=[int(self.labels[x]) for x in self.idx['test']]
edges_label=self.edges_label
#generate pretrain subgraph
train_file=os.path.join(self.finetune_path,'train_edges.pickle')
if os.path.exists(train_file):
with open(train_file,'rb') as f:
self.edges['train'],edges_label['train']=pickle.load(f)
else:
self.edges['train'],edges_label['train']=sampler.generate_false_edges2(self.edges['train'],train_file)
self.edges['valid'],self.edges_label['valid']=sampler.shuffle_edge_label(self.edges['valid'],self.edges_label['valid'])
self.edges['test'],self.edges_label['test']=sampler.shuffle_edge_label(self.edges['test'],self.edges_label['test'])
#generate finetune subgraph
for task in ['train','valid','test']:
edges=self.edges[task]
batch_size=self.batch_size['finetune']
n_batch=int(len(edges)/batch_size)
total_len=len(edges)
for batch in range(n_batch):
i=batch*batch_size
if i+batch_size<total_len:
end=i+batch_size
else:
end=total_len
batch_file=os.path.join(self.finetune_path,'{}/edge_subgraph_{}.bin'.format(task,batch))
#pair_file=self.finetune_path+'{}/pair_subgraph_{}.pickle'.format(task,batch)
if not os.path.exists(batch_file):
subgraph_list=self.sampler.get_edge_subgraph(self.edges[task][i:end])
dgl.save_graphs(batch_file,subgraph_list)
def train(self):
self.preprocess()
self.pretrain()
self.finetune()
def pretrain(self):
print("Start Pretraining...")
stopper=EarlyStopping(self.patience)
batch_size=self.batch_size['pretrain']
self.model['pretrain'].train()
self.is_pretrained=True
if os.path.exists(self.pretrain_save_path):
pass
for epoch in range(self.n_epochs['pretrain']):
print("Epoch {}:".format(epoch))
i=0
total_len=len(self.node_subgraphs['train'])
n_batch=math.ceil(total_len/batch_size)
bar=tqdm(range(n_batch))
avg_loss=0
for batch in bar:
i=batch*batch_size
if i+batch_size<total_len:
subgraph_list=self.node_subgraphs['train'][i:i+batch_size]
else:
subgraph_list=self.node_subgraphs['train'][i:]
pred_data,true_data=self.model['pretrain'](subgraph_list)
loss=self.loss_fn(pred_data.transpose(1,2),true_data)
avg_loss+=float(loss)
self.optimizer['pretrain'].zero_grad()
loss.backward()
self.optimizer['pretrain'].step()
i+=batch_size
bar.set_description("Batch {} Loss: {:.3f}".format(batch,loss))
#torch.save(self.model['pretrain'],self.pretrain_path+'model_'+str(ii)+'SLiCE.pt')
avg_loss=avg_loss/n_batch
print("AvgLoss: {:.3f}".format(avg_loss))
early_stop=stopper.loss_step(avg_loss,self.model['pretrain'])
if early_stop:
print('Early Stop!\tEpoch:' + str(epoch))
break
self.best_epoch['pretrain']=epoch
torch.save(self.model['pretrain'].state_dict(),self.pretrain_save_path)
print("Evaluating for pretraining...")
def finetune(self):
if not os.path.exists(self.pretrain_save_path):
print("Model not pretrained!")
else:
ck_pt=torch.load(self.pretrain_save_path)
self.model['pretrain'].load_state_dict(ck_pt)
self.model['pretrain'].eval()
self.model['pretrain'].set_fine_tuning()
self.model['finetune'].train()
print("Start Finetuning...")
stopper=EarlyStopping(self.patience)
batch_size=self.batch_size['finetune']
for epoch in range(self.n_epochs['finetune']):
batch=0
total_len=len(self.edges['train'])
print("Eopch {}:".format(epoch))
n_batch=math.ceil(total_len/batch_size)
bar=tqdm(range(n_batch))
avg_loss=0
for batch in bar:
i=batch*batch_size
if i+batch_size<total_len:
end=i+batch_size
else:
end=total_len
batch_file=os.path.join(self.finetune_path,'{}/edge_subgraph_{}.bin'.format('train',batch))
if os.path.exists(batch_file):
subgraph_list,_=dgl.load_graphs(batch_file)
else:
subgraph_list=self.sampler.get_edge_subgraph(self.edges['train'][i:end])
dgl.save_graphs(batch_file,subgraph_list)
self.model['pretrain'].set_fine_tuning()
with torch.no_grad():
_,layer_output,_=self.model['pretrain'](subgraph_list)
pred_scores,_,_=self.model['finetune'](layer_output)
loss=F.binary_cross_entropy(pred_scores,torch.tensor(self.edges_label['train'][i:end],dtype=torch.float).reshape(-1,1))
bar.set_description('Batch {}: Loss:{:.3f}'.format(batch,loss))
avg_loss+=float(loss)
self.optimizer['finetune'].zero_grad()
loss.backward()
self.optimizer['finetune'].step()
torch.save(self.model['finetune'].state_dict(),self.finetune_path+'model_'+str(epoch)+'SLiCE.pt')
avg_loss=avg_loss/n_batch
print("AvgLoss: {:.3f}".format(avg_loss))
early_stop=stopper.loss_step(avg_loss,self.model['finetune'])
if early_stop:
print('Early Stop!\tEpoch:' + str(epoch))
break
self.model['finetune']=stopper.best_model
torch.save(self.model,self.finetune_save_path)
#run validation to find the best epoch
self.is_finetuned=True
print("Evaluating for pretraining...")
self._test_step()
def _test_step(self):
with torch.no_grad():
#validation and find best threshold
pred_data={'train':[],'valid':[],'test':[]}
true_data={'train':[],'valid':[],'test':[]}
self.model['pretrain'].eval()
for task in ['valid','test']:
total_len=len(self.edges[task])
batch_size=self.batch_size['finetune']
n_batch=int(total_len/batch_size)
for batch in range(n_batch):
i=batch*batch_size
if i+batch_size<total_len:
end=i+batch_size
else:
end=total_len
#get edge subgraphs for test
batch_file=os.path.join(self.finetune_path,'{}/edge_subgraph_{}.bin'.format(task,batch))
if os.path.exists(batch_file):
subgraph_list,_=dgl.load_graphs(batch_file)
else:
subgraph_list=self.sampler.get_edge_subgraph(self.edges[task][i:end])
dgl.save_graphs(batch_file,subgraph_list)
#get score and label
#output: 100*7*200 layer_output: 100*6*7*200
output,layer_output,_=self.model['pretrain'](subgraph_list)
if not self.is_finetuned:
source_embed = output[:, 0, :].unsqueeze(1)
target_embed = output[:, 1, :].unsqueeze(1).transpose(1, 2)
score = torch.bmm(source_embed, target_embed).squeeze(1)#embedding相乘得到相似度分数
score = torch.sigmoid(score).data.cpu().numpy().tolist()
else:
#score:[ft_batch_size,1]
#src_embedding/dst_embedding:[ft_batch_size,1,embedding_dim]
score,_,_=self.model['finetune'](layer_output)
labels=self.edges_label[task][i:end]
for ii, _ in enumerate(score):
pred_data[task].append(float(score[ii][0]))
true_data[task].append(labels[ii])
i+=batch_size
#test and get result
real_true_data=np.array(true_data['valid'],dtype=np.int)
self.threshold=self.get_threshold(real_true_data,pred_data['valid'])[0]
prediction_data=pred_data['test']
sorted_pred = prediction_data[:]
sorted_pred.sort()
# threshold = sorted_pred[-true_num]
y_pred = np.zeros(len(prediction_data), dtype=np.int32)
for i, _ in enumerate(prediction_data):
if prediction_data[i] >= self.threshold:
y_pred[i] = 1
y_true = np.array(true_data['test'])
y_scores = np.array(prediction_data)
ps, rs, _ = precision_recall_curve(y_true, y_scores)
if self.is_finetuned:
header="Finetuning"
else:
header="Pretraining"
print(f"----------------------Testing for {header}()------------")
print(
f"y_true.shape: {y_true.shape}, y_scores.shape: {y_scores.shape}"
f", y_pred.shape: {y_pred.shape}"
)
try:
roc_auc = roc_auc_score(y_true, y_scores)
except ValueError:
roc_auc = 'UNDEFINED'
f1 = f1_score(y_true, y_pred)
auc_value = auc(rs,ps)
print(
f"{header} : ROC-AUC: {roc_auc},"
f" F1: {f1}, AUC: {auc_value}"
)
def get_threshold(self, target, predicted):
fpr, tpr, threshold = roc_curve(target, predicted,pos_label=1)
i = np.arange(len(tpr),dtype=np.int)
roc = pd.DataFrame(
{
"tf": pd.Series(tpr - (1 - fpr), index=i),
"threshold": pd.Series(threshold, index=i),
}
)
print()
roc_t = roc.loc[(roc.tf - 0).abs().argsort()[:1]]
return list(roc_t["threshold"])
def loss_calculation(self, pos_score, neg_score):
# an example hinge loss
loss = []
for i in pos_score:
loss.append(F.logsigmoid(pos_score[i]))
loss.append(F.logsigmoid(-neg_score[i]))
loss = torch.cat(loss)
return -loss.mean()
def ScorePredictor(self, edge_subgraphs, pairs, x):
#x:[batch_size*num_nodes*embed_dim]
score=[]
labels=[]
for ii,edge_subgraph in enumerate(edge_subgraphs):
src_embed=x[ii,0,:]
dst_embed=x[ii,1,:]
score.append(torch.dot(src_embed,dst_embed))
src,dst,label=pairs[ii]
labels.append(label)
score=torch.sigmoid(torch.tensor(score))
res=F.binary_cross_entropy(score,torch.FloatTensor(labels))
return res
def nid_to_id(self,subgraph,src):
for ii,each in enumerate(subgraph.ndata[dgl.NID]):
if each==src:
return ii
return -1