-
Notifications
You must be signed in to change notification settings - Fork 5
/
train_clustering.py
197 lines (172 loc) · 7.43 KB
/
train_clustering.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
from __future__ import division
from __future__ import print_function
import datetime
import logging
import numpy as np
import os
import pickle
import time
import torch
from config import parser
from models.etm import ETM
from models.sawetm import SawETM
from models.hyperminer import HyperETM, HyperMiner, HyperMinerKG
from utils.data_util import get_data_loader
from utils.train_util import get_dir_name, convert_to_coo_adj, load_glove_embeddings, visualize_topics
from utils.eval_util import text_clustering
def main(args):
global save_dir
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if int(args.cuda) >= 0 and torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
device = torch.device('cuda:' + str(args.cuda))
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda)
else:
device = torch.device('cpu')
if int(args.double_precision):
torch.set_default_dtype(torch.float64)
logging.getLogger().setLevel(logging.INFO)
if args.save:
if not args.save_dir:
dt = datetime.datetime.now()
date = f"{dt.year}_{dt.month}_{dt.day}"
models_dir = os.path.join('logs', args.dataset, date)
save_dir = get_dir_name(models_dir)
else:
save_dir = args.save_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
logging.basicConfig(level=logging.INFO,
handlers=[
logging.FileHandler(os.path.join(save_dir, 'log.txt')),
logging.StreamHandler()
])
logging.info('Clustering experiment')
logging.info(f'Using {device}')
logging.info(f'Using seed {args.seed}')
train_loader, vocab = get_data_loader(args.dataset, args.data_path, 'train', args.batch_size)
test_loader, _ = get_data_loader(args.dataset, args.data_path, 'test', args.batch_size, shuffle=False, drop_last=False)
args.vocab_size = len(vocab)
logging.info(f'Using dataset {args.dataset}')
logging.info(f'{len(vocab)} words as vocabulary')
logging.info(f'{len(train_loader.dataset)} training docs')
logging.info(f'{len(test_loader.dataset)} test docs')
if args.pretrained_embeddings:
logging.info('Using pretrained glove embeddings')
initial_embeddings = load_glove_embeddings(args.embed_size, vocab)
else:
initial_embeddings = None
if args.add_knowledge:
with open(args.file_path, 'rb') as f:
adj, num_topics_list, concept_names = pickle.load(f)
num_layers = len(num_topics_list)
args.num_topics_list = num_topics_list
args.num_hiddens_list = args.num_hiddens_list[: num_layers]
sparse_adj = convert_to_coo_adj(adj)
model = HyperMinerKG(args, device, initial_embeddings, sparse_adj)
else:
if args.manifold == 'Euclidean':
model = SawETM(args, device, initial_embeddings)
# model = ETM(args, device, initial_embeddings)
else:
model = HyperMiner(args, device, initial_embeddings)
# model = HyperETM(args, device, initial_embeddings)
model = model.to(device)
logging.info(str(model))
total_params = sum([np.prod(p.size()) for p in model.parameters()])
logging.info(f"Total number of parameters:{total_params}")
optimizer = torch.optim.Adam(
params=model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay
)
logging.info(f'Using {args.optimizer} optimizer')
logging.info(f'Initial learning rate {args.lr}')
if not args.lr_reduce_freq:
args.lr_reduce_freq = args.epochs
else:
logging.info(f'Decay rate {args.gamma}')
logging.info(f'Step size {args.lr_reduce_freq}')
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=int(args.lr_reduce_freq),
gamma=float(args.gamma)
)
best_purity_epoch = 0
best_purity = 0
best_nmi_epoch = 0
best_nmi = 0
# Train model
t_total = time.time()
for epoch in range(args.epochs):
model.train()
t = time.time()
lowerbound = []
likelihood = []
kl_div = []
for idx, (batch_data, _) in enumerate(train_loader):
batch_data = batch_data.float().to(device)
nelbo, nll, kl_loss, _ = model(batch_data)
lowerbound.append(-nelbo.item())
likelihood.append(-nll.item())
kl_div.append(kl_loss.item())
flag = 0
for p in model.parameters():
flag += torch.sum(torch.isnan(p))
if flag == 0:
optimizer.zero_grad()
nelbo.backward()
if args.grad_clip is not None:
max_norm = float(args.grad_clip)
all_params = list(model.parameters())
for param in all_params:
torch.nn.utils.clip_grad_norm_(param, max_norm)
optimizer.step()
if (idx + 1) % 10 == 0:
print('Epoch: [{}/{}]\t elbo: {}\t likelihood: {}\t kl: {}'.format(
idx + 1, epoch + 1, np.mean(lowerbound), np.mean(likelihood), np.mean(kl_div)))
if (epoch + 1) % args.log_freq == 0:
logging.info(" ".join(['Epoch: {:04d}'.format(epoch + 1),
'\tlr: {}'.format(lr_scheduler.get_last_lr()[0]),
'\telbo: {:.8f}'.format(np.mean(lowerbound)),
'\tlikelihood: {:.8f}'.format(np.mean(likelihood)),
'\ttime: {:.4f}s'.format(time.time() - t)
]))
if (epoch + 1) % args.eval_freq == 0:
model.eval()
test_feats = []
test_labels = []
for idx, (batch_data, batch_labels) in enumerate(test_loader):
batch_data = batch_data.float().to(device)
with torch.no_grad():
_, _, _, thetas = model(batch_data, is_training=False)
# test_feats.append(thetas.cpu().numpy()) # use for ETM
test_feats.append(thetas[0].cpu().numpy())
test_labels.append(batch_labels.numpy())
test_feats = np.concatenate(test_feats, axis=0)
test_labels = np.concatenate(test_labels)
purity, nmi = text_clustering(test_feats, test_labels)
logging.info("Epoch: {:04d}\t Purity: {:.6f}\t NMI: {:.6f}".format(epoch + 1, purity, nmi))
if purity > best_purity:
best_purity = purity
best_purity_epoch = epoch + 1
torch.save(
model.state_dict(),
os.path.join(save_dir, 'ckpt_best_purity.pth')
)
if nmi > best_nmi:
best_nmi = nmi
best_nmi_epoch = epoch + 1
torch.save(
model.state_dict(),
os.path.join(save_dir, 'ckpt_best_nmi.pth')
)
lr_scheduler.step()
logging.info("Optimization Finished!")
logging.info("Best clustering purity: {:.6f} at epoch {}".format(best_purity, best_purity_epoch))
logging.info("Best clustering nmi: {:.6f} at epoch {}".format(best_nmi, best_nmi_epoch))
logging.info("Total time elapsed: {:.4f}s".format(time.time() - t_total))
if __name__ == '__main__':
args = parser.parse_args()
main(args)