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train_wc.py
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train_wc.py
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from __future__ import print_function
import datetime
import time
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
import codecs
from model.crf import *
from model.lm_lstm_crf import *
import model.utils as utils
from model.evaluator import eval_wc
import argparse
import json
import os
import sys
from tqdm import tqdm
import itertools
import functools
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Learning with LM-LSTM-CRF together with Language Model')
parser.add_argument('--rand_embedding', action='store_true', help='random initialize word embedding')
parser.add_argument('--emb_file', default='./embedding/glove.6B.100d.txt', help='path to pre-trained embedding')
parser.add_argument('--train_file', default='./data/ner2003/eng.train.iobes', help='path to training file')
parser.add_argument('--dev_file', default='./data/ner2003/testa.iobes', help='path to development file')
parser.add_argument('--test_file', default='./data/ner2003/eng.testb.iobes', help='path to test file')
parser.add_argument('--gpu', type=int, default=0, help='gpu id')
parser.add_argument('--batch_size', type=int, default=10, help='batch_size')
parser.add_argument('--unk', default='unk', help='unknow-token in pre-trained embedding')
parser.add_argument('--char_hidden', type=int, default=300, help='dimension of char-level layers')
parser.add_argument('--word_hidden', type=int, default=300, help='dimension of word-level layers')
parser.add_argument('--drop_out', type=float, default=0.5, help='dropout ratio')
parser.add_argument('--epoch', type=int, default=200, help='maximum epoch number')
parser.add_argument('--start_epoch', type=int, default=0, help='start point of epoch')
parser.add_argument('--checkpoint', default='./checkpoint/', help='checkpoint path')
parser.add_argument('--caseless', action='store_true', help='caseless or not')
parser.add_argument('--char_dim', type=int, default=30, help='dimension of char embedding')
parser.add_argument('--word_dim', type=int, default=100, help='dimension of word embedding')
parser.add_argument('--char_layers', type=int, default=1, help='number of char level layers')
parser.add_argument('--word_layers', type=int, default=1, help='number of word level layers')
parser.add_argument('--lr', type=float, default=0.01, help='initial learning rate')
parser.add_argument('--lr_decay', type=float, default=0.05, help='decay ratio of learning rate')
parser.add_argument('--fine_tune', action='store_false', help='fine tune the diction of word embedding or not')
parser.add_argument('--load_check_point', default='', help='path previous checkpoint that want to be loaded')
parser.add_argument('--load_opt', action='store_true', help='also load optimizer from the checkpoint')
parser.add_argument('--update', choices=['sgd', 'adam'], default='sgd', help='optimizer choice')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum for sgd')
parser.add_argument('--clip_grad', type=float, default=5.0, help='clip grad at')
parser.add_argument('--small_crf', action='store_false', help='use small crf instead of large crf, refer model.crf module for more details')
parser.add_argument('--mini_count', type=float, default=5, help='thresholds to replace rare words with <unk>')
parser.add_argument('--lambda0', type=float, default=1, help='lambda0')
parser.add_argument('--co_train', action='store_true', help='cotrain language model')
parser.add_argument('--patience', type=int, default=15, help='patience for early stop')
parser.add_argument('--high_way', action='store_true', help='use highway layers')
parser.add_argument('--highway_layers', type=int, default=1, help='number of highway layers')
parser.add_argument('--eva_matrix', choices=['a', 'fa'], default='fa', help='use f1 and accuracy or accuracy alone')
parser.add_argument('--least_iters', type=int, default=50, help='at least train how many epochs before stop')
parser.add_argument('--shrink_embedding', action='store_true', help='shrink the embedding dictionary to corpus (open this if pre-trained embedding dictionary is too large, but disable this may yield better results on external corpus)')
args = parser.parse_args()
if args.gpu >= 0:
torch.cuda.set_device(args.gpu)
print('setting:')
print(args)
# load corpus
print('loading corpus')
with codecs.open(args.train_file, 'r', 'utf-8') as f:
lines = f.readlines()
with codecs.open(args.dev_file, 'r', 'utf-8') as f:
dev_lines = f.readlines()
with codecs.open(args.test_file, 'r', 'utf-8') as f:
test_lines = f.readlines()
dev_features, dev_labels = utils.read_corpus(dev_lines)
test_features, test_labels = utils.read_corpus(test_lines)
if args.load_check_point:
if os.path.isfile(args.load_check_point):
print("loading checkpoint: '{}'".format(args.load_check_point))
checkpoint_file = torch.load(args.load_check_point)
args.start_epoch = checkpoint_file['epoch']
f_map = checkpoint_file['f_map']
l_map = checkpoint_file['l_map']
c_map = checkpoint_file['c_map']
in_doc_words = checkpoint_file['in_doc_words']
train_features, train_labels = utils.read_corpus(lines)
else:
print("no checkpoint found at: '{}'".format(args.load_check_point))
else:
print('constructing coding table')
# converting format
train_features, train_labels, f_map, l_map, c_map = utils.generate_corpus_char(lines, if_shrink_c_feature=True, c_thresholds=args.mini_count, if_shrink_w_feature=False)
f_set = {v for v in f_map}
f_map = utils.shrink_features(f_map, train_features, args.mini_count)
if args.rand_embedding:
print("embedding size: '{}'".format(len(f_map)))
in_doc_words = len(f_map)
else:
dt_f_set = functools.reduce(lambda x, y: x | y, map(lambda t: set(t), dev_features), f_set)
dt_f_set = functools.reduce(lambda x, y: x | y, map(lambda t: set(t), test_features), dt_f_set)
print("feature size: '{}'".format(len(f_map)))
print('loading embedding')
if args.fine_tune: # which means does not do fine-tune
f_map = {'<eof>': 0}
f_map, embedding_tensor, in_doc_words = utils.load_embedding_wlm(args.emb_file, ' ', f_map, dt_f_set, args.caseless, args.unk, args.word_dim, shrink_to_corpus=args.shrink_embedding)
print("embedding size: '{}'".format(len(f_map)))
l_set = functools.reduce(lambda x, y: x | y, map(lambda t: set(t), dev_labels))
l_set = functools.reduce(lambda x, y: x | y, map(lambda t: set(t), test_labels), l_set)
for label in l_set:
if label not in l_map:
l_map[label] = len(l_map)
print('constructing dataset')
# construct dataset
dataset, forw_corp, back_corp = utils.construct_bucket_mean_vb_wc(train_features, train_labels, l_map, c_map, f_map, args.caseless)
dev_dataset, forw_dev, back_dev = utils.construct_bucket_mean_vb_wc(dev_features, dev_labels, l_map, c_map, f_map, args.caseless)
test_dataset, forw_test, back_test = utils.construct_bucket_mean_vb_wc(test_features, test_labels, l_map, c_map, f_map, args.caseless)
dataset_loader = [torch.utils.data.DataLoader(tup, args.batch_size, shuffle=True, drop_last=False) for tup in dataset]
dev_dataset_loader = [torch.utils.data.DataLoader(tup, 50, shuffle=False, drop_last=False) for tup in dev_dataset]
test_dataset_loader = [torch.utils.data.DataLoader(tup, 50, shuffle=False, drop_last=False) for tup in test_dataset]
# build model
print('building model')
ner_model = LM_LSTM_CRF(len(l_map), len(c_map), args.char_dim, args.char_hidden, args.char_layers, args.word_dim, args.word_hidden, args.word_layers, len(f_map), args.drop_out, large_CRF=args.small_crf, if_highway=args.high_way, in_doc_words=in_doc_words, highway_layers = args.highway_layers)
if args.load_check_point:
ner_model.load_state_dict(checkpoint_file['state_dict'])
else:
if not args.rand_embedding:
ner_model.load_pretrained_word_embedding(embedding_tensor)
ner_model.rand_init(init_word_embedding=args.rand_embedding)
if args.update == 'sgd':
optimizer = optim.SGD(ner_model.parameters(), lr=args.lr, momentum=args.momentum)
elif args.update == 'adam':
optimizer = optim.Adam(ner_model.parameters(), lr=args.lr)
if args.load_check_point and args.load_opt:
optimizer.load_state_dict(checkpoint_file['optimizer'])
crit_lm = nn.CrossEntropyLoss()
crit_ner = CRFLoss_vb(len(l_map), l_map['<start>'], l_map['<pad>'])
if args.gpu >= 0:
if_cuda = True
print('device: ' + str(args.gpu))
torch.cuda.set_device(args.gpu)
crit_ner.cuda()
crit_lm.cuda()
ner_model.cuda()
packer = CRFRepack_WC(len(l_map), True)
else:
if_cuda = False
packer = CRFRepack_WC(len(l_map), False)
tot_length = sum(map(lambda t: len(t), dataset_loader))
best_f1 = float('-inf')
best_acc = float('-inf')
track_list = list()
start_time = time.time()
epoch_list = range(args.start_epoch, args.start_epoch + args.epoch)
patience_count = 0
evaluator = eval_wc(packer, l_map, args.eva_matrix)
for epoch_idx, args.start_epoch in enumerate(epoch_list):
epoch_loss = 0
ner_model.train()
for f_f, f_p, b_f, b_p, w_f, tg_v, mask_v, len_v in tqdm(
itertools.chain.from_iterable(dataset_loader), mininterval=2,
desc=' - Tot it %d (epoch %d)' % (tot_length, args.start_epoch), leave=False, file=sys.stdout):
f_f, f_p, b_f, b_p, w_f, tg_v, mask_v = packer.repack_vb(f_f, f_p, b_f, b_p, w_f, tg_v, mask_v, len_v)
ner_model.zero_grad()
scores = ner_model(f_f, f_p, b_f, b_p, w_f)
loss = crit_ner(scores, tg_v, mask_v)
epoch_loss += utils.to_scalar(loss)
if args.co_train:
cf_p = f_p[0:-1, :].contiguous()
cb_p = b_p[1:, :].contiguous()
cf_y = w_f[1:, :].contiguous()
cb_y = w_f[0:-1, :].contiguous()
cfs, _ = ner_model.word_pre_train_forward(f_f, cf_p)
loss = loss + args.lambda0 * crit_lm(cfs, cf_y.view(-1))
cbs, _ = ner_model.word_pre_train_backward(b_f, cb_p)
loss = loss + args.lambda0 * crit_lm(cbs, cb_y.view(-1))
loss.backward()
nn.utils.clip_grad_norm(ner_model.parameters(), args.clip_grad)
optimizer.step()
epoch_loss /= tot_length
# update lr
utils.adjust_learning_rate(optimizer, args.lr / (1 + (args.start_epoch + 1) * args.lr_decay))
# eval & save check_point
if 'f' in args.eva_matrix:
dev_f1, dev_pre, dev_rec, dev_acc = evaluator.calc_score(ner_model, dev_dataset_loader)
if dev_f1 > best_f1:
patience_count = 0
best_f1 = dev_f1
test_f1, test_pre, test_rec, test_acc = evaluator.calc_score(ner_model, test_dataset_loader)
track_list.append(
{'loss': epoch_loss, 'dev_f1': dev_f1, 'dev_acc': dev_acc, 'test_f1': test_f1,
'test_acc': test_acc})
print(
'(loss: %.4f, epoch: %d, dev F1 = %.4f, dev acc = %.4f, F1 on test = %.4f, acc on test= %.4f), saving...' %
(epoch_loss,
args.start_epoch,
dev_f1,
dev_acc,
test_f1,
test_acc))
try:
utils.save_checkpoint({
'epoch': args.start_epoch,
'state_dict': ner_model.state_dict(),
'optimizer': optimizer.state_dict(),
'f_map': f_map,
'l_map': l_map,
'c_map': c_map,
'in_doc_words': in_doc_words
}, {'track_list': track_list,
'args': vars(args)
}, args.checkpoint + 'cwlm_lstm_crf')
except Exception as inst:
print(inst)
else:
patience_count += 1
print('(loss: %.4f, epoch: %d, dev F1 = %.4f, dev acc = %.4f)' %
(epoch_loss,
args.start_epoch,
dev_f1,
dev_acc))
track_list.append({'loss': epoch_loss, 'dev_f1': dev_f1, 'dev_acc': dev_acc})
else:
dev_acc = evaluator.calc_score(ner_model, dev_dataset_loader)
if dev_acc > best_acc:
patience_count = 0
best_acc = dev_acc
test_acc = evaluator.calc_score(ner_model, test_dataset_loader)
track_list.append(
{'loss': epoch_loss, 'dev_acc': dev_acc, 'test_acc': test_acc})
print(
'(loss: %.4f, epoch: %d, dev acc = %.4f, acc on test= %.4f), saving...' %
(epoch_loss,
args.start_epoch,
dev_acc,
test_acc))
try:
utils.save_checkpoint({
'epoch': args.start_epoch,
'state_dict': ner_model.state_dict(),
'optimizer': optimizer.state_dict(),
'f_map': f_map,
'l_map': l_map,
'c_map': c_map,
'in_doc_words': in_doc_words
}, {'track_list': track_list,
'args': vars(args)
}, args.checkpoint + 'cwlm_lstm_crf')
except Exception as inst:
print(inst)
else:
patience_count += 1
print('(loss: %.4f, epoch: %d, dev acc = %.4f)' %
(epoch_loss,
args.start_epoch,
dev_acc))
track_list.append({'loss': epoch_loss, 'dev_acc': dev_acc})
print('epoch: ' + str(args.start_epoch) + '\t in ' + str(args.epoch) + ' take: ' + str(
time.time() - start_time) + ' s')
if patience_count >= args.patience and args.start_epoch >= args.least_iters:
break
#print best
if 'f' in args.eva_matrix:
eprint(args.checkpoint + ' dev_f1: %.4f dev_rec: %.4f dev_pre: %.4f dev_acc: %.4f test_f1: %.4f test_rec: %.4f test_pre: %.4f test_acc: %.4f\n' % (dev_f1, dev_rec, dev_pre, dev_acc, test_f1, test_rec, test_pre, test_acc))
else:
eprint(args.checkpoint + ' dev_acc: %.4f test_acc: %.4f\n' % (dev_acc, test_acc))
# printing summary
print('setting:')
print(args)