def predict(self):
logger = logging.getLogger("brc")
logger.info('Load data_set and vocab...')
with open(os.path.join(self.config.get_filepath().vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(self.test_files) > 0, 'No test files are provided.'
dataloader = Propress(self.config.get_default_params().max_p_num,
self.config.get_default_params().max_p_len,
self.config.get_default_params().max_q_len,
self.config.get_default_params().max_ch_len,
test_files=self.test_files)
logger.info('Converting text into ids...')
dataloader.convert_to_ids(vocab)
logger.info('Restoring the model...')
model = Model(vocab, trainable=False)
model.restore(self.config.get_filepath().model_dir, self.algo)
logger.info('Predicting answers for test set...')
test_batches = dataloader.next_batch('test',
self.config.get_default_params().batch_size,
vocab.get_word_id(vocab.pad_token),
vocab.get_char_id(vocab.pad_token),
shuffle=False)
model.evaluate(test_batches, 'test', result_dir=self.config.get_filepath().output_dir, result_prefix='test.predicted')
def train(self):
logger = logging.getLogger("brc")
logger.info("====== training ======")
logger.info('Load data_set and vocab...')
with open(os.path.join(self.config.get_filepath().vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
# print(vocab.get_char_size())
brc_data = Propress(self.config.get_default_params().max_p_num,
self.config.get_default_params().max_p_len,
self.config.get_default_params().max_q_len,
self.config.get_default_params().max_ch_len,
train_files=self.dev_files, dev_files=self.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Initialize the model...')
rc_model = Model(vocab, trainable=True)
logger.info('Training the model...')
rc_model.train(brc_data,
self.config.get_default_params().epoch,
self.config.get_default_params().batch_size,
save_dir=self.config.get_filepath().model_dir,
save_prefix=self.algo)
logger.info('====== Done with model training! ======')
def evaluate(self):
logger = logging.getLogger("brc")
logger.info("====== evaluating ======")
logger.info('Load data_set and vocab...')
with open(os.path.join(self.config.get_filepath().vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(self.dev_files) > 0, 'No dev files are provided.'
dataloader = Propress(self.config.get_default_params().max_p_num,
self.config.get_default_params().max_p_len,
self.config.get_default_params().max_q_len,
self.config.get_default_params().max_ch_len,
dev_files=self.dev_files)
logger.info('Converting text into ids...')
dataloader.convert_to_ids(vocab)
logger.info('Restoring the model...')
model = Model(vocab, trainable=False)
model.restore(self.config.get_filepath().model_dir, self.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = dataloader.next_batch('dev',
self.config.get_default_params().batch_size,
vocab.get_id_byword(vocab.pad_token),
vocab.get_id_bychar(vocab.pad_token),
shuffle=False)
dev_loss, dev_bleu_rouge, summ = model.evaluate(
dev_batches, 'dev', result_dir=self.config.get_filepath().output_dir, result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(os.path.join(self.config.get_filepath().output_dir)))
def evaluate(args):
logger = logging.getLogger("brc")
logger.info("====== evaluating ======")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert len(args.dev_files) > 0, 'No dev files are provided.'
dataloader = Propress(args.max_p_num,
args.max_p_len,
args.max_q_len,
args.max_ch_len,
dev_files=args.dev_files)
logger.info('Converting text into ids...')
dataloader.convert_to_ids(vocab)
logger.info('Restoring the model...')
model = Model(vocab, args)
model.restore(args.model_dir, args.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = dataloader.next_batch('dev',
args.batch_size,
vocab.get_word_id(vocab.pad_token),
vocab.get_char_id(vocab.pad_token),
shuffle=False)
dev_loss, dev_bleu_rouge = model.evaluate(dev_batches,
result_dir=args.result_dir,
result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(
os.path.join(args.result_dir)))
def train(args):
logger = logging.getLogger("QANet")
logger.info("====== training ======")
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
dataloader = Propress(args.max_p_num, args.max_p_len, args.max_q_len,
args.max_ch_len, args.train_files, args.dev_files)
logger.info('Converting text into ids...')
dataloader.convert_to_ids(vocab)
logger.info('Initialize the model...')
model = Model(vocab, args)
logger.info('Training the model...')
model.train(dataloader,
args.epochs,
args.batch_size,
save_dir=args.model_dir,
save_prefix=args.algo,
dropout=args.dropout)
logger.info('====== Done with model training! ======')
def prepro(args):
logger = logging.getLogger("brc")
logger.info("====== preprocessing ======")
logger.info('Checking the data files...')
for data_path in args.train_files + args.dev_files + args.test_files:
assert os.path.exists(data_path), '{} file does not exist.'.format(
data_path)
logger.info('Preparing the directories...')
for dir_path in [
args.vocab_dir, args.model_dir, args.result_dir, args.summary_dir
]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
logger.info('Building vocabulary...')
dataloader = Propress(args.max_p_num, args.max_p_len, args.max_q_len,
args.max_ch_len, args.train_files, args.dev_files,
args.test_files)
vocab = Vocab(lower=True)
for word in dataloader.word_iter('train'):
vocab.add_word(word)
[vocab.add_char(ch) for ch in word]
unfiltered_vocab_size = vocab.get_vocab_size()
vocab.filter_words_by_cnt(min_cnt=2)
filtered_num = unfiltered_vocab_size - vocab.get_vocab_size()
logger.info(
'After filter {} tokens, the final vocab size is {}, char size is{}'.
format(filtered_num, vocab.get_vocab_size(), vocab.get_char_size()))
unfiltered_vocab_char_size = vocab.get_char_size()
vocab.filter_chars_by_cnt(min_cnt=2)
filtered_char_num = unfiltered_vocab_char_size - vocab.get_char_size()
logger.info(
'After filter {} chars, the final char vocab size is {}'.format(
filtered_char_num, vocab.get_char_size()))
logger.info('Assigning embeddings...')
if args.pretrained_word_path is not None:
vocab.load_pretrained_word_embeddings(args.pretrained_word_path)
else:
vocab.randomly_init_word_embeddings(args.word_embed_size)
if args.pretrained_char_path is not None:
vocab.load_pretrained_char_embeddings(args.pretrained_char_path)
else:
vocab.randomly_init_char_embeddings(args.char_embed_size)
logger.info('Saving vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
pickle.dump(vocab, fout)
logger.info('====== Done with preparing! ======')
def prepare(self):
logger = logging.getLogger("brc")
logger.info("====== preprocessing ======")
logger.info('Checking the data files...')
for data_path in self.train_files + self.dev_files + self.test_files:
assert os.path.exists(data_path), '{} file does not exist.'.format(data_path)
logger.info('Preparing the directories...')
for dir_path in [self.config.get_filepath().vocab_dir,
self.config.get_filepath().model_dir,
self.config.get_filepath().output_dir,
self.config.get_filepath().summary_dir]:
if not os.path.exists(dir_path):
os.makedirs(dir_path)
logger.info('Building vocabulary...')
brc_data = Propress(self.config.get_default_params().max_p_num,
self.config.get_default_params().max_p_len,
self.config.get_default_params().max_q_len,
self.config.get_default_params().max_ch_len,
train_files=self.train_files, dev_files=self.dev_files, test_files=self.test_files)
vocab = Vocab(lower=True)
# 遍历 question_tokens 和 passage里面的passage_token
for word in brc_data.word_iter('train'):
vocab.add_word(word)
[vocab.add_char(ch) for ch in word]
unfiltered_vocab_size = vocab.get_vocab_size()
vocab.filter_words_by_cnt(min_cnt=2)
filtered_num = unfiltered_vocab_size - vocab.get_vocab_size()
logger.info('After filter {} tokens, the final vocab size is {}'.format(filtered_num, vocab.get_vocab_size()))
unfiltered_vocab_char_size = vocab.get_char_size()
vocab.filter_chars_by_cnt(min_cnt=2)
filtered_char_num = unfiltered_vocab_char_size - vocab.get_char_size()
logger.info('After filter {} chars, the final char vocab size is {}'.format(filtered_char_num,
vocab.get_char_size()))
logger.info('Assigning embeddings...')
pretrained_word_path = self.config.get_filepath().vector_file
pretrained_char_path = self.config.get_filepath().char_vector_file
# 读入预训练的词向量
if pretrained_word_path is not None:
vocab.load_pretrained_word_embeddings(pretrained_word_path)
else:
vocab.randomly_init_word_embeddings(self.config.get_default_params().word_embed_size)
# 读入预训练的字向量
if pretrained_char_path is not None:
vocab.load_pretrained_char_embeddings(pretrained_char_path)
else:
vocab.randomly_init_char_embeddings(self.config.get_default_params().char_embed_size)
logger.info('save word vocab size is {}'.format(vocab.get_vocab_size()))
logger.info('save char vocab size is {}'.format(vocab.get_char_size()))
logger.info('Saving vocab...')
with open(os.path.join(self.config.get_filepath().vocab_dir, 'vocab.data'), 'ab') as fout:
pickle.dump(vocab, fout)
logger.info('====== Done with preparing! ======')