def load(train_file='example.train',
dev_file='example.dev',
test_file='example.test',
lower=True,
zeros=True,
tag_schema='iobes',
map_file='map.pkl',
pre_emb=True,
emb_file='wiki_100.utf8'):
train_file = get_data_path(train_file)
dev_file = get_data_path(dev_file)
test_file = get_data_path(test_file)
map_file = get_data_path(map_file)
emb_file = get_data_path(emb_file)
train_sentences = load_sentences(train_file, lower, zeros)
dev_sentences = load_sentences(dev_file, lower, zeros)
test_sentences = load_sentences(test_file, lower, zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, tag_schema)
update_tag_scheme(test_sentences, tag_schema)
# create maps if not exist
if not os.path.isfile(map_file):
# create dictionary for word
if pre_emb:
dico_chars_train = char_mapping(train_sentences, lower)[0]
dico_chars, char_to_id, id_to_char = augment_with_pretrained(
dico_chars_train.copy(), emb_file,
list(
itertools.chain.from_iterable([[w[0] for w in s]
for s in test_sentences])))
else:
_c, char_to_id, id_to_char = char_mapping(train_sentences, lower)
# Create a dictionary and a mapping for tags
_t, tag_to_id, id_to_tag = tag_mapping(train_sentences)
with open(map_file, "wb") as f:
pickle.dump([char_to_id, id_to_char, tag_to_id, id_to_tag], f)
else:
with open(map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
# prepare data, get a collection of list containing index
train_data = prepare_dataset(train_sentences, char_to_id, tag_to_id, lower)
dev_data = prepare_dataset(dev_sentences, char_to_id, tag_to_id, lower)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id, lower)
print("%i / %i / %i sentences in train / dev / test." %
(len(train_data), 0, len(test_data)))
return train_data, dev_data, test_data, char_to_id, tag_to_id, id_to_char, id_to_tag
def evaluate_test():
config = load_config(args.config_file)
logger = get_logger(args.log_file)
with open(args.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag, intent_to_id, id_to_intent = pickle.load(
f)
test_sentences = load_sentences(args.test_file, args.lower, args.zeros)
update_tag_scheme(test_sentences, args.tag_schema)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
intent_to_id, args.lower)
test_manager = BatchManager(test_data, 100)
# limit GPU memory 限制GPU的内存大小
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, args.ckpt_path, load_word2vec,
config, id_to_char, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
def predict_sentences_given_model(sentences_string, model):
"""
:type sentences_string: string
:type model: MainTaggerModel
:param model:
Mappings must be loaded.
"""
from utils import tokenize_sentences_string
from utils.loader import load_sentences, prepare_dataset
tokenized_sentences = tokenize_sentences_string(sentences_string)
# print tokenized_sentences
from utils.morph_analyzer_caller import get_morph_analyzes, create_single_word_single_line_format
# "\n".join([" ".join(x) for x in tokenized_sentences])
dataset_file_string = ""
morph_analyzer_output_for_all_sentences = ""
for tokenized_sentence in tokenized_sentences:
morph_analyzer_output_for_a_single_sentence = get_morph_analyzes(
" ".join(tokenized_sentence))
morph_analyzer_output_for_all_sentences += morph_analyzer_output_for_a_single_sentence + "\n"
# print string_output
dataset_file_string += create_single_word_single_line_format(
morph_analyzer_output_for_a_single_sentence,
conll=True,
for_prediction=True)
dataset_file_string = dataset_file_string.decode('iso-8859-9')
# import sys
# sys.exit(1)
# print sentences_data_string.split("\n")
# We now have the input sentences in our native format
train_sentences, _, _ = load_sentences(dataset_file_string.split("\n"),
model.parameters["zeros"])
char_to_id, id_to_char, id_to_morpho_tag, id_to_tag, id_to_word, morpho_tag_to_id, tag_to_id, word_to_id = \
extract_mapping_dictionaries_from_model(model)
_, _, _, sentences_data = prepare_dataset(
train_sentences,
word_to_id,
char_to_id,
tag_to_id,
morpho_tag_to_id,
model.parameters['lower'],
model.parameters['mt_d'],
model.parameters['mt_t'],
model.parameters['mt_ci'],
morpho_tag_separator=("+" if model.parameters['lang_name'] == "turkish"
else "|"))
f_scores, morph_accuracies, labeled_sentences = \
predict_tags_given_model_and_input([('tagger_output', sentences_data)],
model,
return_result=True)
print(labeled_sentences)
return labeled_sentences, dataset_file_string
def train_ner():
clean(FLAGS)
# load data sets
train_sentences = load_sentences(FLAGS.train_file, FLAGS.lower,
FLAGS.zeros)
dev_sentences = load_sentences(FLAGS.dev_file, FLAGS.lower, FLAGS.zeros)
test_sentences = load_sentences(FLAGS.test_file, FLAGS.lower, FLAGS.zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, FLAGS.tag_schema)
update_tag_scheme(test_sentences, FLAGS.tag_schema)
update_tag_scheme(dev_sentences, FLAGS.tag_schema)
# create maps if not exist
if not os.path.isfile(FLAGS.map_file):
# create dictionary for word
if FLAGS.pre_emb:
dico_chars_train = char_mapping(train_sentences, FLAGS.lower)[0]
dico_chars, char_to_id, id_to_char = augment_with_pretrained(
dico_chars_train.copy(), FLAGS.emb_file,
list(
itertools.chain.from_iterable([[w[0] for w in s]
for s in test_sentences])))
else:
_c, char_to_id, id_to_char = char_mapping(train_sentences,
FLAGS.lower)
# Create a dictionary and a mapping for tags
_t, tag_to_id, id_to_tag = tag_mapping(train_sentences)
with open(FLAGS.map_file, "wb") as f:
pickle.dump([char_to_id, id_to_char, tag_to_id, id_to_tag], f)
else:
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
# prepare data, get a collection of list containing index
train_data = prepare_dataset(train_sentences, char_to_id, tag_to_id,
FLAGS.lower)
dev_data = prepare_dataset(dev_sentences, char_to_id, tag_to_id,
FLAGS.lower)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
FLAGS.lower)
print("%i / %i / %i sentences in train / dev / test." %
(len(train_data), 0, len(test_data)))
train_manager = BatchManager(train_data, FLAGS.batch_size)
dev_manager = BatchManager(dev_data, 100)
test_manager = BatchManager(test_data, 100)
# make path for store log and model if not exist
make_path(FLAGS)
if os.path.isfile(FLAGS.config_file):
config = load_config(FLAGS.config_file)
else:
config = config_model(char_to_id, tag_to_id)
save_config(config, FLAGS.config_file)
make_path(FLAGS)
log_path = os.path.join("log", FLAGS.log_file)
logger = get_logger(log_path)
print_config(config, logger)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
steps_per_epoch = train_manager.len_data
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
logger.info("start training")
loss = []
for i in range(25):
for batch in train_manager.iter_batch(shuffle=True):
step, batch_loss = model.run_step(sess, True, batch)
loss.append(batch_loss)
if step % FLAGS.steps_check == 0:
iteration = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration, step % steps_per_epoch,
steps_per_epoch, np.mean(loss)))
loss = []
best = evaluate(sess, model, "dev", dev_manager, id_to_tag, logger)
if best:
save_model(sess, model, FLAGS.ckpt_path, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
def train():
# load data sets
train_sentences = load_sentences(args.train_file, args.lower, args.zeros)
dev_sentences = load_sentences(args.dev_file, args.lower, args.zeros)
test_sentences = load_sentences(args.test_file, args.lower, args.zeros)
# Use selected tagging scheme (IOB / IOBES)
# 检测并维护数据集的 tag 标记
update_tag_scheme(train_sentences, args.tag_schema)
update_tag_scheme(test_sentences, args.tag_schema)
update_tag_scheme(dev_sentences, args.tag_schema)
# create maps if not exist
# 根据数据集创建 char_to_id, id_to_char, tag_to_id, id_to_tag 字典，并储存为 pkl 文件
if not os.path.isfile(args.map_file):
# create dictionary for word
if args.pre_emb:
dico_chars_train = char_mapping(train_sentences, args.lower)[0]
# 利用预训练嵌入集增强（扩充）字符字典，然后返回字符与位置映射关系
dico_chars, char_to_id, id_to_char = augment_with_pretrained(
dico_chars_train.copy(), args.emb_file,
list(
itertools.chain.from_iterable([[w[0] for w in s]
for s in test_sentences])))
else:
_c, char_to_id, id_to_char = char_mapping(train_sentences,
args.lower)
# Create a dictionary and a mapping for tags
# 获取标记与位置映射关系
tag_to_id, id_to_tag, intent_to_id, id_to_intent = tag_mapping(
train_sentences)
with open(args.map_file, "wb") as f:
pickle.dump([
char_to_id, id_to_char, tag_to_id, id_to_tag, intent_to_id,
id_to_intent
], f)
else:
with open(args.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag, intent_to_id, id_to_intent = pickle.load(
f)
# 提取句子特征
# prepare data, get a collection of list containing index
train_data = prepare_dataset(train_sentences, char_to_id, tag_to_id,
intent_to_id, args.lower)
dev_data = prepare_dataset(dev_sentences, char_to_id, tag_to_id,
intent_to_id, args.lower)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
intent_to_id, args.lower)
# code.interact(local=locals())
print("%i / %i / %i sentences in train / dev / test." %
(len(train_data), len(dev_data), len(test_data)))
# 获取可供模型训练的单个批次数据
train_manager = BatchManager(train_data, args.batch_size)
dev_manager = BatchManager(dev_data, 100)
test_manager = BatchManager(test_data, 100)
# make path for store log and model if not exist
make_path(args)
if os.path.isfile(args.config_file):
config = load_config(args.config_file)
else:
config = config_model(char_to_id, tag_to_id, intent_to_id)
save_config(config, args.config_file)
make_path(args)
logger = get_logger(args.log_file)
print_config(config, logger)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
# 训练集全量跑一次需要迭代的次数
steps_per_epoch = train_manager.len_data
with tf.Session(config=tf_config) as sess:
# 此处模型创建为项目最核心代码
model = create_model(sess, Model, args.ckpt_path, load_word2vec,
config, id_to_char, logger)
logger.info("start training")
loss_slot = []
loss_intent = []
# with tf.device("/gpu:0"):
for i in range(100):
for batch in train_manager.iter_batch(shuffle=True):
step, batch_loss_slot, batch_loss_intent = model.run_step(
sess, True, batch)
loss_slot.append(batch_loss_slot)
loss_intent.append(batch_loss_intent)
if step % args.steps_check == 0:
iteration = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"INTENT loss:{:>9.6f}, "
"NER loss:{:>9.6f}".format(
iteration, step % steps_per_epoch,
steps_per_epoch, np.mean(loss_intent),
np.mean(loss_slot)))
loss_slot = []
loss_intent = []
best = evaluate(sess, model, "dev", dev_manager, id_to_tag, logger)
if best:
# if i%7 == 0:
save_model(sess, model, args.ckpt_path, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
def train():
tf.io.gfile.mkdir(FLAGS.output)
log_path = os.path.join(FLAGS.output, 'model.log')
logger = get_logger(log_path)
# load data sets
train_sentences = load_sentences(os.path.join(FLAGS.data, "train.txt"),
FLAGS.zeros)
dev_sentences = load_sentences(os.path.join(FLAGS.data, "dev.txt"),
FLAGS.zeros)
test_sentences = load_sentences(os.path.join(FLAGS.data, "test.txt"),
FLAGS.zeros)
# create maps if not exist
map_file = os.path.join(FLAGS.output, 'maps.pkl')
if not os.path.isfile(map_file):
# Create a dictionary and a mapping for tags
_t, tag_to_id, id_to_tag = tag_mapping(train_sentences)
with open(map_file, "wb") as f:
pickle.dump([tag_to_id, id_to_tag], f)
else:
with open(map_file, "rb") as f:
tag_to_id, id_to_tag = pickle.load(f)
# prepare data, get a collection of list containing index
train_data = prepare_dataset(train_sentences, FLAGS.max_seq_len, tag_to_id)
dev_data = prepare_dataset(dev_sentences, FLAGS.max_seq_len, tag_to_id)
test_data = prepare_dataset(test_sentences, FLAGS.max_seq_len, tag_to_id)
logger.info("%i / %i / %i sentences in train / dev / test." %
(len(train_data), len(dev_data), len(test_data)))
train_manager = BatchManager(train_data, FLAGS.batch_size)
dev_manager = BatchManager(dev_data, FLAGS.batch_size)
test_manager = BatchManager(test_data, FLAGS.batch_size)
# make path for store log and model if not exist
config_file = os.path.join(FLAGS.output, 'config.json')
if os.path.isfile(config_file):
config = load_config(config_file)
else:
config = config_model(tag_to_id)
save_config(config, config_file)
print_config(config, logger)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
steps_per_epoch = train_manager.len_data
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model,
os.path.join(FLAGS.output, 'checkpoint'), config,
logger)
logger.info("start training")
loss = []
for i in range(100):
for batch in train_manager.iter_batch(shuffle=True):
step, batch_loss = model.run_step(sess, True, batch)
loss.append(batch_loss)
if step % FLAGS.steps_check == 0:
iteration = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration, step % steps_per_epoch,
steps_per_epoch, np.mean(loss)))
loss = []
best = evaluate(sess, model, "dev", dev_manager, id_to_tag, logger)
if best:
save_model(sess,
model,
os.path.join(FLAGS.output, 'checkpoint'),
logger,
global_steps=step)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
def train():
# load data sets
train_sentences = load_sentences(FLAGS.train_file, FLAGS.lower,
FLAGS.zeros)
dev_sentences = load_sentences(FLAGS.dev_file, FLAGS.lower, FLAGS.zeros)
test_sentences = load_sentences(FLAGS.test_file, FLAGS.lower, FLAGS.zeros)
# Use selected tagging scheme (IOB / IOBES)
#update_tag_scheme(train_sentences, FLAGS.tag_schema)
#update_tag_scheme(test_sentences, FLAGS.tag_schema)
# create maps if not exist
if not os.path.isfile(FLAGS.map_file):
# create dictionary for word
_c, char_to_id, id_to_char = char_mapping(train_sentences, FLAGS.lower)
# Create a dictionary and a mapping for tags
_t, tag_to_id, id_to_tag = tag_mapping(train_sentences)
os.makedirs('%s' % FLAGS.save_path)
with open(FLAGS.map_file, "wb") as f:
pickle.dump([char_to_id, id_to_char, tag_to_id, id_to_tag], f)
else:
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag = pickle.load(f)
# prepare data, get a collection of list containing index
train_data = prepare_padding_dataset(train_sentences, FLAGS.max_seq_len,
char_to_id, tag_to_id, FLAGS.lower)
dev_data = prepare_padding_dataset(dev_sentences, FLAGS.max_seq_len,
char_to_id, tag_to_id, FLAGS.lower)
test_data = prepare_padding_dataset(test_sentences, FLAGS.max_seq_len,
char_to_id, tag_to_id, FLAGS.lower)
print("%i / %i / %i sentences in train / dev / test." %
(len(train_data), len(dev_data), len(test_data)))
train_manager = BatchManager(train_data, FLAGS.batch_size)
dev_manager = BatchManager(dev_data, 100)
test_manager = BatchManager(test_data, 100)
"""
batch = train_manager.batch_data[0]
strings, chars, segs, tags = batch
for chrs in chars:
print(chrs)
for chrs in segs:
print(chrs)
print(tag_to_id)
"""
# make path for store log and model if not exist
make_path(FLAGS)
if os.path.isfile(FLAGS.config_file):
config = load_config(FLAGS.config_file)
else:
config = config_model(char_to_id, tag_to_id)
save_config(config, FLAGS.config_file)
make_path(FLAGS)
log_path = os.path.join(FLAGS.save_path, "log", FLAGS.log_file)
logger = get_logger(log_path)
print_config(config, logger)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
steps_per_epoch = train_manager.len_data
with tf.Session(config=tf_config) as sess:
model = TransformerCRFModel(config, is_training=True)
sess.run(tf.global_variables_initializer())
logger.info("start training")
loss = []
for i in range(100):
for batch in train_manager.iter_batch(shuffle=True):
step, batch_loss = model.run_step(sess, True, batch)
loss.append(batch_loss)
if step % FLAGS.steps_check == 0:
iteration = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration, step % steps_per_epoch,
steps_per_epoch, np.mean(loss)))
loss = []
predict_lists = []
source_tag = []
best_dev_f1 = 0.0
best_test_f1 = 0.0
for batch in dev_manager.iter_batch(shuffle=False):
lengths, logits = model.run_step(sess, False, batch)
_, chars, segs, tags = batch
transition = model.transition.eval(session=sess)
pre_seq = model.predict(logits, transition, lengths)
pre_label = recover_label(pre_seq, lengths, id_to_tag)
"""
for p in range(len(pre_label)):
print(chars[p])
print(pre_label[p])
"""
source_label = recover_label(tags, lengths, id_to_tag)
predict_lists.extend(pre_label)
source_tag.extend(source_label)
train_loss_v = np.round(float(np.mean(loss)), 4)
print('****************************************************')
acc, p, r, f = get_ner_fmeasure(source_tag, predict_lists,
config["tag_schema"])
logger.info('epoch:\t{}\ttrain loss:\t{}\t'.format(
i + 1, train_loss_v))
logger.info('dev acc:\t{}\tp:\t{}\tr:\t{}\tf:\t{}'.format(
acc, p, r, f))
for batch in test_manager.iter_batch(shuffle=False):
lengths, logits = model.run_step(sess, False, batch)
_, chars, segs, tags = batch
transition = model.transition.eval(session=sess)
pre_seq = model.predict(logits, transition, lengths)
pre_label = recover_label(pre_seq, lengths, id_to_tag)
source_label = recover_label(tags, lengths, id_to_tag)
predict_lists.extend(pre_label)
source_tag.extend(source_label)
acc_t, p_t, r_t, f_t = get_ner_fmeasure(source_tag, predict_lists,
config["tag_schema"])
logger.info('test acc:\t{}\tp:\t{}\tr:\t{}\tf:\t{}'.format(
acc_t, p_t, r_t, f_t))
if f > best_dev_f1:
save_model(sess, model, FLAGS.ckpt_path, logger)
best_dev_f1 = f
best_test_f1 = f_t
logger.info(
'save epoch:\t{} model with best dev f1-score'.format(i +
1))
print('****************************************************\n\n')
