parser.add_argument(
"--gaz", default="", nargs="+",
help="gazetteers paths."
)
args = parser.parse_args()
# external features
parameters = dict()
parameters['lower'] = args.lower == 1
parameters['upenn_stem'] = args.upenn_stem
parameters['pos_model'] = args.pos_model
parameters['cluster'] = args.cluster
parameters['ying_stem'] = args.ying_stem
parameters['gaz'] = args.gaz
sentences = load_sentences(args.bio_input)
feats, stem = generate_features(sentences, parameters)
# output bio with features
if feats:
bio = []
for i, s in enumerate(sentences):
bio_s = []
for j, w in enumerate(s):
bio_s.append(' '.join(w[:args.feat_column] + feats[i][j] +
w[args.feat_column:]))
bio.append('\n'.join(bio_s))
with open(args.bio_output, 'w') as f:
f.write('\n\n'.join(bio))
else:
raise Exception('CoNLL evaluation script not found at "%s"' % eval_script)
if not os.path.exists(eval_temp):
os.makedirs(eval_temp)
if not os.path.exists(models_path):
os.makedirs(models_path)
# Initialize model
model = Model(parameters=parameters, models_path=models_path)
print "Model location: %s" % model.model_path
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
# Load sentences
train_sentences = loader.load_sentences(opts.train, lower, zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, tag_scheme)
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
dico_words, word_to_id, id_to_word = word_mapping(train_sentences, lower)
dico_words_train = dico_words
# Create a dictionary and a mapping for words / POS tags / tags
dico_chars, char_to_id, id_to_char = char_mapping(train_sentences)
dico_tags, tag_to_id, id_to_tag = tag_mapping(train_sentences)
# Save the mappings to disk
print 'Saving the mappings to disk...'
# how this work, should have a mapping in disk and load every time?
model.save_mappings(id_to_word, id_to_char, id_to_tag)
logging.basicConfig(format="[%(levelname)s] %(asctime)-15s: %(message)s",
level=logging.INFO)
logger = logging.getLogger
# Initialize model
model = Model(parameters=parameters, models_path=models_path)
logging.info("Model location: %s" % model.model_path)
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
##tag_scheme = parameters['tag_scheme']
# Load sentences
train_sentences = loader.load_sentences(opts.train, lower, zeros)
dev_sentences = loader.load_sentences(opts.dev, lower, zeros)
test_sentences = loader.load_sentences(opts.test, lower, zeros)
# Use selected tagging scheme (IOB / IOBES)
##update_tag_scheme(train_sentences, tag_scheme)
##update_tag_scheme(dev_sentences, tag_scheme)
##update_tag_scheme(test_sentences, tag_scheme)
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
if parameters['pre_emb']:
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(), parameters['pre_emb'], None)
else:
if not os.path.isfile(eval_script):
raise Exception('CoNLL evaluation script not found at "%s"' % eval_script)
if not os.path.exists(eval_temp):
os.makedirs(eval_temp)
if not os.path.exists(models_path):
os.makedirs(models_path)
args.train_crfFeaturesFile = args.train
from thyme_code_oct2016 import preprocessingscript_thyme
preprocessingscript_thyme.loadFeatures2Tokens(args)
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
train_sentences = loader.load_sentences(args.train, lower, zeros)
dev_sentences = loader.load_sentences(args.dev, lower, zeros)
test_sentences = loader.load_sentences(args.test, lower, zeros)
test_train_sentences = loader.load_sentences(args.test_train, lower, zeros)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
update_tag_scheme(test_train_sentences, tag_scheme)
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(), parameters['pre_emb'],
list(
itertools.chain.from_iterable([[w[0] for w in s]
overwrite_mappings=opts.overwrite_mappings)
else:
# Initialize model
model = MainTaggerModel(parameters=parameters, models_path=models_path, overwrite_mappings=opts.overwrite_mappings)
print "MainTaggerModel location: %s" % model.model_path
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['t_s']
max_sentence_lengths = {}
max_word_lengths = {}
# Load sentences
train_sentences, max_sentence_lengths['train'], max_word_lengths['train'] = loader.load_sentences(opts.train, lower, zeros)
dev_sentences, max_sentence_lengths['dev'], max_word_lengths['dev'] = loader.load_sentences(opts.dev, lower, zeros)
test_sentences, max_sentence_lengths['test'], max_word_lengths['test'] = loader.load_sentences(opts.test, lower, zeros)
if parameters['test_with_yuret'] or parameters['train_with_yuret']:
# train.merge and test.merge
yuret_train_sentences, max_sentence_lengths['yuret_train'], max_word_lengths['yuret_train'] = \
loader.load_sentences(opts.yuret_train, lower, zeros)
yuret_test_sentences, max_sentence_lengths['yuret_test'], max_word_lengths['yuret_test'] = \
loader.load_sentences(opts.yuret_test, lower, zeros)
update_tag_scheme(yuret_train_sentences, tag_scheme)
update_tag_scheme(yuret_test_sentences, tag_scheme)
else:
yuret_train_sentences = []
yuret_test_sentences = []
# Initialize model
model = Model(parameters=parameters, models_path=models_path)
print "Model location: %s" % model.model_path
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
train_path = "iob/training_data_iob_" + str(x) + ".txt"
vali_path = "iob/validation_data_iob_" + str(x) + ".txt"
test_path = "iob/test_data_iob_" + str(x) + ".txt"
# Load sentences
train_sentences = loader.load_sentences(train_path, lower, zeros)
dev_sentences = loader.load_sentences(vali_path, lower, zeros)
test_sentences = loader.load_sentences(test_path, lower, zeros)
# are there right tag, just (I, O, B)
#check_tag_scheme(train_sentences, tag_scheme)
#check_tag_scheme(dev_sentences, tag_scheme)
#check_tag_scheme(test_sentences, tag_scheme)
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
if parameters['pre_emb']:
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(),
parameters['pre_emb'],
def train():
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)
#选择标注方式IBO or IBOES
update_tag_scheme(train_sentences, FLAGS.tag_schema)
update_tag_scheme(test_sentences, FLAGS.tag_schema)
if not os.path.isfile(FLAGS.map_file):
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)
_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)
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), 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)
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)
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(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, FLAGS.ckpt_path, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
sys.stdout = Tee(sys.stdout, f)
print('Training data: %s' % args.train)
print('Dev data: %s' % args.dev)
print('Test data: %s' % args.test)
print("Model location: %s" % model_dir)
print('Model parameters:')
for k, v in parameters.items():
print('%s=%s' % (k, v))
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
# Load sentences
train_sentences = load_sentences(args.train, lower, zeros)
dev_sentences = load_sentences(args.dev, lower, zeros)
test_sentences = load_sentences(args.test, lower, zeros)
# train_sentences = train_sentences[:50]
# dev_sentences = dev_sentences[:50]
# test_sentences = test_sentences[:50]
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
if parameters['pre_emb']:
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(), parameters['pre_emb'],
list(
itertools.chain.from_iterable([[w[0] for w in s]
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, load data if exists maps
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)
# 设置训练日志目录
train_log = os.path.join(FLAGS.logdir, "train")
if not os.path.exists(train_log):
os.makedirs(train_log)
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
steps_per_epoch = train_manager.len_data # the nums of batch data
with tf.Session(config=tf_config) as sess:
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
# 观察所建立的计算图
train_writer = tf.summary.FileWriter(train_log, sess.graph)
logger.info("start training")
loss = []
dev_f1 = []
test_f1 = []
for i in range(100):
for batch in train_manager.iter_batch(shuffle=True):
step, batch_loss, merged = model.run_step(
sess, True, batch) # step是global step
# 在迭代中输出到结果
train_writer.add_summary(merged, step)
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 = []
# use dev data to validation the model
best, dev_f1_value = evaluate(sess, model, "dev", dev_manager,
id_to_tag, logger)
# store the dev f1
dev_f1.append(dev_f1_value)
if best:
save_model(sess, model, FLAGS.ckpt_path, logger)
# use current the model to test
_, test_f1_value = evaluate(sess, model, "test", test_manager,
id_to_tag, logger)
# store the test f1
test_f1.append(test_f1_value)
# write the dev_f1 and test_f1 to file
f1_result = {}
f1_result["dev_f1"] = dev_f1
f1_result["test_f1"] = test_f1
write_data_to_file(f1_result, "f1_result")
print 'Reloading previous model...'
_, f_eval = model.build(training=False, **parameters)
model.reload()
print "--------"
print opts.test_folder
arr = os.listdir(opts.test_folder)
print arr.sort()
for test_file in arr:
print test_file
if test_file.endswith(".conll") or test_file.endswith(".txt"):
out_file = test_file.replace(".conll", "") + ".txt"
test_sentences = loader.load_sentences(opts.test_folder + "/" + test_file, lower, zeros)
update_tag_scheme(test_sentences, tag_scheme)
test_data = prepare_dataset2(
test_sentences, word_to_id, char_to_id, tag_to_id, model.feature_maps, lower
)
print "input: ", test_file, ":" , len(test_sentences), len(test_data)
score_file = opts.out_folder + "/" + opts.prefix + out_file
print "output: ", score_file
test_score, iob_test_score, s_result_test, eval_lines, log = evaluate(parameters, f_eval, test_sentences,
test_data, model.id_to_tag, blog=opts.log, eval_script=eval_script)
parameters['all_emb'] = opts.all_emb == 1
parameters['cap_dim'] = opts.cap_dim
parameters['crf'] = opts.crf == 1
parameters['dropout'] = opts.dropout
parameters['reload'] = opts.reload == 1
parameters['name'] = opts.name
parameters['char_mode'] = opts.char_mode
parameters['use_gpu'] = opts.use_gpu == 1 and torch.cuda.is_available()
use_gpu = parameters['use_gpu']
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
train_sentences = loader.load_sentences(opts.train, lower, zeros)
dev_sentences = loader.load_sentences(opts.dev, lower, zeros)
test_sentences = loader.load_sentences(opts.test, lower, zeros)
test_train_sentences = loader.load_sentences(opts.test_train, lower, zeros)
test_single_out_sentences = loader.load_sentences(
"./dataset/origin_single_out.txt", lower, zeros)
name = parameters['name']
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
update_tag_scheme(test_train_sentences, tag_scheme)
update_tag_scheme(test_single_out_sentences, tag_scheme)
dico_words_train = word_mapping(train_sentences, lower)[0]
def train(self,
n_epochs=100,
freq_eval=1000,
verbose=True,
eval_test_set=False):
"""
:param n_epochs: number of epochs over the training set
:param freq_eval: evaluate on dev every freq_eval steps
:return: Saves the model with the best F1-Score, evaluated on the dev set
"""
# Initialize model
model = Model(parameters=self.parameters, models_path=models_path)
print("Model location: %s" % model.model_path)
# Data parameters
lower = self.parameters['lower']
zeros = self.parameters['zeros']
tag_scheme = self.parameters['tag_scheme']
# Load sentences
train_sentences = loader.load_sentences(self.parameters['train'],
lower, zeros)
dev_sentences = loader.load_sentences(self.parameters['dev'], lower,
zeros)
test_sentences = loader.load_sentences(self.parameters['test'], lower,
zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
if self.parameters['pre_emb']:
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(), self.parameters['pre_emb'],
list(
itertools.chain.from_iterable([[w[0] for w in s]
for s in dev_sentences +
test_sentences]))
if not self.parameters['all_emb'] else None)
else:
dico_words, word_to_id, id_to_word = word_mapping(
train_sentences, lower)
dico_words_train = dico_words
# Create a dictionary and a mapping for words / POS tags / tags
dico_chars, char_to_id, id_to_char = char_mapping(train_sentences)
dico_tags, tag_to_id, id_to_tag = tag_mapping(train_sentences)
# Index data
train_data = prepare_dataset(train_sentences, word_to_id, char_to_id,
tag_to_id, lower)
dev_data = prepare_dataset(dev_sentences, word_to_id, char_to_id,
tag_to_id, lower)
test_data = prepare_dataset(test_sentences, word_to_id, char_to_id,
tag_to_id, lower)
print("%i / %i / %i sentences in train / dev / test." %
(len(train_data), len(dev_data), len(test_data)))
# Save the mappings to disk
print('Saving the mappings to disk...')
model.save_mappings(id_to_word, id_to_char, id_to_tag)
# Build the model
f_train, f_eval = model.build(**self.parameters)
# Reload previous model values
if self.parameters['reload']:
print('Reloading previous model...')
model.reload()
#
# Train network
#
singletons = set(
[word_to_id[k] for k, v in dico_words_train.items() if v == 1])
best_dev = -np.inf
best_test = -np.inf
count = 0
for epoch in range(n_epochs):
epoch_costs = []
print("Starting epoch %i at..." % epoch, time.ctime())
for i, index in enumerate(np.random.permutation(len(train_data))):
count += 1
input = create_input(train_data[index], self.parameters, True,
singletons)
new_cost = f_train(*input)
epoch_costs.append(new_cost)
if i % 50 == 0 and i > 0 == 0 and verbose:
print("%i, cost average: %f" %
(i, np.mean(epoch_costs[-50:])))
if count % freq_eval == 0:
dev_score = evaluate(self.parameters,
f_eval,
dev_sentences,
dev_data,
id_to_tag,
verbose=verbose)
if eval_test_set:
test_score = evaluate(self.parameters,
f_eval,
test_sentences,
test_data,
id_to_tag,
verbose=verbose)
print("Score on dev: %.5f" % dev_score)
if eval_test_set:
print("Score on test: %.5f" % test_score)
if dev_score > best_dev:
best_dev = dev_score
print("New best score on dev.")
print("Saving model to disk...")
model.save()
if eval_test_set:
if test_score > best_test:
best_test = test_score
print("New best score on test.")
print(
"Epoch %i done. Average cost: %f. Ended at..." %
(epoch, np.mean(epoch_costs)), time.ctime())
return best_dev
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 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([tag_to_id, id_to_tag], f)
else:
with open(FLAGS.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, FLAGS.lower
)
dev_data = prepare_dataset(
dev_sentences, FLAGS.max_seq_len, tag_to_id, FLAGS.lower
)
# test_data = prepare_dataset(
# test_sentences, FLAGS.max_seq_len, tag_to_id, FLAGS.lower
# )
print("%i / %i / %i sentences in train / dev / test." % (
len(train_data), 0, len(dev_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
make_path(FLAGS)
if os.path.isfile(FLAGS.config_file):
config = load_config(FLAGS.config_file)
else:
config = config_model(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, FLAGS.bilstm_ckpt_path, 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, FLAGS.ckpt_path, logger, global_steps=step)
{v: k for k, v in x.items()}
for x in [model.id_to_word, model.id_to_char]
]
print 'Reloading previous model...'
_, f_eval = model.build(training=False, **parameters)
model.reload()
assert os.path.isfile(opts.test_file)
test_file = opts.test_file
out_txt = opts.out_txt
out_json = opts.out_json
test_sentences = loader.load_sentences(test_file, lower, zeros)
update_tag_scheme(test_sentences, tag_scheme)
test_data = prepare_dataset3(
test_sentences, word_to_id, char_to_id, model.tag_maps, model.feature_maps, lower
)
print "input: ", test_file
from pprint import pprint
print(model.tag_maps)
pprint(model.tag_maps)
test_score, iob_test_score, result_test, _ = evaluate_multilayer(parameters, f_eval, test_sentences, test_data, model.tag_maps)
# Use selected tagging scheme (IOB / IOBES)
# Save the mappings to disk
# Build the model
_, f_eval, f_eval_softmax = model.build(**parameters)
# Reload previous model values
print 'Reloading previous model...'
model.reload()
#
# Train network
#
if opts.dev:
dev_sentences = loader.load_sentences(opts.dev, lower, zeros)
dev_data = prepare_dataset(dev_sentences, word_to_id, char_to_id,
tag_to_id, POS_to_id, lower)
dev_score = evaluate(parameters, f_eval, dev_sentences, dev_data,
id_to_tag, eval_temp, -1, 'valid')
dico_tags = []
for line in open(opts.test):
fn = line.rstrip().split('\t')[0]
try:
test_sentences = loader.load_sentences(fn, lower, zeros)
test_data = prepare_dataset(test_sentences, word_to_id, char_to_id,
tag_to_id, POS_to_id, lower)
outfn = line.rstrip().split('\t')[1]
print fn
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, logger)
while True:
line = input("请输入测试句子:")
result = model.evaluate_line(sess, input_from_line(line, char_to_id), id_to_tag)
print('result:',result)
break
def main():
if FLAGS.train:
if FLAGS.clean:
clean(FLAGS)
train(X_train,X_dev,X_test)
if __name__ == "__main__":
sentences = load_sentences("target_data/All_skill.train", True, False)
kf = RepeatedKFold(n_splits=5, n_repeats=1)
for train_index, test_index in kf.split(sentences):
X_trainall, X_test = np.array(sentences)[train_index], np.array(sentences)[test_index]
kt = RepeatedKFold(n_splits=10, n_repeats=1)
for train_index1, test_index1 in kt.split(X_trainall):
X_train, X_dev = np.array(X_trainall)[train_index1], np.array(X_trainall)[test_index1]
print(' X_train,X_test,X_dev', len(X_train), len(X_test), len(X_dev))
main()
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(dev_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
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)
# author : wn
_t_pos, pos_to_id, id_to_pos = pos_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, pos_to_id,
id_to_pos
], 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)
# author : wn
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag, pos_to_id, id_to_pos = pickle.load(
f)
print(tag_to_id)
print(pos_to_id)
# 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
# )
train_data = prepare_dataset(train_sentences, char_to_id, tag_to_id,
pos_to_id, FLAGS.lower)
dev_data = prepare_dataset(dev_sentences, char_to_id, tag_to_id, pos_to_id,
FLAGS.lower)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
pos_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)
train_dev_manager = BatchManager(train_data, 100)
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)
# author : wn
config = config_model(char_to_id, tag_to_id, pos_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 = []
current_epoch = FLAGS.current_epoch
while current_epoch < FLAGS.max_epoch:
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.debug("iteration:{} step:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration, step % steps_per_epoch,
steps_per_epoch, np.mean(loss)))
loss = []
logger.info(
"\n\n *******************epoch-{} NER loss:{:>9.6f}************************"
.format(current_epoch, np.mean(loss)))
best = evaluate(sess, model, "dev", dev_manager, id_to_tag, logger)
if best:
# save_model(sess, model, FLAGS.ckpt_path, logger)
save_model(sess,
model,
FLAGS.ckpt_path,
logger,
current_epoch,
np.mean(loss),
remark='best_dev')
# elif current_epoch%10 ==0 :
# save_model(sess, model, FLAGS.ckpt_path, logger, current_epoch, np.mean(loss))
evaluate(sess, model, "train", train_dev_manager, id_to_tag,
logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
current_epoch += 1
def train(X_train,X_dev,X_test):
# load data sets
train_sentences = X_train
dev_sentences = X_dev
test_sentences = X_test
train_sentences_loc = load_sentences(FLAGS.train_file_loc, FLAGS.lower, FLAGS.zeros)
dev_sentences_loc = load_sentences(FLAGS.dev_file_loc, FLAGS.lower, FLAGS.zeros)
test_sentences_loc = load_sentences(FLAGS.test_file_loc, FLAGS.lower, FLAGS.zeros)
train_sentences_org = load_sentences(FLAGS.train_file_org, FLAGS.lower, FLAGS.zeros)
dev_sentences_org = load_sentences(FLAGS.dev_file_org, FLAGS.lower, FLAGS.zeros)
test_sentences_org = load_sentences(FLAGS.test_file_org, FLAGS.lower, FLAGS.zeros)
train_sentences_per = load_sentences(FLAGS.train_file_per, FLAGS.lower, FLAGS.zeros)
dev_sentences_per = load_sentences(FLAGS.dev_file_per, FLAGS.lower, FLAGS.zeros)
test_sentences_per = load_sentences(FLAGS.test_file_per, 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(train_sentences_loc, FLAGS.tag_schema)
update_tag_scheme(test_sentences_loc, FLAGS.tag_schema)
update_tag_scheme(train_sentences_per, FLAGS.tag_schema)
update_tag_scheme(test_sentences_per, FLAGS.tag_schema)
update_tag_scheme(train_sentences_org, FLAGS.tag_schema)
update_tag_scheme(test_sentences_org, 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])
)
)
dico_chars_train_loc = char_mapping(train_sentences_loc, FLAGS.lower)[0]
dico_chars_loc, char_to_id_loc, id_to_char_loc = augment_with_pretrained(
dico_chars_train_loc.copy(),
FLAGS.emb_file,
list(itertools.chain.from_iterable(
[[w[0] for w in s] for s in test_sentences_loc])
)
)
dico_chars_train_per = char_mapping(train_sentences_per, FLAGS.lower)[0]
dico_chars_per, char_to_id_per, id_to_char_per = augment_with_pretrained(
dico_chars_train_per.copy(),
FLAGS.emb_file,
list(itertools.chain.from_iterable(
[[w[0] for w in s] for s in test_sentences_per])
)
)
dico_chars_train_org = char_mapping(train_sentences_org, FLAGS.lower)[0]
dico_chars_org, char_to_id_org, id_to_char_org = augment_with_pretrained(
dico_chars_train_org.copy(),
FLAGS.emb_file,
list(itertools.chain.from_iterable(
[[w[0] for w in s] for s in test_sentences_org])
)
)
else:
_c, char_to_id, id_to_char = char_mapping(train_sentences, FLAGS.lower)
_c_loc, char_to_id_loc, id_to_char_loc = char_mapping(train_sentences_loc, FLAGS.lower)
_c_per, char_to_id_per, id_to_char_per = char_mapping(train_sentences_per, FLAGS.lower)
_c_org, char_to_id_org, id_to_char_org = char_mapping(train_sentences_org, FLAGS.lower)
# Create a dictionary and a mapping for tags
_t, tag_to_id, id_to_tag = tag_mapping(train_sentences)
_t_loc, tag_to_id_loc, id_to_tag_loc = tag_mapping(train_sentences_loc)
_t_per, tag_to_id_per, id_to_tag_per = tag_mapping(train_sentences_per)
_t_org, tag_to_id_org, id_to_tag_org = tag_mapping(train_sentences_org)
with open(FLAGS.map_file, "wb") as f:
pickle.dump([char_to_id, id_to_char, tag_to_id, id_to_tag,char_to_id_loc, id_to_char_loc, tag_to_id_loc, id_to_tag_loc,char_to_id_per, id_to_char_per, tag_to_id_per, id_to_tag_per,char_to_id_org, id_to_char_org, tag_to_id_org, id_to_tag_org], f)
else:
with open(FLAGS.map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag,char_to_id_loc, id_to_char_loc, tag_to_id_loc, id_to_tag_loc,char_to_id_per, id_to_char_per, tag_to_id_per, id_to_tag_per,char_to_id_org, id_to_char_org, tag_to_id_org, id_to_tag_org = 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),len(dev_data), len(test_data)))
train_data_loc = prepare_dataset_ner(
train_sentences_loc, char_to_id_loc, tag_to_id_loc, FLAGS.lower
)
dev_data_loc = prepare_dataset_ner(
dev_sentences_loc, char_to_id_loc, tag_to_id_loc, FLAGS.lower
)
test_data_loc = prepare_dataset_ner(
test_sentences_loc, char_to_id_loc, tag_to_id_loc, FLAGS.lower
)
print("%i / %i / %i sentences_loc in train / dev / test." % (
len(train_data_loc), len(dev_data_loc), len(test_data_loc)))
train_data_per = prepare_dataset_ner(
train_sentences_per, char_to_id_per, tag_to_id_per, FLAGS.lower
)
dev_data_per = prepare_dataset_ner(
dev_sentences_per, char_to_id_per, tag_to_id_per, FLAGS.lower
)
test_data_per = prepare_dataset_ner(
test_sentences_per, char_to_id_per, tag_to_id_per, FLAGS.lower
)
print("%i / %i / %i sentences_per in train / dev / test." % (
len(train_data_per), len(dev_data_per), len(test_data_per)))
train_data_org = prepare_dataset_ner(
train_sentences_org, char_to_id_org, tag_to_id_org, FLAGS.lower
)
dev_data_org = prepare_dataset_ner(
dev_sentences_org, char_to_id_org, tag_to_id_org, FLAGS.lower
)
test_data_org = prepare_dataset_ner(
test_sentences_org, char_to_id_org, tag_to_id_org, FLAGS.lower
)
print("%i / %i / %i sentences_org in train / dev / test." % (
len(train_data_org), len(dev_data_org), len(test_data_org)))
train_manager = BatchManager(train_data, FLAGS.batch_size)
dev_manager = BatchManager(dev_data, 100)
test_manager = BatchManager(test_data, 100)
train_manager_loc = BatchManager(train_data_loc, FLAGS.batch_size)
train_manager_per = BatchManager(train_data_per, FLAGS.batch_size)
train_manager_org = BatchManager(train_data_org, FLAGS.batch_size)
# 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,char_to_id_loc, tag_to_id_loc,char_to_id_per, tag_to_id_per,char_to_id_org, tag_to_id_org)
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
steps_per_epoch_loc = train_manager_loc.len_data
steps_per_epoch_per = train_manager_per.len_data
steps_per_epoch_org = train_manager_org.len_data
model = create_model(Model, FLAGS.ckpt_path, load_word2vec, config, id_to_char, id_to_char_loc, id_to_char_per, id_to_char_org, logger)
with tf.Session(config=tf_config, graph = model.graph ) as sess:
sess.run(tf.global_variables_initializer())
if config["pre_emb"]:
emb_weights = sess.run(model.char_lookup.read_value())
emb_weights_ner = sess.run(model.char_lookup.read_value())
emb_weights, emb_weights_ner = load_word2vec(config["emb_file"], id_to_char, id_to_char_loc,id_to_char_per,id_to_char_org, config["char_dim"],
emb_weights, emb_weights_ner)
sess.run(model.char_lookup.assign(emb_weights))
logger.info("Load pre-trained embedding.")
logger.info("start training")
loss = []
loss_loc = []
loss_per = []
loss_org = []
for i in range(100):
for batch_loc in train_manager_loc.iter_batch(shuffle=True):
step_loc, batch_loss_loc = model.run_step_ner(sess, True, batch_loc)
loss_loc.append(batch_loss_loc)
if step_loc % FLAGS.steps_check == 0:
iteration_loc = step_loc // steps_per_epoch_loc + 1
logger.info("iteration:{} step_loc:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration_loc, step_loc % steps_per_epoch_loc, steps_per_epoch_loc, np.mean(loss_loc)))
loss_loc = []
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_1 = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"SKILL loss:{:>9.6f}".format(
iteration_1, step % steps_per_epoch, steps_per_epoch, np.mean(loss)))
loss = []
precision_loc_dev = model.precision(sess, dev_manager, id_to_tag)
precision_loc_test = model.precision(sess, test_manager, id_to_tag)
for batch_per in train_manager_per.iter_batch(shuffle=True):
step_per, batch_loss_per = model.run_step_ner(sess, True, batch_per)
loss_per.append(batch_loss_per)
if step_per % FLAGS.steps_check == 0:
iteration_per = step_per // steps_per_epoch_per + 1
logger.info("iteration:{} step_per:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration_per, step_per % steps_per_epoch_per, steps_per_epoch_per, np.mean(loss_per)))
loss_per = []
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_2 = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"SKILL loss:{:>9.6f}".format(
iteration_2, step % steps_per_epoch, steps_per_epoch, np.mean(loss)))
loss = []
precision_per_dev = model.precision(sess, dev_manager, id_to_tag)
precision_per_test = model.precision(sess, test_manager, id_to_tag)
for batch_org in train_manager_org.iter_batch(shuffle=True):
step_org, batch_loss_org = model.run_step_ner(sess, True, batch_org)
loss_org.append(batch_loss_org)
if step_org % FLAGS.steps_check == 0:
iteration_org = step_org // steps_per_epoch_org + 1
logger.info("iteration:{} step_org:{}/{}, "
"NER loss:{:>9.6f}".format(
iteration_org, step_org % steps_per_epoch_org, steps_per_epoch_org, np.mean(loss_org)))
loss_org = []
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_3 = step // steps_per_epoch + 1
logger.info("iteration:{} step:{}/{}, "
"SKILL loss:{:>9.6f}".format(
iteration_3, step % steps_per_epoch, steps_per_epoch, np.mean(loss)))
loss = []
precision_org_dev = model.precision(sess, dev_manager, id_to_tag)
precision_org_test = model.precision(sess, test_manager, id_to_tag)
best = evaluate(sess, model, "dev", dev_manager, id_to_tag,precision_loc_dev,precision_per_dev,precision_org_dev, logger)
if best:
save_model(sess, model, FLAGS.ckpt_path, logger)
best_test,results= evaluate(sess, model, "test", test_manager, id_to_tag,precision_loc_test,precision_per_test,precision_org_test, logger)
with open("CDTL_PSE-result.csv", "a",encoding='utf-8')as st_re:
st_re.write(str(results).replace("[", "").replace("]", ""))
st_re.write("\n")
model = Model(parameters=parameters,
models_path=models_path,
overwrite_mappings=opts.overwrite_mappings)
print "Model location: %s" % model.model_path
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['t_s']
max_sentence_lengths = {}
max_word_lengths = {}
# Load sentences
train_sentences, max_sentence_lengths['train'], max_word_lengths['train'] =\
loader.load_sentences(opts.train, lower, zeros)
dev_sentences, max_sentence_lengths['dev'], max_word_lengths[
'dev'] = loader.load_sentences(opts.dev, lower, zeros)
test_sentences, max_sentence_lengths['test'], max_word_lengths[
'test'] = loader.load_sentences(opts.test, lower, zeros)
global_max_sentence_length, global_max_char_length = \
calculate_global_maxes(max_sentence_lengths, max_word_lengths)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
def load_data(pre_emb):
train_sentences = loader.load_sentences('dataset/eng.train', lower, zeros)
dev_sentences = loader.load_sentences("dataset/eng.testa", lower, zeros)
test_sentences = loader.load_sentences("dataset/eng.testb", lower, zeros)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
all_emb = 1
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(), pre_emb,
list(
itertools.chain.from_iterable([[w[0] for w in s]
for s in dev_sentences +
test_sentences]))
if not all_emb else None)
dico_chars, char_to_id, id_to_char = char_mapping(train_sentences)
dico_tags, tag_to_id, id_to_tag = tag_mapping(train_sentences)
all_word_embeds = {}
for i, line in enumerate(codecs.open(pre_emb, 'r', 'utf-8')):
s = line.strip().split()
if len(s) == word_dim + 1:
all_word_embeds[s[0]] = np.array([float(i) for i in s[1:]])
word_embeds = np.random.uniform(-np.sqrt(0.06), np.sqrt(0.06),
(len(word_to_id), word_dim))
for w in word_to_id:
if w in all_word_embeds:
word_embeds[word_to_id[w]] = all_word_embeds[w]
elif w.lower() in all_word_embeds:
word_embeds[word_to_id[w]] = all_word_embeds[w.lower()]
print('Loaded %i pretrained embeddings.' % len(all_word_embeds))
pretrained_word_list = set([
line.rstrip().split()[0].strip()
for line in codecs.open(pre_emb, 'r', 'utf-8') if len(pre_emb) > 0
])
filter_ppdb_list = []
for i in pretrained_word_list:
if re.match('[^\x00-\x7F]+', i):
continue
else:
if all(w in dico_chars for w in i):
filter_ppdb_list.append(i)
filter_ppdb_list = set(filter_ppdb_list) | set(dico_words_train.keys())
train_sentences_packed = packed_data(train_sentences)
dev_sentences_packed = packed_data(dev_sentences)
test_sentences_packed = packed_data(test_sentences)
return train_sentences_packed, dev_sentences_packed, test_sentences_packed, word_embeds, word_to_id, filter_ppdb_list
mapping_file = 'models/mapping.pkl'
print(parameters)
eval_script = "./evaluation/conlleval.pl"
eval_temp = "./evaluation/temp"
print("eval_script = ", eval_script)
print("eval_temp = ", eval_temp)
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
train_sentences = loader.load_sentences(parameters["train"], lower, zeros)
dev_sentences = loader.load_sentences(parameters["dev"], lower, zeros)
test_sentences = loader.load_sentences(parameters["test"], lower, zeros)
test_train_sentences = loader.load_sentences(parameters["test_train"], lower, zeros)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(),
parameters['pre_emb'],
list(itertools.chain.from_iterable(
[[w[0] for w in s] for s in dev_sentences + test_sentences])
) if not parameters['all_emb'] else None
def test_inference_performance():
from sklearn.metrics import f1_score
from torchtext.datasets import SequenceTaggingDataset
from torchtext.data import Field, NestedField
WORD = Field(init_token='<bos>', eos_token='<eos>')
CHAR_NESTING = Field(tokenize=list, init_token='<bos>', eos_token='<eos>')
CHAR = NestedField(CHAR_NESTING, init_token='<bos>', eos_token='<eos>')
ENTITY = Field(init_token='<bos>', eos_token='<eos>')
data_file = tempfile.NamedTemporaryFile(delete=True)
# TODO Need to be decoded in Python 3
data_file.write(requests.get(CORA_URL).content)
fields = [(('text', 'char'),
(WORD, CHAR))] + [(None, None)] * 22 + [('entity', ENTITY)]
dataset = SequenceTaggingDataset(data_file.name, fields, separator=" ")
model = Model(model_path='models/neuralParsCit')
model.parameters['pre_emb'] = os.path.join(os.getcwd(),
'vectors_with_unk.kv')
f = model.build(training=False, **model.parameters)
model.reload()
word_to_id = {v: i for i, v in model.id_to_word.items()}
char_to_id = {v: i for i, v in model.id_to_char.items()}
tag_to_id = {tag: i for i, tag in model.id_to_tag.items()}
tf = tempfile.NamedTemporaryFile(delete=False)
tf.write("\n\n".join(
["\n".join(example.text) for example in dataset.examples]))
tf.close()
train_sentences = load_sentences(tf.name, model.parameters['lower'],
model.parameters['zeros'])
train_inputs = prepare_dataset(train_sentences, word_to_id, char_to_id,
model.parameters['lower'], True)
preds = []
for citation in train_inputs:
inputs = create_input(citation, model.parameters, False)
y_pred = np.array(f[1](*inputs))[1:-1]
preds.append([(w, y_pred[i])
for i, w in enumerate(citation['str_words'])])
assert len(preds) == len(dataset.examples)
results = []
for P, T in zip(preds, dataset.examples):
for p, t in zip(P, zip(T.text, T.entity)):
results.append((p[1], tag_to_id[t[1]]))
pred, true = zip(*results)
eval_metrics = {
'micro_f1': f1_score(true, pred, average='micro'),
'macro_f1': f1_score(true, pred, average='macro')
}
data_file.close()
assert eval_metrics == pytest.approx({
'macro_f1': 0.984,
'micro_f1': 0.993
},
abs=0.001)
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)
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('maps.txt','w',encoding='utf8') as f1:
#f1.writelines(str(char_to_id)+" "+id_to_char+" "+str(tag_to_id)+" "+id_to_tag+'\n')
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(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 i % 7 == 0:
save_model(sess, model, FLAGS.ckpt_path, 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)
all_train_sentences = load_sentences(FLAGS.train_file, FLAGS.lower,
FLAGS.zeros)
train_sentences, dev_sentences = split_train_dev(all_train_sentences)
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_train = char_mapping(all_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(all_train_sentences,
FLAGS.lower)
# _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(all_train_sentences)
# _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)
# nlp = StanfordCoreNLP(r'E:\DC\dataset\泰一指尚评测数据\stanford-corenlp-full-2017-06-09')
#l_sorted_lexcion = load_lexcion(FLAGS.lexcion_file, nlp)
l_sorted_lexcion = []
# prepare data, get a collection of list containing index
train_data = prepare_dataset(train_sentences, char_to_id, tag_to_id,
l_sorted_lexcion, FLAGS.lower)
dev_data = prepare_dataset(dev_sentences, char_to_id, tag_to_id,
l_sorted_lexcion, FLAGS.lower)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
l_sorted_lexcion, FLAGS.lower)
print("%i / %i / %i sentences in train / dev / test." %
(len(train_data), len(dev_data), len(test_data)))
max_len = max(
[len(sentence[0]) for sentence in train_data + test_data + dev_data])
train_manager = BatchManager(train_data, FLAGS.batch_size, max_len)
dev_manager = BatchManager(dev_data, 800, max_len)
test_manager = BatchManager(test_data, 800, max_len)
# random.shuffle(train_data)
# pad_test_data = pad_data(test_data)
# pad_dev_data = pad_data(dev_data)
# 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, max_len)
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(FLAGS.max_epoch):
random.shuffle(train_data)
pad_train_data = pad_data(train_data, max_len)
strings, chars, lexcion_teatures, pos_ids, dep_ids, head_ids, targets = pad_train_data
for j in range(0, len(strings), FLAGS.batch_size):
batch = [
strings[j:j + FLAGS.batch_size],
chars[j:j + FLAGS.batch_size],
lexcion_teatures[j:j + FLAGS.batch_size],
pos_ids[j:j + FLAGS.batch_size],
dep_ids[j:j + FLAGS.batch_size],
head_ids[j:j + FLAGS.batch_size],
targets[j:j + FLAGS.batch_size]
]
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:{}/{}, "
"AS 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, i)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
evaluate(sess, model, "test", test_manager, id_to_tag, logger)
os.makedirs(models_path)
# Initialize model
start_time = time.time()
model = Model(parameters=parameters, models_path=models_path)
print "Model location: %s" % model.model_path
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
use_gaze = parameters['use_gaze']
batch_size = opts.batch_size
# Load sentences
train_sentences = loader.load_sentences(opts.train, zeros, lower)
dev_sentences = loader.load_sentences(opts.dev, zeros, lower)
test_sentences = loader.load_sentences(opts.test, zeros, lower)
# Use selected tagging scheme (IOB / IOESB / IOESB1B2)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
# Create a dictionary / mapping of chars
# If we use pretrained embeddings, we add them to the dictionary.
if parameters['pre_emb']:
dico_chars_train = char_mapping(train_sentences)[0]
dico_chars, char_to_id, id_to_char = augment_with_pretrained(
dico_chars_train.copy(), parameters['pre_emb'],
list(
def train_new():
train_sent = load_sentences(FLAGS.filepath)
update_tag_scheme(train_sent, FLAGS.tag_schema)
if not os.path.isfile(FLAGS.map_file):
_c, char_to_id, id_to_char = char_mapping(train_sent, FLAGS.lower)
print("random embedding")
# Create a dictionary and a mapping for tags
_t, tag_to_id, id_to_tag = tag_mapping(train_sent)
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)
# 数据准备，划分验证集和训练集
np.random.seed(10)
train_sent_ = np.array(train_sent)
shuffle_indices = np.random.permutation(np.arange(len(train_sent)))
sent_shuffled = train_sent_[shuffle_indices]
dev_sample_index = -1 * int(FLAGS.dev_percentage * float(len(train_sent)))
train_sent_new, dev_sent = sent_shuffled[:dev_sample_index], sent_shuffled[
dev_sample_index:]
train_data = prepare_dataset(train_sent_new, char_to_id, tag_to_id,
FLAGS.lower)
dev_data = prepare_dataset(dev_sent, char_to_id, tag_to_id, FLAGS.lower)
print("%i / %i sentences in train." % (len(train_data), len(dev_data)))
train_manager = BatchManager(train_data, FLAGS.batch_size)
dev_manager = BatchManager(dev_data, 100)
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 = FLAGS.log_file
logger = get_logger(log_path)
print_config(config, logger)
# 根据需求，设置动态使用GPU资源
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:
fig = plt.figure()
ax = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
plt.grid(True)
plt.ion()
model = create_model(sess, Model, FLAGS.ckpt_path, load_word2vec,
config, id_to_char, logger)
logger.info("start training")
loss = []
for i in range(FLAGS.max_epoch):
for batch in train_manager.iter_batch(shuffle=True):
step, batch_loss = model.run_step(sess, True, batch)
loss.append(batch_loss)
if step % 20 == 0:
ax.scatter(step, np.mean(loss), c='b', marker='.')
plt.pause(0.001)
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, f1 = evaluate(sess, model, "dev", dev_manager, id_to_tag,
logger)
ax2.scatter(i + 1, f1, c='b', marker='.')
plt.pause(0.001)
if best:
save_model(sess, model, FLAGS.ckpt_path, logger, "best")
def train():
# load data sets
train_sentences = load_sentences(
FLAGS.train_file, FLAGS.lower,
FLAGS.zeros) # dimension:num_sentence*len_sentence*2
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) # dimension:num_sentence*len_sentence*2
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
if FLAGS.pre_emb: # 如果使用预训练的词嵌入
dico_chars_train = char_mapping(train_sentences, FLAGS.lower)[
0] # dico_chars_train dimension: 训练数据集中出现的字符类别数*2，
dico_chars, char_to_id, id_to_char = augment_with_pretrained( # 利用测试数据样本集中的字对dico_chars_train进行补充
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: # 创建map_file文件
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) # dimension: NumSentence*4*LenSentence
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), len(dev_data), len(test_data)))
train_manager = BatchManager(
train_data, FLAGS.batch_size
) # batch_data dimension: BatchNum*4*BatchSize*MaxLenSentence
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): # 若已有config_file则读取加载
config = load_config(FLAGS.config_file)
else: # 若没有config_file则新建并保存为文件
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) # 将config打印到日志文件
# limit GPU memory
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True # 动态申请内存
steps_per_epoch = train_manager.len_data # len_data: ceil(NumSentence/BatchSize)
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(FLAGS.max_epoch): # 括号中数字是epoach数量
for batch in train_manager.iter_batch(
shuffle=True
): # 一次从batch_data中取出一个batch,Shuffle为True表示打乱batch_data的顺序
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)
# View the tensorboard graph by running the following code and then going to the terminal and typing:
# tensorboard --logdir = tensorboard_logs
merged = tf.summary.merge_all()
if not os.path.exists('tensorboard_logs/'):
os.makedirs('tensorboard_logs/')
my_writer = tf.summary.FileWriter('tensorboard_logs/', sess.graph)
def train():
# load data sets
datasets = load_sentences(FLAGS.train_file, FLAGS.lower)
random.shuffle(datasets)
train_sentences = datasets[:14000]
test_sentences = datasets[14000:]
# 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
char_to_id, _ = elmo_char_mapping(FLAGS.elmo_vocab)
# 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, tag_to_id, id_to_tag], f)
else:
with open(FLAGS.map_file, "rb") as f:
char_to_id, 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)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
FLAGS.lower)
print("%i / %i sentences in train / dev." %
(len(train_data), len(test_data)))
elmo_batcher = get_batcher()
train_manager = BatchManager(train_data, FLAGS.batch_size, elmo_batcher)
test_manager = BatchManager(test_data, FLAGS.batch_size, elmo_batcher)
# 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(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(allow_soft_placement=True)
tf_config.gpu_options.allow_growth = True
steps_per_epoch = train_manager.len_data
with tf.Session(config=tf_config) as sess:
elmo_model = load_elmo()
model = create_model(sess, Model, FLAGS.ckpt_path, elmo_model, config,
logger)
logger.info("start training")
loss = []
for i in range(FLAGS.max_epoch):
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, "test", test_manager, id_to_tag,
logger)
# evaluate(sess, model, "dev", dev_manager, id_to_tag, logger)
if best:
save_model(sess, model, FLAGS.ckpt_path, logger)
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {"ner": NerProcessor}
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
bert_config = modeling.BertConfig.from_json_file(
FLAGS.bert_config_file) # 加载bert模型的参数设置
if FLAGS.max_seq_length > bert_config.max_position_embeddings: # 限制ner的max_seq_length不大于bert的最大长度限制512
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels(
) # 获取label标签["O", "B-DIS", "I-DIS", "X", "[CLS]", "[SEP]"]
tokenizer = tokenization.FullTokenizer( # 对vocab的初始处理，包括word:id，大小写等
vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name: # use_tpu 默认为False
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.
save_checkpoints_steps, # how often to save the model checkpoint. 1000
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop, # 1000
num_shards=FLAGS.num_tpu_cores, # 8
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None # warm up 步数的比例，比如说总共学习100步，warmup_proportion=0.1表示前10步用来warm up，warm up时以
# 较低的学习率进行学习(lr = global_step/num_warmup_steps * init_lr)，10步之后以正常(或衰减)的学习
# 率来学习。
##################
train_sentences = load_sentences(
os.path.join(FLAGS.data_dir, "ner.train"), FLAGS.lower,
FLAGS.zeros) # 加载训练数据，格式为二维list，外层存储每一句话，内层为每句话的一个字和对应的tag
dev_sentences = load_sentences(os.path.join(FLAGS.data_dir, "ner.dev"),
FLAGS.lower, FLAGS.zeros)
test_sentences = load_sentences(os.path.join(FLAGS.data_dir, "ner.dev"),
FLAGS.lower, FLAGS.zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences,
FLAGS.tag_schema) # 默认IOBES，更新tag方案，将IOB转化为IOBES
update_tag_scheme(dev_sentences,
FLAGS.tag_schema) # 默认IOBES，更新tag方案，将IOB转化为IOBES
update_tag_scheme(test_sentences, FLAGS.tag_schema)
# create maps if not exist
if not os.path.isfile(map_file):
# create dictionary for word
if FLAGS.pre_emb: # use pre-trained embedding
dico_chars_train = char_mapping(train_sentences, FLAGS.lower)[0]
dico_chars, char_to_id, id_to_char = augment_with_pretrained( # 为了保证训练集中未出现的测试集中的字至少也能用预训练的word embedding
dico_chars_train.copy(),
FLAGS.emb_file,
list(
itertools.chain.from_iterable([[w[0] for w in s]
for s in test_sentences
]) # 将嵌套的列表拼接
))
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)
# 执行mark_mapping
_c, mark_to_id, id_to_mark = mark_mapping(train_sentences)
entropy_dict = load_entropy_dict(FLAGS.entropy_dict)
with open(map_file, "wb") as f:
pickle.dump([
char_to_id, id_to_char, tag_to_id, id_to_tag, mark_to_id,
id_to_mark, entropy_dict
], f)
else:
with open(map_file, "rb") as f:
char_to_id, id_to_char, tag_to_id, id_to_tag, mark_to_id, id_to_mark, entropy_dict = pickle.load(
f)
# prepare data, get a collection of list containing index
train_data = prepare_dataset(train_sentences, char_to_id, tag_to_id,
mark_to_id, entropy_dict, FLAGS.lower)
dev_data = prepare_dataset(dev_sentences, char_to_id, tag_to_id,
mark_to_id, entropy_dict, FLAGS.lower)
test_data = prepare_dataset(test_sentences, char_to_id, tag_to_id,
mark_to_id, entropy_dict, FLAGS.lower)
###############
if FLAGS.do_train:
train_examples = processor.get_train_examples(
FLAGS.data_dir, train_data) # 返回的每一个元素是一个InputExample对象
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size *
FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
model_fn = model_fn_builder(
bert_config=bert_config,
num_labels=len(label_list) + 1,
init_checkpoint=FLAGS.
init_checkpoint, # 将预训练的bert模型的参数加载到模型中作为fine-tuning的初始化参数
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
filed_based_convert_examples_to_features(train_examples, label_list,
FLAGS.max_seq_length, tokenizer,
train_file)
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
filed_based_convert_examples_to_features(eval_examples, label_list,
FLAGS.max_seq_length, tokenizer,
eval_file)
token_path = os.path.join(FLAGS.output_dir, "token_test.txt")
with open(FLAGS.output_dir + '/label2id.pkl', 'rb') as rf:
label2id = pickle.load(rf)
id2label = {value: key for key, value in label2id.items()}
if os.path.exists(token_path):
os.remove(token_path)
predict_examples = processor.get_test_examples(FLAGS.data_dir)
predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
# batch_labels 是以句为单位的[[1,2,0,0,1,2],[...]]
batch_tokens, batch_labels = filed_based_convert_examples_to_features(
predict_examples,
label_list,
FLAGS.max_seq_length,
tokenizer,
predict_file,
mode="test")
for actual_train_step in list(range(1000, num_train_steps,
2000)) + [num_train_steps]:
if FLAGS.do_train:
start = time.clock()
tf.logging.info("start training time: %f", start)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", actual_train_step)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn,
max_steps=actual_train_step)
end = time.clock()
tf.logging.info("end training time: %f", end)
tf.logging.info("training time: %f", end - start)
if FLAGS.do_eval:
start = time.clock()
tf.logging.info("start evaluation time: %f", start)
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Num examples = %d", len(eval_examples))
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_steps = None
if FLAGS.use_tpu:
eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)
eval_drop_remainder = True if FLAGS.use_tpu else False
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn,
steps=eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir,
"eval_results.txt")
with open(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
end = time.clock()
tf.logging.info("end evaluation time: %f", end)
tf.logging.info("evaluation time: %f", end - start)
if FLAGS.do_predict:
start = time.clock()
tf.logging.info("start predict time: %f", start)
tf.logging.info("***** Running prediction *****")
tf.logging.info(" Num examples = %d", len(predict_examples))
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
if FLAGS.use_tpu:
# Warning: According to tpu_estimator.py Prediction on TPU is an
# experimental feature and hence not supported here
raise ValueError("Prediction in TPU not supported")
predict_drop_remainder = True if FLAGS.use_tpu else False
predict_input_fn = file_based_input_fn_builder(
input_file=predict_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=predict_drop_remainder)
result = estimator.predict(input_fn=predict_input_fn)
_result = []
for prediction in result:
_result += [prediction_id for prediction_id in prediction]
output_predict_file = os.path.join(
FLAGS.output_dir + "/label_test/",
"label_test.txt-" + str(actual_train_step))
Writer(output_predict_file, _result, batch_tokens, batch_labels,
id2label)
end = time.clock()
tf.logging.info("end predict time: %f", end)
tf.logging.info("predict time: %f", end - start)
def runModelInLoop(dropout,char_dim,char_lstm_dim,word_dim,word_lstm_dim):
#results File
resultsPath = "/Users/Ehsan/Documents/Ehsan_General/HMQ/HMQ_Projects/DNR2/COLING-2016-Code/i2b2-2010/results/"
for u_dropout in dropout:
for v_char_dim in char_dim:
for w_char_lstm_dim in char_lstm_dim:
for x_word_dim in word_dim:
for y_word_lstm_dim in word_lstm_dim:
for dataset in datasets:
print "+++++++++++++++"
print u_dropout,v_char_dim,w_char_lstm_dim,x_word_dim,y_word_lstm_dim,dataset
parameters['dropout'] = u_dropout
parameters['char_dim'] = v_char_dim
parameters['char_lstm_dim'] =w_char_lstm_dim
parameters['word_dim'] = x_word_dim
parameters['word_lstm_dim'] = y_word_lstm_dim
# If dataset is DrugBank assign predefined path
if(dataset == "i2b2-2010"):
opts.train = i2b2BasePath+"train.txt"
opts.dev = i2b2BasePath+ "dev.txt"
opts.test = i2b2BasePath+ "test.txt"
resultsFile = resultsPath +"i2b2_2010_Results.txt"
# Initialize model
model = Model(parameters=parameters, models_path=models_path)
print "Model location: %s" % model.model_path
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
# Load sentences
train_sentences = loader.load_sentences(opts.train, lower, zeros)
dev_sentences = loader.load_sentences(opts.dev, lower, zeros)
test_sentences = loader.load_sentences(opts.test, lower, zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
if parameters['pre_emb']:
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(),
parameters['pre_emb'],
list(itertools.chain.from_iterable(
[[w[0] for w in s] for s in dev_sentences + test_sentences])
) if not parameters['all_emb'] else None
)
else:
dico_words, word_to_id, id_to_word = word_mapping(train_sentences, lower)
dico_words_train = dico_words
# Create a dictionary and a mapping for words / POS tags / tags
dico_chars, char_to_id, id_to_char = char_mapping(train_sentences)
dico_tags, tag_to_id, id_to_tag = tag_mapping(train_sentences)
print "Calling the prepare_dataset :--"
# Index data
train_data = prepare_dataset(
train_sentences, word_to_id, char_to_id, tag_to_id, lower
)
dev_data = prepare_dataset(
dev_sentences, word_to_id, char_to_id, tag_to_id, lower
)
test_data = prepare_dataset(
test_sentences, word_to_id, char_to_id, tag_to_id, lower
)
print "%i / %i / %i sentences in train / dev / test." % (
len(train_data), len(dev_data), len(test_data))
# Save the mappings to disk
print 'Saving the mappings to disk...'
model.save_mappings(id_to_word, id_to_char, id_to_tag)
# Build the model
f_train, f_eval = model.build(**parameters)
# Reload previous model values
if opts.reload:
print 'Reloading previous model...'
model.reload()
# Train network
#
singletons = set([word_to_id[k] for k, v
in dico_words_train.items() if v == 1])
n_epochs = 2 # number of epochs over the training set
freq_eval = 1000 # evaluate on dev every freq_eval steps
best_dev = -np.inf
best_test = -np.inf
count = 0
for epoch in xrange(n_epochs):
epoch_costs = []
print "Starting epoch %i..." % epoch
for i, index in enumerate(np.random.permutation(len(train_data))):
count += 1
input = create_input(train_data[index], parameters, True, singletons)
new_cost = f_train(*input)
epoch_costs.append(new_cost)
#if i % 50 == 0 and i > 0 == 0:
# print "%i, cost average: %f" % (i, np.mean(epoch_costs[-50:]))
if count % freq_eval == 0:
dev_score = evaluate(parameters, f_eval, dev_sentences,
dev_data, id_to_tag, dico_tags)
test_score = evaluate(parameters, f_eval, test_sentences,
test_data, id_to_tag, dico_tags)
print "Score on dev: %.5f" % dev_score
print "Score on test: %.5f" % test_score
if dev_score > best_dev:
best_dev = dev_score
print "New best score on dev."+str(best_dev)
# print "Saving model to disk..."
# model.save()
if test_score > best_test:
best_test = test_score
print "New best score on test."+str(best_test)
# print "Config values used are : "
print "Epoch %i done. Average cost: %f" % (epoch, np.mean(epoch_costs))
# Write the best dev and test scores to the file
del model
with open(resultsFile, 'a') as f:
f.write("dropout: "+ str(parameters['dropout'] ) +"| char_dim: |"+str(parameters['char_dim'])+ "| char_lstm_dim: "+str(parameters['char_lstm_dim']) +" word_dim: "+ str(parameters['word_dim']) +" |word_lstm_dim: "+ str( parameters['word_lstm_dim'] )+" | Best Dev Score: "+str(best_dev) + " | Best Test Score: "+str(best_test) +"\n")
return
if not os.path.exists(eval_temp):
os.makedirs(eval_temp)
if not os.path.exists(models_path):
os.makedirs(models_path)
# Initialize model
model = Model(parameters=parameters, models_path=models_path)
print "Model location: %s" % model.model_path
# Data parameters
lower = parameters['lower']
zeros = parameters['zeros']
tag_scheme = parameters['tag_scheme']
# Load sentences
train_sentences = loader.load_sentences(opts.train, lower, zeros)
dev_sentences = loader.load_sentences(opts.dev, lower, zeros)
test_sentences = loader.load_sentences(opts.test, lower, zeros)
# Use selected tagging scheme (IOB / IOBES)
update_tag_scheme(train_sentences, tag_scheme)
update_tag_scheme(dev_sentences, tag_scheme)
update_tag_scheme(test_sentences, tag_scheme)
# Create a dictionary / mapping of words
# If we use pretrained embeddings, we add them to the dictionary.
if parameters['pre_emb']:
dico_words_train = word_mapping(train_sentences, lower)[0]
dico_words, word_to_id, id_to_word = augment_with_pretrained(
dico_words_train.copy(),
parameters['pre_emb'],
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
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(100):
for batch in train_manager.iter_batch(shuffle=True):
#print batch
step, batch_loss = model.run_step(sess, True, batch)
#print step
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)
