def __init__(self,
model_name,
model_type="BidLSTM_CRF",
embeddings_name=None,
char_emb_size=25,
max_char_length=30,
char_lstm_units=25,
word_lstm_units=100,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=True,
use_crf=True,
batch_size=20,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
early_stop=True,
patience=5,
max_checkpoints_to_keep=5,
log_dir=None,
use_ELMo=True,
fold_number=1):
self.model = None
self.models = None
self.p = None
self.log_dir = log_dir
self.embeddings_name = embeddings_name
word_emb_size = 0
if embeddings_name is not None:
self.embeddings = Embeddings(embeddings_name, use_ELMo=use_ELMo)
word_emb_size = self.embeddings.embed_size
self.model_config = ModelConfig(model_name=model_name,
model_type=model_type,
embeddings_name=embeddings_name,
word_embedding_size=word_emb_size,
char_emb_size=char_emb_size,
char_lstm_units=char_lstm_units,
max_char_length=max_char_length,
word_lstm_units=word_lstm_units,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
use_crf=use_crf,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo)
self.training_config = TrainingConfig(batch_size, optimizer, learning_rate,
lr_decay, clip_gradients, max_epoch,
early_stop, patience,
max_checkpoints_to_keep)
def load(self, dir_path='data/models/sequenceLabelling/'):
self.model_config = ModelConfig.load(
os.path.join(dir_path, self.model_config.model_name,
self.config_file))
self.p = WordPreprocessor.load(
os.path.join(dir_path, self.model_config.model_name,
self.preprocessor_file))
if self.model_config.model_type.lower().find("bert") != -1:
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag),
dir_path=dir_path)
self.model.load_model()
return
# load embeddings
# Do not use cache in 'production' mode
self.embeddings = Embeddings(self.model_config.embeddings_name,
use_ELMo=self.model_config.use_ELMo,
use_BERT=self.model_config.use_BERT,
use_cache=False)
self.model_config.word_embedding_size = self.embeddings.embed_size
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag))
self.model.load(filepath=os.path.join(
dir_path, self.model_config.model_name, self.weight_file))
def load(self,
dir_path='data/models/sequenceLabelling/',
weight_file=DEFAULT_WEIGHT_FILE_NAME):
model_path = os.path.join(dir_path, self.model_config.model_name)
self.model_config = ModelConfig.load(
os.path.join(model_path, CONFIG_FILE_NAME))
if self.model_config.embeddings_name is not None:
# load embeddings
# Do not use cache in 'prediction/production' mode
self.embeddings = Embeddings(self.model_config.embeddings_name,
resource_registry=self.registry,
use_ELMo=self.model_config.use_ELMo,
use_cache=False)
self.model_config.word_embedding_size = self.embeddings.embed_size
else:
self.embeddings = None
self.model_config.word_embedding_size = 0
self.p = Preprocessor.load(
os.path.join(dir_path, self.model_config.model_name,
PROCESSOR_FILE_NAME))
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag),
load_pretrained_weights=False,
local_path=os.path.join(
dir_path, self.model_config.model_name))
print(
"load weights from",
os.path.join(dir_path, self.model_config.model_name, weight_file))
self.model.load(filepath=os.path.join(
dir_path, self.model_config.model_name, weight_file))
self.model.print_summary()
def load(self, dir_path='data/models/sequenceLabelling/'):
self.p = WordPreprocessor.load(os.path.join(dir_path, self.model_config.model_name, self.preprocessor_file))
self.model_config = ModelConfig.load(os.path.join(dir_path, self.model_config.model_name, self.config_file))
# load embeddings
self.embeddings = Embeddings(self.model_config.embeddings_name, use_ELMo=self.model_config.use_ELMo)
self.model_config.word_embedding_size = self.embeddings.embed_size
self.model = get_model(self.model_config, self.p, ntags=len(self.p.vocab_tag))
self.model.load(filepath=os.path.join(dir_path, self.model_config.model_name, self.weight_file))
class Sequence(object):
config_file = 'config.json'
weight_file = 'model_weights.hdf5'
preprocessor_file = 'preprocessor.pkl'
# number of parallel worker for the data generator when not using ELMo
nb_workers = 6
def __init__(self,
model_name,
model_type="BidLSTM_CRF",
embeddings_name=None,
char_emb_size=25,
max_char_length=30,
char_lstm_units=25,
word_lstm_units=100,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=True,
use_crf=True,
batch_size=20,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
early_stop=True,
patience=5,
max_checkpoints_to_keep=5,
log_dir=None,
use_ELMo=True,
fold_number=1):
self.model = None
self.models = None
self.p = None
self.log_dir = log_dir
self.embeddings_name = embeddings_name
word_emb_size = 0
if embeddings_name is not None:
self.embeddings = Embeddings(embeddings_name, use_ELMo=use_ELMo)
word_emb_size = self.embeddings.embed_size
self.model_config = ModelConfig(model_name=model_name,
model_type=model_type,
embeddings_name=embeddings_name,
word_embedding_size=word_emb_size,
char_emb_size=char_emb_size,
char_lstm_units=char_lstm_units,
max_char_length=max_char_length,
word_lstm_units=word_lstm_units,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
use_crf=use_crf,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo)
self.training_config = TrainingConfig(batch_size, optimizer, learning_rate,
lr_decay, clip_gradients, max_epoch,
early_stop, patience,
max_checkpoints_to_keep)
def train(self, x_train, y_train, x_valid=None, y_valid=None):
# TBD if valid is None, segment train to get one
x_all = np.concatenate((x_train, x_valid), axis=0)
y_all = np.concatenate((y_train, y_valid), axis=0)
self.p = prepare_preprocessor(x_all, y_all, self.model_config)
self.model_config.char_vocab_size = len(self.p.vocab_char)
self.model_config.case_vocab_size = len(self.p.vocab_case)
"""
if self.embeddings.use_ELMo:
# dump token context independent data for the train set, done once for the training
x_train_local = x_train
if not self.training_config.early_stop:
# in case we want to train with the validation set too, we dump also
# the ELMo embeddings for the token of the valid set
x_train_local = np.concatenate((x_train, x_valid), axis=0)
self.embeddings.dump_ELMo_token_embeddings(x_train_local)
"""
self.model = get_model(self.model_config, self.p, len(self.p.vocab_tag))
trainer = Trainer(self.model,
self.models,
self.embeddings,
self.model_config,
self.training_config,
checkpoint_path=self.log_dir,
preprocessor=self.p
)
trainer.train(x_train, y_train, x_valid, y_valid)
if self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
def train_nfold(self, x_train, y_train, x_valid=None, y_valid=None, fold_number=10):
if x_valid is not None and y_valid is not None:
x_all = np.concatenate((x_train, x_valid), axis=0)
y_all = np.concatenate((y_train, y_valid), axis=0)
self.p = prepare_preprocessor(x_all, y_all, self.model_config)
else:
self.p = prepare_preprocessor(x_train, y_train, self.model_config)
self.model_config.char_vocab_size = len(self.p.vocab_char)
self.model_config.case_vocab_size = len(self.p.vocab_case)
self.p.return_lengths = True
#self.model = get_model(self.model_config, self.p, len(self.p.vocab_tag))
self.models = []
for k in range(0, fold_number):
model = get_model(self.model_config, self.p, len(self.p.vocab_tag))
self.models.append(model)
trainer = Trainer(self.model,
self.models,
self.embeddings,
self.model_config,
self.training_config,
checkpoint_path=self.log_dir,
preprocessor=self.p
)
trainer.train_nfold(x_train, y_train, x_valid, y_valid)
if self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
def eval(self, x_test, y_test):
if self.model_config.fold_number > 1 and self.models and len(self.models) == self.model_config.fold_number:
self.eval_nfold(x_test, y_test)
else:
self.eval_single(x_test, y_test)
def eval_single(self, x_test, y_test):
if self.model:
# Prepare test data(steps, generator)
test_generator = DataGenerator(x_test, y_test,
batch_size=self.training_config.batch_size, preprocessor=self.p,
char_embed_size=self.model_config.char_embedding_size,
embeddings=self.embeddings, shuffle=False)
# Build the evaluator and evaluate the model
scorer = Scorer(test_generator, self.p, evaluation=True)
scorer.model = self.model
scorer.on_epoch_end(epoch=-1)
else:
raise (OSError('Could not find a model.'))
def eval_nfold(self, x_test, y_test):
if self.models is not None:
total_f1 = 0
best_f1 = 0
best_index = 0
worst_f1 = 1
worst_index = 0
reports = []
total_precision = 0
total_recall = 0
for i in range(0, self.model_config.fold_number):
print('\n------------------------ fold ' + str(i) + '--------------------------------------')
# Prepare test data(steps, generator)
test_generator = DataGenerator(x_test, y_test,
batch_size=self.training_config.batch_size, preprocessor=self.p,
char_embed_size=self.model_config.char_embedding_size,
embeddings=self.embeddings, shuffle=False)
# Build the evaluator and evaluate the model
scorer = Scorer(test_generator, self.p, evaluation=True)
scorer.model = self.models[i]
scorer.on_epoch_end(epoch=-1)
f1 = scorer.f1
precision = scorer.precision
recall = scorer.recall
reports.append(scorer.report)
if best_f1 < f1:
best_f1 = f1
best_index = i
if worst_f1 > f1:
worst_f1 = f1
worst_index = i
total_f1 += f1
total_precision += precision
total_recall += recall
macro_f1 = total_f1 / self.model_config.fold_number
macro_precision = total_precision / self.model_config.fold_number
macro_recall = total_recall / self.model_config.fold_number
print("\naverage over", self.model_config.fold_number, "folds")
print("\tmacro f1 =", macro_f1)
print("\tmacro precision =", macro_precision)
print("\tmacro recall =", macro_recall, "\n")
print("\n** Worst ** model scores - \n")
print(reports[worst_index])
self.model = self.models[best_index]
print("\n** Best ** model scores - \n")
print(reports[best_index])
def tag(self, texts, output_format):
# annotate a list of sentences, return the list of annotations in the
# specified output_format
if self.model:
tagger = Tagger(self.model, self.model_config, self.embeddings, preprocessor=self.p)
start_time = time.time()
annotations = tagger.tag(texts, output_format)
runtime = round(time.time() - start_time, 3)
if output_format is 'json':
annotations["runtime"] = runtime
#else:
# print("runtime: %s seconds " % (runtime))
return annotations
else:
raise (OSError('Could not find a model.'))
def tag_file(self, file_in, output_format, file_out):
# Annotate a text file containing one sentence per line, the annotations are
# written in the output file if not None, in the standard output otherwise.
# Processing is streamed by batches so that we can process huge files without
# memory issues
if self.model:
tagger = Tagger(self.model, self.model_config, self.embeddings, preprocessor=self.p)
start_time = time.time()
if file_out is not None:
out = open(file_out,'w')
first = True
with open(file_in, 'r') as f:
texts = None
while texts is None or len(texts) == self.model_config.batch_size * self.nb_workers:
texts = next_n_lines(f, self.model_config.batch_size * self.nb_workers)
annotations = tagger.tag(texts, output_format)
# if the following is true, we just output the JSON returned by the tagger without any modification
directDump = False
if first:
first = False
if len(texts) < self.model_config.batch_size * self.nb_workers:
runtime = round(time.time() - start_time, 3)
annotations['runtime'] = runtime
jsonString = json.dumps(annotations, sort_keys=False, indent=4, ensure_ascii=False)
if file_out is None:
print(jsonString)
else:
out.write(jsonString)
directDump = True
else:
# we need to modify a bit the JSON outputted by the tagger to glue the different batches
# output the general information attributes
jsonString = '{\n "software": ' + json.dumps(annotations["software"], ensure_ascii=False) + ",\n"
jsonString += ' "date": ' + json.dumps(annotations["date"], ensure_ascii=False) + ",\n"
jsonString += ' "model": ' + json.dumps(annotations["model"], ensure_ascii=False) + ",\n"
jsonString += ' "texts": ['
if file_out is None:
print(jsonString, end='', flush=True)
else:
out.write(jsonString)
first = True
for jsonStr in annotations["texts"]:
jsonString = json.dumps(jsonStr, sort_keys=False, indent=4, ensure_ascii=False)
#jsonString = jsonString.replace('\n', '\n\t\t')
jsonString = re.sub('\n', '\n ', jsonString)
if file_out is None:
if not first:
print(',\n '+jsonString, end='', flush=True)
else:
first = False
print('\n '+jsonString, end='', flush=True)
else:
if not first:
out.write(',\n ')
out.write(jsonString)
else:
first = False
out.write('\n ')
out.write(jsonString)
else:
for jsonStr in annotations["texts"]:
jsonString = json.dumps(jsonStr, sort_keys=False, indent=4, ensure_ascii=False)
jsonString = re.sub('\n', '\n ', jsonString)
if file_out is None:
print(',\n '+jsonString, end='', flush=True)
else:
out.write(',\n ')
out.write(jsonString)
runtime = round(time.time() - start_time, 3)
if not directDump:
jsonString = "\n ],\n"
jsonString += ' "runtime": ' + str(runtime)
jsonString += "\n}\n"
if file_out is None:
print(jsonString)
else:
out.write(jsonString)
if file_out is not None:
out.close()
#print("runtime: %s seconds " % (runtime))
else:
raise (OSError('Could not find a model.'))
def save(self, dir_path='data/models/sequenceLabelling/'):
# create subfolder for the model if not already exists
directory = os.path.join(dir_path, self.model_config.model_name)
if not os.path.exists(directory):
os.makedirs(directory)
self.p.save(os.path.join(directory, self.preprocessor_file))
print('preprocessor saved')
self.model_config.save(os.path.join(directory, self.config_file))
print('model config file saved')
self.model.save(os.path.join(directory, self.weight_file))
print('model saved')
def load(self, dir_path='data/models/sequenceLabelling/'):
self.p = WordPreprocessor.load(os.path.join(dir_path, self.model_config.model_name, self.preprocessor_file))
self.model_config = ModelConfig.load(os.path.join(dir_path, self.model_config.model_name, self.config_file))
# load embeddings
self.embeddings = Embeddings(self.model_config.embeddings_name, use_ELMo=self.model_config.use_ELMo)
self.model_config.word_embedding_size = self.embeddings.embed_size
self.model = get_model(self.model_config, self.p, ntags=len(self.p.vocab_tag))
self.model.load(filepath=os.path.join(dir_path, self.model_config.model_name, self.weight_file))
class Sequence(object):
config_file = 'config.json'
weight_file = 'model_weights.hdf5'
preprocessor_file = 'preprocessor.json'
#preprocessor_file_new = 'preprocessor.json'
# number of parallel worker for the data generator when not using ELMo
nb_workers = 6
def __init__(self,
model_name,
model_type="BidLSTM_CRF",
embeddings_name=None,
char_emb_size=25,
max_char_length=30,
char_lstm_units=25,
word_lstm_units=100,
max_sequence_length=300,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=True,
use_crf=True,
batch_size=20,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
early_stop=True,
patience=5,
max_checkpoints_to_keep=5,
log_dir=None,
use_ELMo=False,
use_BERT=False,
fold_number=1,
multiprocessing=True,
features_indices=None):
self.model = None
self.models = None
self.p = None
self.log_dir = log_dir
self.embeddings_name = embeddings_name
word_emb_size = 0
if embeddings_name is not None:
self.embeddings = Embeddings(embeddings_name,
use_ELMo=use_ELMo,
use_BERT=use_BERT)
word_emb_size = self.embeddings.embed_size
else:
self.embeddings = None
self.model_config = ModelConfig(
model_name=model_name,
model_type=model_type,
embeddings_name=embeddings_name,
word_embedding_size=word_emb_size,
char_emb_size=char_emb_size,
char_lstm_units=char_lstm_units,
max_char_length=max_char_length,
word_lstm_units=word_lstm_units,
max_sequence_length=max_sequence_length,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
use_crf=use_crf,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo,
use_BERT=use_BERT,
features_indices=features_indices)
self.training_config = TrainingConfig(batch_size, optimizer,
learning_rate, lr_decay,
clip_gradients, max_epoch,
early_stop, patience,
max_checkpoints_to_keep,
multiprocessing)
def train(self,
x_train,
y_train,
f_train: np.array = None,
x_valid=None,
y_valid=None,
f_valid: np.array = None,
callbacks=None):
# TBD if valid is None, segment train to get one
x_all = np.concatenate(
(x_train, x_valid), axis=0) if x_valid is not None else x_train
y_all = np.concatenate(
(y_train, y_valid), axis=0) if y_valid is not None else y_train
features_all = concatenate_or_none((f_train, f_valid), axis=0)
self.p = prepare_preprocessor(x_all,
y_all,
features=features_all,
model_config=self.model_config)
self.model_config.char_vocab_size = len(self.p.vocab_char)
self.model_config.case_vocab_size = len(self.p.vocab_case)
self.model = get_model(self.model_config, self.p,
len(self.p.vocab_tag))
if self.p.return_features is not False:
print('x_train.shape: ', x_train.shape)
print('features_train.shape: ', f_train.shape)
sample_transformed_features = self.p.transform_features(f_train)
self.model_config.max_feature_size = np.asarray(
sample_transformed_features).shape[-1]
print('max_feature_size: ', self.model_config.max_feature_size)
trainer = Trainer(self.model,
self.models,
self.embeddings,
self.model_config,
self.training_config,
checkpoint_path=self.log_dir,
preprocessor=self.p)
trainer.train(x_train,
y_train,
x_valid,
y_valid,
features_train=f_train,
features_valid=f_valid,
callbacks=callbacks)
if self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
if self.embeddings.use_BERT:
self.embeddings.clean_BERT_cache()
def train_nfold(self,
x_train,
y_train,
x_valid=None,
y_valid=None,
f_train: np.array = None,
f_valid: np.array = None,
fold_number=10,
callbacks=None):
x_all = np.concatenate(
(x_train, x_valid), axis=0) if x_valid is not None else x_train
y_all = np.concatenate(
(y_train, y_valid), axis=0) if y_valid is not None else y_train
features_all = concatenate_or_none((f_train, f_valid), axis=0)
self.p = prepare_preprocessor(x_all,
y_all,
features=features_all,
model_config=self.model_config)
self.model_config.char_vocab_size = len(self.p.vocab_char)
self.model_config.case_vocab_size = len(self.p.vocab_case)
self.p.return_lengths = True
if 'bert' in self.model_config.model_type.lower():
self.model = get_model(self.model_config, self.p,
len(self.p.vocab_tag))
self.models = []
for k in range(0, fold_number):
model = get_model(self.model_config, self.p, len(self.p.vocab_tag))
self.models.append(model)
trainer = Trainer(self.model,
self.models,
self.embeddings,
self.model_config,
self.training_config,
checkpoint_path=self.log_dir,
preprocessor=self.p)
trainer.train_nfold(x_train,
y_train,
x_valid,
y_valid,
f_train=f_train,
f_valid=f_valid,
callbacks=callbacks)
if self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
if self.embeddings.use_BERT:
self.embeddings.clean_BERT_cache()
if 'bert' in self.model_config.model_type.lower():
self.save()
def eval(self, x_test, y_test, features=None):
if self.models and 1 < self.model_config.fold_number == len(
self.models):
self.eval_nfold(x_test, y_test, features=features)
else:
self.eval_single(x_test, y_test, features=features)
def eval_single(self, x_test, y_test, features=None):
if 'bert' not in self.model_config.model_type.lower():
if self.model:
# Prepare test data(steps, generator)
test_generator = DataGenerator(
x_test,
y_test,
batch_size=self.model_config.batch_size,
preprocessor=self.p,
char_embed_size=self.model_config.char_embedding_size,
max_sequence_length=self.model_config.max_sequence_length,
embeddings=self.embeddings,
shuffle=False,
features=features)
# Build the evaluator and evaluate the model
scorer = Scorer(test_generator, self.p, evaluation=True)
scorer.model = self.model
scorer.on_epoch_end(epoch=-1)
else:
raise (OSError('Could not find a model.'))
else:
# BERT architecture model
y_pred = self.model.predict(x_test, fold_id=-1)
nb_alignment_issues = 0
for i in range(len(y_test)):
if len(y_test[i]) != len(y_pred[i]):
nb_alignment_issues += 1
# BERT tokenizer appears to introduce some additional tokens without ## prefix,
# but this is normally handled when predicting.
# To be very conservative, the following ensure the number of tokens always
# match, but it should never be used in practice.
if len(y_test[i]) < len(y_pred[i]):
y_test[i] = y_test[i] + ["O"] * (len(y_pred[i]) -
len(y_test[i]))
if len(y_test[i]) > len(y_pred[i]):
y_pred[i] = y_pred[i] + ["O"] * (len(y_test[i]) -
len(y_pred[i]))
if nb_alignment_issues > 0:
print("number of alignment issues with test set:",
nb_alignment_issues)
report, report_as_map = classification_report(y_test,
y_pred,
digits=4)
print(report)
def eval_nfold(self, x_test, y_test, features=None):
if self.models is not None:
total_f1 = 0
best_f1 = 0
best_index = 0
worst_f1 = 1
worst_index = 0
reports = []
reports_as_map = []
total_precision = 0
total_recall = 0
for i in range(self.model_config.fold_number):
print('\n------------------------ fold ' + str(i) +
' --------------------------------------')
if 'bert' not in self.model_config.model_type.lower():
# Prepare test data(steps, generator)
test_generator = DataGenerator(
x_test,
y_test,
batch_size=self.model_config.batch_size,
preprocessor=self.p,
char_embed_size=self.model_config.char_embedding_size,
max_sequence_length=self.model_config.
max_sequence_length,
embeddings=self.embeddings,
shuffle=False,
features=features)
# Build the evaluator and evaluate the model
scorer = Scorer(test_generator, self.p, evaluation=True)
scorer.model = self.models[i]
scorer.on_epoch_end(epoch=-1)
f1 = scorer.f1
precision = scorer.precision
recall = scorer.recall
reports.append(scorer.report)
reports_as_map.append(scorer.report_as_map)
else:
# BERT architecture model
dir_path = 'data/models/sequenceLabelling/'
self.model_config = ModelConfig.load(
os.path.join(dir_path, self.model_config.model_name,
self.config_file))
self.p = WordPreprocessor.load(
os.path.join(dir_path, self.model_config.model_name,
self.preprocessor_file))
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag))
self.model.load_model(i)
y_pred = self.model.predict(x_test, fold_id=i)
nb_alignment_issues = 0
for j in range(len(y_test)):
if len(y_test[i]) != len(y_pred[j]):
nb_alignment_issues += 1
# BERT tokenizer appears to introduce some additional tokens without ## prefix,
# but this is normally handled when predicting.
# To be very conservative, the following ensure the number of tokens always
# match, but it should never be used in practice.
if len(y_test[j]) < len(y_pred[j]):
y_test[j] = y_test[j] + ["O"] * (
len(y_pred[j]) - len(y_test[j]))
if len(y_test[j]) > len(y_pred[j]):
y_pred[j] = y_pred[j] + ["O"] * (
len(y_test[j]) - len(y_pred[j]))
if nb_alignment_issues > 0:
print("number of alignment issues with test set:",
nb_alignment_issues)
f1 = f1_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
print("\tf1: {:04.2f}".format(f1 * 100))
print("\tprecision: {:04.2f}".format(precision * 100))
print("\trecall: {:04.2f}".format(recall * 100))
report, report_as_map = classification_report(y_test,
y_pred,
digits=4)
reports.append(report)
reports_as_map.append(report_as_map)
if best_f1 < f1:
best_f1 = f1
best_index = i
if worst_f1 > f1:
worst_f1 = f1
worst_index = i
total_f1 += f1
total_precision += precision
total_recall += recall
fold_average_evaluation = {'labels': {}, 'micro': {}, 'macro': {}}
micro_f1 = total_f1 / self.model_config.fold_number
micro_precision = total_precision / self.model_config.fold_number
micro_recall = total_recall / self.model_config.fold_number
micro_eval_block = {
'f1': micro_f1,
'precision': micro_precision,
'recall': micro_recall
}
fold_average_evaluation['micro'] = micro_eval_block
# field-level average over the n folds
labels = []
for label in sorted(self.p.vocab_tag):
if label == 'O' or label == '<PAD>':
continue
if label.startswith("B-") or label.startswith(
"S-") or label.startswith("I-") or label.startswith(
"E-"):
label = label[2:]
if label in labels:
continue
labels.append(label)
sum_p = 0
sum_r = 0
sum_f1 = 0
sum_support = 0
for j in range(0, self.model_config.fold_number):
if not label in reports_as_map[j]['labels']:
continue
report_as_map = reports_as_map[j]['labels'][label]
sum_p += report_as_map["precision"]
sum_r += report_as_map["recall"]
sum_f1 += report_as_map["f1"]
sum_support += report_as_map["support"]
avg_p = sum_p / self.model_config.fold_number
avg_r = sum_r / self.model_config.fold_number
avg_f1 = sum_f1 / self.model_config.fold_number
avg_support = sum_support / self.model_config.fold_number
avg_support_dec = str(avg_support - int(avg_support))[1:]
if avg_support_dec != '0':
avg_support = math.floor(avg_support)
block_label = {
'precision': avg_p,
'recall': avg_r,
'support': avg_support,
'f1': avg_f1
}
fold_average_evaluation['labels'][label] = block_label
print(
"----------------------------------------------------------------------"
)
print("\n** Worst ** model scores - run", str(worst_index))
print(reports[worst_index])
print("\n** Best ** model scores - run", str(best_index))
print(reports[best_index])
if 'bert' not in self.model_config.model_type.lower():
self.model = self.models[best_index]
else:
# copy best BERT model fold_number
best_model_dir = 'data/models/sequenceLabelling/' + self.model_config.model_name + str(
best_index)
new_model_dir = 'data/models/sequenceLabelling/' + self.model_config.model_name
# update new_model_dir if it already exists, keep its existing config content
merge_folders(best_model_dir, new_model_dir)
# clean other fold directory
for i in range(self.model_config.fold_number):
shutil.rmtree('data/models/sequenceLabelling/' +
self.model_config.model_name + str(i))
print(
"----------------------------------------------------------------------"
)
print("\nAverage over", self.model_config.fold_number, "folds")
print(
get_report(fold_average_evaluation,
digits=4,
include_avgs=['micro']))
def tag(self, texts, output_format, features=None):
# annotate a list of sentences, return the list of annotations in the
# specified output_format
if self.model:
tagger = Tagger(self.model,
self.model_config,
self.embeddings,
preprocessor=self.p)
start_time = time.time()
annotations = tagger.tag(texts, output_format, features=features)
runtime = round(time.time() - start_time, 3)
if output_format is 'json':
annotations["runtime"] = runtime
#else:
# print("runtime: %s seconds " % (runtime))
return annotations
else:
raise (OSError('Could not find a model.' + str(self.model)))
def tag_file(self, file_in, output_format, file_out):
# Annotate a text file containing one sentence per line, the annotations are
# written in the output file if not None, in the standard output otherwise.
# Processing is streamed by batches so that we can process huge files without
# memory issues
if self.model:
tagger = Tagger(self.model,
self.model_config,
self.embeddings,
preprocessor=self.p)
start_time = time.time()
if file_out is not None:
out = open(file_out, 'w')
first = True
with open(file_in, 'r') as f:
texts = None
while texts is None or len(
texts
) == self.model_config.batch_size * self.nb_workers:
texts = next_n_lines(
f, self.model_config.batch_size * self.nb_workers)
annotations = tagger.tag(texts, output_format)
# if the following is true, we just output the JSON returned by the tagger without any modification
directDump = False
if first:
first = False
if len(
texts
) < self.model_config.batch_size * self.nb_workers:
runtime = round(time.time() - start_time, 3)
annotations['runtime'] = runtime
jsonString = json.dumps(annotations,
sort_keys=False,
indent=4,
ensure_ascii=False)
if file_out is None:
print(jsonString)
else:
out.write(jsonString)
directDump = True
else:
# we need to modify a bit the JSON outputted by the tagger to glue the different batches
# output the general information attributes
jsonString = '{\n "software": ' + json.dumps(
annotations["software"],
ensure_ascii=False) + ",\n"
jsonString += ' "date": ' + json.dumps(
annotations["date"],
ensure_ascii=False) + ",\n"
jsonString += ' "model": ' + json.dumps(
annotations["model"],
ensure_ascii=False) + ",\n"
jsonString += ' "texts": ['
if file_out is None:
print(jsonString, end='', flush=True)
else:
out.write(jsonString)
first = True
for jsonStr in annotations["texts"]:
jsonString = json.dumps(jsonStr,
sort_keys=False,
indent=4,
ensure_ascii=False)
#jsonString = jsonString.replace('\n', '\n\t\t')
jsonString = re.sub('\n', '\n ',
jsonString)
if file_out is None:
if not first:
print(',\n ' + jsonString,
end='',
flush=True)
else:
first = False
print('\n ' + jsonString,
end='',
flush=True)
else:
if not first:
out.write(',\n ')
out.write(jsonString)
else:
first = False
out.write('\n ')
out.write(jsonString)
else:
for jsonStr in annotations["texts"]:
jsonString = json.dumps(jsonStr,
sort_keys=False,
indent=4,
ensure_ascii=False)
jsonString = re.sub('\n', '\n ', jsonString)
if file_out is None:
print(',\n ' + jsonString,
end='',
flush=True)
else:
out.write(',\n ')
out.write(jsonString)
runtime = round(time.time() - start_time, 3)
if not directDump:
jsonString = "\n ],\n"
jsonString += ' "runtime": ' + str(runtime)
jsonString += "\n}\n"
if file_out is None:
print(jsonString)
else:
out.write(jsonString)
if file_out is not None:
out.close()
#print("runtime: %s seconds " % (runtime))
else:
raise (OSError('Could not find a model.'))
def save(self, dir_path='data/models/sequenceLabelling/'):
# create subfolder for the model if not already exists
directory = os.path.join(dir_path, self.model_config.model_name)
if not os.path.exists(directory):
os.makedirs(directory)
self.model_config.save(os.path.join(directory, self.config_file))
print('model config file saved')
self.p.save(os.path.join(directory, self.preprocessor_file))
print('preprocessor saved')
# bert model are always saved via training process steps as checkpoint
if self.model_config.model_type.lower().find("bert") == -1:
if self.model is None and self.model_config.fold_number != 0 and self.model_config.fold_number != 1:
print(
'Error: model not saved. Evaluation need to be called first to select the best fold model to be saved'
)
else:
self.model.save(os.path.join(directory, self.weight_file))
print('model saved')
def load(self, dir_path='data/models/sequenceLabelling/'):
self.model_config = ModelConfig.load(
os.path.join(dir_path, self.model_config.model_name,
self.config_file))
self.p = WordPreprocessor.load(
os.path.join(dir_path, self.model_config.model_name,
self.preprocessor_file))
if self.model_config.model_type.lower().find("bert") != -1:
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag),
dir_path=dir_path)
self.model.load_model()
return
# load embeddings
# Do not use cache in 'production' mode
self.embeddings = Embeddings(self.model_config.embeddings_name,
use_ELMo=self.model_config.use_ELMo,
use_BERT=self.model_config.use_BERT,
use_cache=False)
self.model_config.word_embedding_size = self.embeddings.embed_size
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag))
self.model.load(filepath=os.path.join(
dir_path, self.model_config.model_name, self.weight_file))
def eval_nfold(self, x_test, y_test, features=None):
if self.models is not None:
total_f1 = 0
best_f1 = 0
best_index = 0
worst_f1 = 1
worst_index = 0
reports = []
reports_as_map = []
total_precision = 0
total_recall = 0
for i in range(self.model_config.fold_number):
print('\n------------------------ fold ' + str(i) +
' --------------------------------------')
if 'bert' not in self.model_config.model_type.lower():
# Prepare test data(steps, generator)
test_generator = DataGenerator(
x_test,
y_test,
batch_size=self.model_config.batch_size,
preprocessor=self.p,
char_embed_size=self.model_config.char_embedding_size,
max_sequence_length=self.model_config.
max_sequence_length,
embeddings=self.embeddings,
shuffle=False,
features=features)
# Build the evaluator and evaluate the model
scorer = Scorer(test_generator, self.p, evaluation=True)
scorer.model = self.models[i]
scorer.on_epoch_end(epoch=-1)
f1 = scorer.f1
precision = scorer.precision
recall = scorer.recall
reports.append(scorer.report)
reports_as_map.append(scorer.report_as_map)
else:
# BERT architecture model
dir_path = 'data/models/sequenceLabelling/'
self.model_config = ModelConfig.load(
os.path.join(dir_path, self.model_config.model_name,
self.config_file))
self.p = WordPreprocessor.load(
os.path.join(dir_path, self.model_config.model_name,
self.preprocessor_file))
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag))
self.model.load_model(i)
y_pred = self.model.predict(x_test, fold_id=i)
nb_alignment_issues = 0
for j in range(len(y_test)):
if len(y_test[i]) != len(y_pred[j]):
nb_alignment_issues += 1
# BERT tokenizer appears to introduce some additional tokens without ## prefix,
# but this is normally handled when predicting.
# To be very conservative, the following ensure the number of tokens always
# match, but it should never be used in practice.
if len(y_test[j]) < len(y_pred[j]):
y_test[j] = y_test[j] + ["O"] * (
len(y_pred[j]) - len(y_test[j]))
if len(y_test[j]) > len(y_pred[j]):
y_pred[j] = y_pred[j] + ["O"] * (
len(y_test[j]) - len(y_pred[j]))
if nb_alignment_issues > 0:
print("number of alignment issues with test set:",
nb_alignment_issues)
f1 = f1_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
print("\tf1: {:04.2f}".format(f1 * 100))
print("\tprecision: {:04.2f}".format(precision * 100))
print("\trecall: {:04.2f}".format(recall * 100))
report, report_as_map = classification_report(y_test,
y_pred,
digits=4)
reports.append(report)
reports_as_map.append(report_as_map)
if best_f1 < f1:
best_f1 = f1
best_index = i
if worst_f1 > f1:
worst_f1 = f1
worst_index = i
total_f1 += f1
total_precision += precision
total_recall += recall
fold_average_evaluation = {'labels': {}, 'micro': {}, 'macro': {}}
micro_f1 = total_f1 / self.model_config.fold_number
micro_precision = total_precision / self.model_config.fold_number
micro_recall = total_recall / self.model_config.fold_number
micro_eval_block = {
'f1': micro_f1,
'precision': micro_precision,
'recall': micro_recall
}
fold_average_evaluation['micro'] = micro_eval_block
# field-level average over the n folds
labels = []
for label in sorted(self.p.vocab_tag):
if label == 'O' or label == '<PAD>':
continue
if label.startswith("B-") or label.startswith(
"S-") or label.startswith("I-") or label.startswith(
"E-"):
label = label[2:]
if label in labels:
continue
labels.append(label)
sum_p = 0
sum_r = 0
sum_f1 = 0
sum_support = 0
for j in range(0, self.model_config.fold_number):
if not label in reports_as_map[j]['labels']:
continue
report_as_map = reports_as_map[j]['labels'][label]
sum_p += report_as_map["precision"]
sum_r += report_as_map["recall"]
sum_f1 += report_as_map["f1"]
sum_support += report_as_map["support"]
avg_p = sum_p / self.model_config.fold_number
avg_r = sum_r / self.model_config.fold_number
avg_f1 = sum_f1 / self.model_config.fold_number
avg_support = sum_support / self.model_config.fold_number
avg_support_dec = str(avg_support - int(avg_support))[1:]
if avg_support_dec != '0':
avg_support = math.floor(avg_support)
block_label = {
'precision': avg_p,
'recall': avg_r,
'support': avg_support,
'f1': avg_f1
}
fold_average_evaluation['labels'][label] = block_label
print(
"----------------------------------------------------------------------"
)
print("\n** Worst ** model scores - run", str(worst_index))
print(reports[worst_index])
print("\n** Best ** model scores - run", str(best_index))
print(reports[best_index])
if 'bert' not in self.model_config.model_type.lower():
self.model = self.models[best_index]
else:
# copy best BERT model fold_number
best_model_dir = 'data/models/sequenceLabelling/' + self.model_config.model_name + str(
best_index)
new_model_dir = 'data/models/sequenceLabelling/' + self.model_config.model_name
# update new_model_dir if it already exists, keep its existing config content
merge_folders(best_model_dir, new_model_dir)
# clean other fold directory
for i in range(self.model_config.fold_number):
shutil.rmtree('data/models/sequenceLabelling/' +
self.model_config.model_name + str(i))
print(
"----------------------------------------------------------------------"
)
print("\nAverage over", self.model_config.fold_number, "folds")
print(
get_report(fold_average_evaluation,
digits=4,
include_avgs=['micro']))
def __init__(self,
model_name=None,
architecture=None,
embeddings_name=None,
char_emb_size=25,
max_char_length=30,
char_lstm_units=25,
word_lstm_units=100,
max_sequence_length=300,
dropout=0.5,
recurrent_dropout=0.25,
batch_size=20,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
early_stop=True,
patience=5,
max_checkpoints_to_keep=0,
use_ELMo=False,
log_dir=None,
fold_number=1,
multiprocessing=True,
features_indices=None,
transformer_name: str = None):
if model_name is None:
# add a dummy name based on the architecture
model_name = architecture
if embeddings_name is not None:
model_name += "_" + embeddings_name
if transformer_name is not None:
model_name += "_" + transformer_name
self.model = None
self.models = None
self.p: Preprocessor = None
self.log_dir = log_dir
self.embeddings_name = embeddings_name
word_emb_size = 0
self.embeddings = None
self.model_local_path = None
self.registry = load_resource_registry("delft/resources-registry.json")
if self.embeddings_name is not None:
self.embeddings = Embeddings(self.embeddings_name,
resource_registry=self.registry,
use_ELMo=use_ELMo)
word_emb_size = self.embeddings.embed_size
else:
self.embeddings = None
word_emb_size = 0
self.model_config = ModelConfig(
model_name=model_name,
architecture=architecture,
embeddings_name=embeddings_name,
word_embedding_size=word_emb_size,
char_emb_size=char_emb_size,
char_lstm_units=char_lstm_units,
max_char_length=max_char_length,
word_lstm_units=word_lstm_units,
max_sequence_length=max_sequence_length,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo,
features_indices=features_indices,
transformer_name=transformer_name)
self.training_config = TrainingConfig(batch_size, optimizer,
learning_rate, lr_decay,
clip_gradients, max_epoch,
early_stop, patience,
max_checkpoints_to_keep,
multiprocessing)
class Sequence(object):
# number of parallel worker for the data generator
nb_workers = 6
def __init__(self,
model_name=None,
architecture=None,
embeddings_name=None,
char_emb_size=25,
max_char_length=30,
char_lstm_units=25,
word_lstm_units=100,
max_sequence_length=300,
dropout=0.5,
recurrent_dropout=0.25,
batch_size=20,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
early_stop=True,
patience=5,
max_checkpoints_to_keep=0,
use_ELMo=False,
log_dir=None,
fold_number=1,
multiprocessing=True,
features_indices=None,
transformer_name: str = None):
if model_name is None:
# add a dummy name based on the architecture
model_name = architecture
if embeddings_name is not None:
model_name += "_" + embeddings_name
if transformer_name is not None:
model_name += "_" + transformer_name
self.model = None
self.models = None
self.p: Preprocessor = None
self.log_dir = log_dir
self.embeddings_name = embeddings_name
word_emb_size = 0
self.embeddings = None
self.model_local_path = None
self.registry = load_resource_registry("delft/resources-registry.json")
if self.embeddings_name is not None:
self.embeddings = Embeddings(self.embeddings_name,
resource_registry=self.registry,
use_ELMo=use_ELMo)
word_emb_size = self.embeddings.embed_size
else:
self.embeddings = None
word_emb_size = 0
self.model_config = ModelConfig(
model_name=model_name,
architecture=architecture,
embeddings_name=embeddings_name,
word_embedding_size=word_emb_size,
char_emb_size=char_emb_size,
char_lstm_units=char_lstm_units,
max_char_length=max_char_length,
word_lstm_units=word_lstm_units,
max_sequence_length=max_sequence_length,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo,
features_indices=features_indices,
transformer_name=transformer_name)
self.training_config = TrainingConfig(batch_size, optimizer,
learning_rate, lr_decay,
clip_gradients, max_epoch,
early_stop, patience,
max_checkpoints_to_keep,
multiprocessing)
def train(self,
x_train,
y_train,
f_train=None,
x_valid=None,
y_valid=None,
f_valid=None,
callbacks=None):
# TBD if valid is None, segment train to get one if early_stop is True
# we concatenate all the training+validation data to create the model vocabulary
if not x_valid is None:
x_all = np.concatenate((x_train, x_valid), axis=0)
else:
x_all = x_train
if not y_valid is None:
y_all = np.concatenate((y_train, y_valid), axis=0)
else:
y_all = y_train
features_all = concatenate_or_none((f_train, f_valid), axis=0)
self.p = prepare_preprocessor(x_all,
y_all,
features=features_all,
model_config=self.model_config)
self.model_config.char_vocab_size = len(self.p.vocab_char)
self.model_config.case_vocab_size = len(self.p.vocab_case)
self.model = get_model(self.model_config,
self.p,
len(self.p.vocab_tag),
load_pretrained_weights=True)
print_parameters(self.model_config, self.training_config)
self.model.print_summary()
# uncomment to plot graph
#plot_model(self.model,
# to_file='data/models/textClassification/'+self.model_config.model_name+'_'+self.model_config.architecture+'.png')
trainer = Trainer(
self.model,
self.models,
self.embeddings,
self.model_config,
self.training_config,
checkpoint_path=self.log_dir,
preprocessor=self.p,
transformer_preprocessor=self.model.transformer_preprocessor)
trainer.train(x_train,
y_train,
x_valid,
y_valid,
features_train=f_train,
features_valid=f_valid,
callbacks=callbacks)
if self.embeddings and self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
def train_nfold(self,
x_train,
y_train,
x_valid=None,
y_valid=None,
f_train=None,
f_valid=None,
callbacks=None):
x_all = np.concatenate(
(x_train, x_valid), axis=0) if x_valid is not None else x_train
y_all = np.concatenate(
(y_train, y_valid), axis=0) if y_valid is not None else y_train
features_all = concatenate_or_none((f_train, f_valid), axis=0)
self.p = prepare_preprocessor(x_all,
y_all,
features=features_all,
model_config=self.model_config)
self.model_config.char_vocab_size = len(self.p.vocab_char)
self.model_config.case_vocab_size = len(self.p.vocab_case)
self.models = []
trainer = Trainer(self.model,
self.models,
self.embeddings,
self.model_config,
self.training_config,
checkpoint_path=self.log_dir,
preprocessor=self.p)
trainer.train_nfold(x_train,
y_train,
x_valid,
y_valid,
f_train=f_train,
f_valid=f_valid,
callbacks=callbacks)
if self.embeddings and self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
def eval(self, x_test, y_test, features=None):
if self.model_config.fold_number > 1:
self.eval_nfold(x_test, y_test, features=features)
else:
self.eval_single(x_test, y_test, features=features)
def eval_single(self, x_test, y_test, features=None):
if self.model is None:
raise (OSError('Could not find a model.'))
print_parameters(self.model_config, self.training_config)
self.model.print_summary()
if self.model_config.transformer_name is None:
# we can use a data generator for evaluation
# Prepare test data(steps, generator)
generator = self.model.get_generator()
test_generator = generator(
x_test,
y_test,
batch_size=self.model_config.batch_size,
preprocessor=self.p,
char_embed_size=self.model_config.char_embedding_size,
max_sequence_length=self.model_config.max_sequence_length,
embeddings=self.embeddings,
shuffle=False,
features=features,
output_input_offsets=True,
use_chain_crf=self.model_config.use_chain_crf)
# Build the evaluator and evaluate the model
scorer = Scorer(test_generator,
self.p,
evaluation=True,
use_crf=self.model_config.use_crf,
use_chain_crf=self.model_config.use_chain_crf)
scorer.model = self.model
scorer.on_epoch_end(epoch=-1)
else:
# the architecture model uses a transformer layer
# note that we could also use the above test_generator, but as an alternative here we check the
# test/prediction alignment of tokens and the validity of the maximum sequence input length
# wrt the length of the test sequences
tagger = Tagger(
self.model,
self.model_config,
self.embeddings,
preprocessor=self.p,
transformer_preprocessor=self.model.transformer_preprocessor)
y_pred_pairs = tagger.tag(x_test,
output_format=None,
features=features)
# keep only labels
y_pred = []
for result in y_pred_pairs:
result_labels = []
for pair in result:
result_labels.append(pair[1])
y_pred.append(result_labels)
nb_alignment_issues = 0
for i in range(len(y_test)):
if len(y_test[i]) != len(y_pred[i]):
#print("y_test:", y_test[i])
#print("y_pred:", y_pred[i])
nb_alignment_issues += 1
# BERT tokenizer appears to introduce some additional tokens without ## prefix,
# but we normally handled that well when predicting.
# To be very conservative, the following ensure the number of tokens always
# match, but it should never be used in practice.
if len(y_test[i]) < len(y_pred[i]):
y_test[i] = y_test[i] + ["O"] * (len(y_pred[i]) -
len(y_test[i]))
if len(y_test[i]) > len(y_pred[i]):
y_pred[i] = y_pred[i] + ["O"] * (len(y_test[i]) -
len(y_pred[i]))
if nb_alignment_issues > 0:
print("number of alignment issues with test set:",
nb_alignment_issues)
print(
"to solve them consider increasing the maximum sequence input length of the model and retrain"
)
report, report_as_map = classification_report(y_test,
y_pred,
digits=4)
print(report)
def eval_nfold(self, x_test, y_test, features=None):
if self.models is not None:
total_f1 = 0
best_f1 = 0
best_index = 0
worst_f1 = 1
worst_index = 0
reports = []
reports_as_map = []
total_precision = 0
total_recall = 0
for i in range(self.model_config.fold_number):
if self.model_config.transformer_name is None:
the_model = self.models[i]
bert_preprocessor = None
else:
# the architecture model uses a transformer layer, it is large and needs to be loaded from disk
dir_path = 'data/models/sequenceLabelling/'
weight_file = DEFAULT_WEIGHT_FILE_NAME.replace(
".hdf5",
str(i) + ".hdf5")
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag),
load_pretrained_weights=False,
local_path=os.path.join(
dir_path,
self.model_config.model_name))
self.model.load(filepath=os.path.join(
dir_path, self.model_config.model_name, weight_file))
the_model = self.model
bert_preprocessor = self.model.transformer_preprocessor
if i == 0:
the_model.print_summary()
print_parameters(self.model_config, self.training_config)
print('\n------------------------ fold ' + str(i) +
' --------------------------------------')
# we can use a data generator for evaluation
# Prepare test data(steps, generator)
generator = the_model.get_generator()
test_generator = generator(
x_test,
y_test,
batch_size=self.model_config.batch_size,
preprocessor=self.p,
bert_preprocessor=bert_preprocessor,
char_embed_size=self.model_config.char_embedding_size,
max_sequence_length=self.model_config.max_sequence_length,
embeddings=self.embeddings,
shuffle=False,
features=features,
output_input_offsets=True,
use_chain_crf=self.model_config.use_chain_crf)
# Build the evaluator and evaluate the model
scorer = Scorer(test_generator,
self.p,
evaluation=True,
use_crf=self.model_config.use_crf,
use_chain_crf=self.model_config.use_chain_crf)
scorer.model = the_model
scorer.on_epoch_end(epoch=-1)
f1 = scorer.f1
precision = scorer.precision
recall = scorer.recall
reports.append(scorer.report)
reports_as_map.append(scorer.report_as_map)
if best_f1 < f1:
best_f1 = f1
best_index = i
if worst_f1 > f1:
worst_f1 = f1
worst_index = i
total_f1 += f1
total_precision += precision
total_recall += recall
fold_average_evaluation = {'labels': {}, 'micro': {}, 'macro': {}}
micro_f1 = total_f1 / self.model_config.fold_number
micro_precision = total_precision / self.model_config.fold_number
micro_recall = total_recall / self.model_config.fold_number
micro_eval_block = {
'f1': micro_f1,
'precision': micro_precision,
'recall': micro_recall
}
fold_average_evaluation['micro'] = micro_eval_block
# field-level average over the n folds
labels = []
for label in sorted(self.p.vocab_tag):
if label == 'O' or label == '<PAD>':
continue
if label.startswith("B-") or label.startswith(
"S-") or label.startswith("I-") or label.startswith(
"E-"):
label = label[2:]
if label in labels:
continue
labels.append(label)
sum_p = 0
sum_r = 0
sum_f1 = 0
sum_support = 0
for j in range(0, self.model_config.fold_number):
if label not in reports_as_map[j]['labels']:
continue
report_as_map = reports_as_map[j]['labels'][label]
sum_p += report_as_map["precision"]
sum_r += report_as_map["recall"]
sum_f1 += report_as_map["f1"]
sum_support += report_as_map["support"]
avg_p = sum_p / self.model_config.fold_number
avg_r = sum_r / self.model_config.fold_number
avg_f1 = sum_f1 / self.model_config.fold_number
avg_support = sum_support / self.model_config.fold_number
avg_support_dec = str(avg_support - int(avg_support))[1:]
if avg_support_dec != '0':
avg_support = math.floor(avg_support)
block_label = {
'precision': avg_p,
'recall': avg_r,
'support': avg_support,
'f1': avg_f1
}
fold_average_evaluation['labels'][label] = block_label
print(
"----------------------------------------------------------------------"
)
print("\n** Worst ** model scores - run", str(worst_index))
print(reports[worst_index])
print("\n** Best ** model scores - run", str(best_index))
print(reports[best_index])
fold_nb = self.model_config.fold_number
self.model_config.fold_number = 1
if self.model_config.transformer_name is None:
self.model = self.models[best_index]
else:
dir_path = 'data/models/sequenceLabelling/'
weight_file = DEFAULT_WEIGHT_FILE_NAME.replace(
".hdf5",
str(best_index) + ".hdf5")
# saved config file must be updated to single fold
self.model.load(filepath=os.path.join(
dir_path, self.model_config.model_name, weight_file))
print(
"----------------------------------------------------------------------"
)
print("\nAverage over", str(int(fold_nb)), "folds")
print(
get_report(fold_average_evaluation,
digits=4,
include_avgs=['micro']))
def tag(self, texts, output_format, features=None, batch_size=None):
# annotate a list of sentences, return the list of annotations in the
# specified output_format
if batch_size != None:
self.model_config.batch_size = batch_size
print("---")
print("batch_size (prediction):", self.model_config.batch_size)
print("---")
if self.model:
tagger = Tagger(
self.model,
self.model_config,
self.embeddings,
preprocessor=self.p,
transformer_preprocessor=self.model.transformer_preprocessor)
start_time = time.time()
annotations = tagger.tag(texts, output_format, features=features)
runtime = round(time.time() - start_time, 3)
if output_format == 'json':
annotations["runtime"] = runtime
#else:
# print("runtime: %s seconds " % (runtime))
return annotations
else:
raise (OSError('Could not find a model.' + str(self.model)))
def tag_file(self, file_in, output_format, file_out, batch_size=None):
# Annotate a text file containing one sentence per line, the annotations are
# written in the output file if not None, in the standard output otherwise.
# Processing is streamed by batches so that we can process huge files without
# memory issues
if batch_size != None:
self.model_config.batch_size = batch_size
print("---")
print("batch_size (prediction):", self.model_config.batch_size)
print("---")
if self.model:
tagger = Tagger(
self.model,
self.model_config,
self.embeddings,
preprocessor=self.p,
transformer_preprocessor=self.model.transformer_preprocessor)
start_time = time.time()
if file_out != None:
out = open(file_out, 'w')
first = True
with open(file_in, 'r') as f:
texts = None
while texts == None or len(
texts
) == self.model_config.batch_size * self.nb_workers:
texts = next_n_lines(
f, self.model_config.batch_size * self.nb_workers)
annotations = tagger.tag(texts, output_format)
# if the following is true, we just output the JSON returned by the tagger without any modification
directDump = False
if first:
first = False
if len(
texts
) < self.model_config.batch_size * self.nb_workers:
runtime = round(time.time() - start_time, 3)
annotations['runtime'] = runtime
jsonString = json.dumps(annotations,
sort_keys=False,
indent=4,
ensure_ascii=False)
if file_out == None:
print(jsonString)
else:
out.write(jsonString)
directDump = True
else:
# we need to modify a bit the JSON outputted by the tagger to glue the different batches
# output the general information attributes
jsonString = '{\n "software": ' + json.dumps(
annotations["software"],
ensure_ascii=False) + ",\n"
jsonString += ' "date": ' + json.dumps(
annotations["date"],
ensure_ascii=False) + ",\n"
jsonString += ' "model": ' + json.dumps(
annotations["model"],
ensure_ascii=False) + ",\n"
jsonString += ' "texts": ['
if file_out == None:
print(jsonString, end='', flush=True)
else:
out.write(jsonString)
first = True
for jsonStr in annotations["texts"]:
jsonString = json.dumps(jsonStr,
sort_keys=False,
indent=4,
ensure_ascii=False)
#jsonString = jsonString.replace('\n', '\n\t\t')
jsonString = re.sub('\n', '\n ',
jsonString)
if file_out == None:
if not first:
print(',\n ' + jsonString,
end='',
flush=True)
else:
first = False
print('\n ' + jsonString,
end='',
flush=True)
else:
if not first:
out.write(',\n ')
out.write(jsonString)
else:
first = False
out.write('\n ')
out.write(jsonString)
else:
for jsonStr in annotations["texts"]:
jsonString = json.dumps(jsonStr,
sort_keys=False,
indent=4,
ensure_ascii=False)
jsonString = re.sub('\n', '\n ', jsonString)
if file_out == None:
print(',\n ' + jsonString,
end='',
flush=True)
else:
out.write(',\n ')
out.write(jsonString)
runtime = round(time.time() - start_time, 3)
if not directDump:
jsonString = "\n ],\n"
jsonString += ' "runtime": ' + str(runtime)
jsonString += "\n}\n"
if file_out == None:
print(jsonString)
else:
out.write(jsonString)
if file_out != None:
out.close()
#print("runtime: %s seconds " % (runtime))
else:
raise (OSError('Could not find a model.'))
def save(self,
dir_path='data/models/sequenceLabelling/',
weight_file=DEFAULT_WEIGHT_FILE_NAME):
# create subfolder for the model if not already exists
directory = os.path.join(dir_path, self.model_config.model_name)
if not os.path.exists(directory):
os.makedirs(directory)
self.model_config.save(os.path.join(directory, CONFIG_FILE_NAME))
print('model config file saved')
self.p.save(os.path.join(directory, PROCESSOR_FILE_NAME))
print('preprocessor saved')
if self.model is None and self.model_config.fold_number > 1:
print(
'Error: model not saved. Evaluation need to be called first to select the best fold model to be saved'
)
else:
self.model.save(os.path.join(directory, weight_file))
# save pretrained transformer config if used in the model
if self.model.transformer_config is not None:
self.model.transformer_config.to_json_file(
os.path.join(directory, TRANSFORMER_CONFIG_FILE_NAME))
print('transformer config saved')
if self.model.transformer_preprocessor is not None:
self.model.transformer_preprocessor.tokenizer.save_pretrained(
os.path.join(directory, DEFAULT_TRANSFORMER_TOKENIZER_DIR))
print('transformer tokenizer saved')
print('model saved')
def load(self,
dir_path='data/models/sequenceLabelling/',
weight_file=DEFAULT_WEIGHT_FILE_NAME):
model_path = os.path.join(dir_path, self.model_config.model_name)
self.model_config = ModelConfig.load(
os.path.join(model_path, CONFIG_FILE_NAME))
if self.model_config.embeddings_name is not None:
# load embeddings
# Do not use cache in 'prediction/production' mode
self.embeddings = Embeddings(self.model_config.embeddings_name,
resource_registry=self.registry,
use_ELMo=self.model_config.use_ELMo,
use_cache=False)
self.model_config.word_embedding_size = self.embeddings.embed_size
else:
self.embeddings = None
self.model_config.word_embedding_size = 0
self.p = Preprocessor.load(
os.path.join(dir_path, self.model_config.model_name,
PROCESSOR_FILE_NAME))
self.model = get_model(self.model_config,
self.p,
ntags=len(self.p.vocab_tag),
load_pretrained_weights=False,
local_path=os.path.join(
dir_path, self.model_config.model_name))
print(
"load weights from",
os.path.join(dir_path, self.model_config.model_name, weight_file))
self.model.load(filepath=os.path.join(
dir_path, self.model_config.model_name, weight_file))
self.model.print_summary()