def __init__(self,
model_name="",
model_type="gru",
embeddings_name=None,
list_classes=[],
char_emb_size=25,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=False,
batch_size=256,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
patience=5,
log_dir=None,
maxlen=300,
fold_number=1,
use_roc_auc=True,
use_ELMo=False,
use_BERT=False,
embeddings=(),
class_weights=None,
multiprocessing=True):
self.model = None
self.models = None
self.log_dir = log_dir
self.embeddings_name = embeddings_name
word_emb_size = 0
if embeddings_name is not None and model_type.find("bert") == -1:
self.embeddings = Embeddings(embeddings_name, use_ELMo=use_ELMo, use_BERT=use_BERT)
word_emb_size = self.embeddings.embed_size
self.model_config = ModelConfig(model_name=model_name,
model_type=model_type,
embeddings_name=embeddings_name,
list_classes=list_classes,
char_emb_size=char_emb_size,
word_emb_size=word_emb_size,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
maxlen=maxlen,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo,
use_BERT=use_BERT)
self.training_config = TrainingConfig(batch_size, optimizer, learning_rate,
lr_decay, clip_gradients, max_epoch,
patience, use_roc_auc,
class_weights=class_weights, multiprocessing=multiprocessing)
def load(self, dir_path='data/models/textClassification/'):
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,
use_BERT=self.model_config.use_BERT)
self.model_config.word_embedding_size = self.embeddings.embed_size
self.model = getModel(self.model_config, self.training_config)
if self.model_config.fold_number is 1:
self.model.load_weights(
os.path.join(
dir_path, self.model_config.model_name,
self.model_config.model_type + "." + self.weight_file))
else:
self.models = []
for i in range(0, self.model_config.fold_number):
local_model = getModel(self.model_config, self.training_config)
local_model.load_weights(
os.path.join(
dir_path, self.model_config.model_name,
self.model_config.model_type +
".model{0}_weights.hdf5".format(i)))
self.models.append(local_model)
def load(self, dir_path='data/models/textClassification/'):
model_path = os.path.join(dir_path, self.model_config.model_name)
self.model_config = ModelConfig.load(os.path.join(model_path, self.config_file))
if self.model_config.transformer_name is None:
# load embeddings
# Do not use cache in 'production' mode
self.embeddings = Embeddings(self.model_config.embeddings_name, resource_registry=self.registry, use_cache=False)
self.model_config.word_embedding_size = self.embeddings.embed_size
else:
self.transformer_name = self.model_config.transformer_name
self.embeddings = None
self.model = getModel(self.model_config,
self.training_config,
load_pretrained_weights=False,
local_path=model_path)
print_parameters(self.model_config, self.training_config)
self.model.print_summary()
if self.model_config.fold_number == 1:
print("load weights from", os.path.join(model_path, self.weight_file))
self.model.load(os.path.join(model_path, self.weight_file))
else:
self.models = []
if self.model_config.transformer_name is None:
for i in range(0, self.model_config.fold_number):
local_model = getModel(self.model_config,
self.training_config,
load_pretrained_weights=False,
local_path=model_path)
local_model.load(os.path.join(model_path, "model{0}_weights.hdf5".format(i)))
self.models.append(local_model)
else:
# only init first fold one, the other will be init at prediction time, all weights will be loaded at prediction time
local_model = getModel(self.model_config,
self.training_config,
load_pretrained_weights=False,
local_path=model_path)
self.models.append(local_model)
class Classifier(object):
config_file = 'config.json'
weight_file = 'model_weights.hdf5'
def __init__(self,
model_name="",
model_type="gru",
embeddings_name=None,
list_classes=[],
char_emb_size=25,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=False,
batch_size=256,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
patience=5,
log_dir=None,
maxlen=300,
fold_number=1,
use_roc_auc=True,
use_ELMo=False,
use_BERT=False,
embeddings=(),
class_weights=None,
multiprocessing=True):
self.model = None
self.models = None
self.log_dir = log_dir
self.embeddings_name = embeddings_name
word_emb_size = 0
if embeddings_name is not None and model_type.find("bert") == -1:
self.embeddings = Embeddings(embeddings_name, use_ELMo=use_ELMo, use_BERT=use_BERT)
word_emb_size = self.embeddings.embed_size
self.model_config = ModelConfig(model_name=model_name,
model_type=model_type,
embeddings_name=embeddings_name,
list_classes=list_classes,
char_emb_size=char_emb_size,
word_emb_size=word_emb_size,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
maxlen=maxlen,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo,
use_BERT=use_BERT)
self.training_config = TrainingConfig(batch_size, optimizer, learning_rate,
lr_decay, clip_gradients, max_epoch,
patience, use_roc_auc,
class_weights=class_weights, multiprocessing=multiprocessing)
def train(self, x_train, y_train, vocab_init=None, callbacks=None):
self.model = getModel(self.model_config, self.training_config)
# bert models
if self.model_config.model_type.find("bert") != -1:
self.model.processor = BERT_classifier_processor(labels=self.model_config.list_classes, x_train=x_train, y_train=y_train)
self.model.train()
return
# create validation set in case we don't use k-folds
xtr, val_x, y, val_y = train_test_split(x_train, y_train, test_size=0.1)
training_generator = DataGenerator(xtr, y, batch_size=self.training_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=True)
validation_generator = DataGenerator(val_x, None, batch_size=self.training_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False)
# uncomment to plot graph
#plot_model(self.model,
# to_file='data/models/textClassification/'+self.model_config.model_name+'_'+self.model_config.model_type+'.png')
self.model, best_roc_auc = train_model(self.model, self.model_config.list_classes, self.training_config.batch_size,
self.training_config.max_epoch, self.training_config.use_roc_auc, self.training_config.class_weights,
training_generator, validation_generator, val_y, use_ELMo=self.embeddings.use_ELMo,
use_BERT=self.embeddings.use_BERT, multiprocessing=self.training_config.multiprocessing, 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, vocab_init=None, callbacks=None):
# bert models
if self.model_config.model_type.find("bert") != -1:
self.model = getModel(self.model_config, self.training_config)
self.model.processor = BERT_classifier_processor(labels=self.model_config.list_classes, x_train=x_train, y_train=y_train)
self.model.train()
return
self.models = train_folds(x_train, y_train, self.model_config, self.training_config, self.embeddings, callbacks=callbacks)
if self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
if self.embeddings.use_BERT:
self.embeddings.clean_BERT_cache()
# classification
def predict(self, texts, output_format='json', use_main_thread_only=False):
if self.model_config.fold_number is 1:
if self.model is not None:
# bert model?
if self.model_config.model_type.find("bert") != -1:
# be sure the input processor is instanciated
self.model.processor = BERT_classifier_processor(labels=self.model_config.list_classes)
result = self.model.predict(texts)
else:
predict_generator = DataGenerator(texts, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False)
result = predict(self.model, predict_generator, use_ELMo=self.embeddings.use_ELMo, use_BERT=self.embeddings.use_BERT, use_main_thread_only=use_main_thread_only)
else:
raise (OSError('Could not find a model.'))
else:
# bert model?
if self.model_config.model_type.find("bert") != -1:
# we don't support n classifiers for BERT for prediction currently
# (it would be too large and too slow if loaded 10 times from file for each batch)
# (however it is done for eval, models are loaded 1 time for the complete dataset, not each time per batch, and we should do the same here)
# be sure the input processor is instanciated
self.model.processor = BERT_classifier_processor(labels=self.model_config.list_classes)
#result = self.models[0].predict(texts)
result = self.model.predict(texts)
else:
if self.models is not None:
predict_generator = DataGenerator(texts, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False)
result = predict_folds(self.models, predict_generator, use_ELMo=self.embeddings.use_ELMo, use_BERT=self.embeddings.use_BERT, use_main_thread_only=use_main_thread_only)
else:
raise (OSError('Could not find nfolds models.'))
if output_format is 'json':
res = {
"software": "DeLFT",
"date": datetime.datetime.now().isoformat(),
"model": self.model_config.model_name,
"classifications": []
}
i = 0
for text in texts:
classification = {
"text": text
}
the_res = result[i]
j = 0
for cl in self.model_config.list_classes:
classification[cl] = float(the_res[j])
j += 1
res["classifications"].append(classification)
i += 1
return res
else:
return result
def eval(self, x_test, y_test, use_main_thread_only=False):
if self.model_config.fold_number == 1:
if self.model is not None:
# bert model?
if self.model_config.model_type.find("bert") != -1:
#self.model.eval(x_test, y_test)
result = self.model.predict(x_test)
else:
test_generator = DataGenerator(x_test, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False)
result = predict(self.model, test_generator, use_ELMo=self.embeddings.use_ELMo, use_BERT=self.embeddings.use_BERT, use_main_thread_only=use_main_thread_only)
else:
raise (OSError('Could not find a model.'))
else:
if self.models is not None or (self.model_config.model_type.find("bert") != -1 and self.model is not None):
# bert model?
print(self.model_config.model_type)
if self.model_config.model_type.find("bert") != -1:
result_list = []
for i in range(self.model_config.fold_number):
result = self.model.predict(x_test, i)
result_list.append(result)
result = np.ones(result_list[0].shape)
for fold_result in result_list:
result *= fold_result
result **= (1. / len(result_list))
else:
test_generator = DataGenerator(x_test, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False)
result = predict_folds(self.models, test_generator, use_ELMo=self.embeddings.use_ELMo, use_BERT=self.embeddings.use_BERT, use_main_thread_only=use_main_thread_only)
else:
raise (OSError('Could not find nfolds models.'))
print("-----------------------------------------------")
print("\nEvaluation on", x_test.shape[0], "instances:")
total_accuracy = 0.0
total_f1 = 0.0
total_loss = 0.0
total_roc_auc = 0.0
'''
def normer(t):
if t < 0.5:
return 0
else:
return 1
vfunc = np.vectorize(normer)
result_binary = vfunc(result)
'''
result_intermediate = np.asarray([np.argmax(line) for line in result])
def vectorize(index, size):
result = np.zeros(size)
if index < size:
result[index] = 1
return result
result_binary = np.array([vectorize(xi, len(self.model_config.list_classes)) for xi in result_intermediate])
precision, recall, fscore, support = precision_recall_fscore_support(y_test, result_binary, average=None)
print('{:>14} {:>12} {:>12} {:>12} {:>12}'.format(" ", "precision", "recall", "f-score", "support"))
p = 0
for the_class in self.model_config.list_classes:
the_class = the_class[:14]
print('{:>14} {:>12} {:>12} {:>12} {:>12}'.format(the_class, "{:10.4f}"
.format(precision[p]), "{:10.4f}".format(recall[p]), "{:10.4f}".format(fscore[p]), support[p]))
p += 1
# macro-average (average of class scores)
# we distinguish 1-class and multiclass problems
if len(self.model_config.list_classes) is 1:
total_accuracy = accuracy_score(y_test, result_binary)
total_f1 = f1_score(y_test, result_binary)
total_loss = log_loss(y_test, result, labels=[0,1])
if len(np.unique(y_test)) == 1:
# roc_auc_score sklearn implementation is not working in this case, it needs more balanced batches
# a simple fix is to return the r2_score instead in this case (which is a regression score and not a loss)
total_roc_auc = r2_score(y_test, result)
if total_roc_auc < 0:
total_roc_auc = 0
else:
total_roc_auc = roc_auc_score(y_test, result)
else:
for j in range(0, len(self.model_config.list_classes)):
accuracy = accuracy_score(y_test[:, j], result_binary[:, j])
total_accuracy += accuracy
f1 = f1_score(y_test[:, j], result_binary[:, j], average='micro')
total_f1 += f1
loss = log_loss(y_test[:, j], result[:, j], labels=[0,1])
total_loss += loss
if len(np.unique(y_test[:, j])) == 1:
# roc_auc_score sklearn implementation is not working in this case, it needs more balanced batches
# a simple fix is to return the r2_score instead in this case (which is a regression score and not a loss)
roc_auc = r2_score(y_test[:, j], result[:, j])
if roc_auc < 0:
roc_auc = 0
else:
roc_auc = roc_auc_score(y_test[:, j], result[:, j])
total_roc_auc += roc_auc
'''
print("\nClass:", self.model_config.list_classes[j])
print("\taccuracy at 0.5 =", accuracy)
print("\tf-1 at 0.5 =", f1)
print("\tlog-loss =", loss)
print("\troc auc =", roc_auc)
'''
total_accuracy /= len(self.model_config.list_classes)
total_f1 /= len(self.model_config.list_classes)
total_loss /= len(self.model_config.list_classes)
total_roc_auc /= len(self.model_config.list_classes)
'''
if len(self.model_config.list_classes) is not 1:
print("\nMacro-average:")
print("\taverage accuracy at 0.5 =", "{:10.4f}".format(total_accuracy))
print("\taverage f-1 at 0.5 =", "{:10.4f}".format(total_f1))
print("\taverage log-loss =","{:10.4f}".format( total_loss))
print("\taverage roc auc =", "{:10.4f}".format(total_roc_auc))
'''
# micro-average (average of scores for each instance)
# make sense only if we have more than 1 class, otherwise same as
# macro-avergae
if len(self.model_config.list_classes) is not 1:
total_accuracy = 0.0
total_f1 = 0.0
total_loss = 0.0
total_roc_auc = 0.0
for i in range(0, result.shape[0]):
accuracy = accuracy_score(y_test[i,:], result_binary[i,:])
total_accuracy += accuracy
f1 = f1_score(y_test[i,:], result_binary[i,:], average='micro')
total_f1 += f1
loss = log_loss(y_test[i,:], result[i,:])
total_loss += loss
roc_auc = roc_auc_score(y_test[i,:], result[i,:])
total_roc_auc += roc_auc
total_accuracy /= result.shape[0]
total_f1 /= result.shape[0]
total_loss /= result.shape[0]
total_roc_auc /= result.shape[0]
'''
print("\nMicro-average:")
print("\taverage accuracy at 0.5 =", "{:10.4f}".format(total_accuracy))
print("\taverage f-1 at 0.5 =", "{:10.4f}".format(total_f1))
print("\taverage log-loss =", "{:10.4f}".format(total_loss))
print("\taverage roc auc =", "{:10.4f}".format(total_roc_auc))
'''
def save(self, dir_path='data/models/textClassification/'):
# 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')
# bert model are always saved via training process steps as checkpoint
if self.model_config.model_type.find("bert") != -1:
print('model saved')
return
if self.model_config.fold_number is 1:
if self.model is not None:
self.model.save(os.path.join(directory, self.model_config.model_type+"."+self.weight_file))
print('model saved')
else:
print('Error: model has not been built')
else:
if self.models is None:
print('Error: nfolds models have not been built')
else:
for i in range(0, self.model_config.fold_number):
self.models[i].save(os.path.join(directory, self.model_config.model_type+".model{0}_weights.hdf5".format(i)))
print('nfolds model saved')
def load(self, dir_path='data/models/textClassification/'):
self.model_config = ModelConfig.load(os.path.join(dir_path, self.model_config.model_name, self.config_file))
if self.model_config.model_type.find("bert") != -1:
self.model = getModel(self.model_config, self.training_config)
self.model.load()
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 = getModel(self.model_config, self.training_config)
if self.model_config.fold_number is 1:
self.model.load_weights(os.path.join(dir_path, self.model_config.model_name, self.model_config.model_type+"."+self.weight_file))
else:
self.models = []
for i in range(0, self.model_config.fold_number):
local_model = getModel(self.model_config, self.training_config)
local_model.load_weights(os.path.join(dir_path, self.model_config.model_name, self.model_config.model_type+".model{0}_weights.hdf5".format(i)))
self.models.append(local_model)
def __init__(self,
model_name=None,
architecture="gru",
embeddings_name=None,
list_classes=[],
char_emb_size=25,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=False,
batch_size=256,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
patience=5,
log_dir=None,
maxlen=300,
fold_number=1,
use_roc_auc=True,
early_stop=True,
class_weights=None,
multiprocessing=True,
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.log_dir = log_dir
self.embeddings_name = embeddings_name
self.embeddings = None
# if transformer_name is None, no bert layer is present in the model
self.transformer_name = None
self.registry = load_resource_registry("delft/resources-registry.json")
word_emb_size = 0
if transformer_name is not None:
self.transformer_name = transformer_name
self.embeddings_name = None
self.embeddings = None
elif self.embeddings_name is not None:
self.embeddings = Embeddings(self.embeddings_name, resource_registry=self.registry)
word_emb_size = self.embeddings.embed_size
self.model_config = ModelConfig(model_name=model_name,
architecture=architecture,
embeddings_name=embeddings_name,
list_classes=list_classes,
char_emb_size=char_emb_size,
word_emb_size=word_emb_size,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
maxlen=maxlen,
fold_number=fold_number,
batch_size=batch_size,
transformer_name=self.transformer_name)
self.training_config = TrainingConfig(batch_size=batch_size,
optimizer=optimizer,
learning_rate=learning_rate,
lr_decay=lr_decay,
clip_gradients=clip_gradients,
max_epoch=max_epoch,
patience=patience,
use_roc_auc=use_roc_auc,
early_stop=early_stop,
class_weights=class_weights,
multiprocessing=multiprocessing)
class Classifier(object):
config_file = 'config.json'
weight_file = 'model_weights.hdf5'
def __init__(self,
model_name=None,
architecture="gru",
embeddings_name=None,
list_classes=[],
char_emb_size=25,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=False,
batch_size=256,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
patience=5,
log_dir=None,
maxlen=300,
fold_number=1,
use_roc_auc=True,
early_stop=True,
class_weights=None,
multiprocessing=True,
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.log_dir = log_dir
self.embeddings_name = embeddings_name
self.embeddings = None
# if transformer_name is None, no bert layer is present in the model
self.transformer_name = None
self.registry = load_resource_registry("delft/resources-registry.json")
word_emb_size = 0
if transformer_name is not None:
self.transformer_name = transformer_name
self.embeddings_name = None
self.embeddings = None
elif self.embeddings_name is not None:
self.embeddings = Embeddings(self.embeddings_name, resource_registry=self.registry)
word_emb_size = self.embeddings.embed_size
self.model_config = ModelConfig(model_name=model_name,
architecture=architecture,
embeddings_name=embeddings_name,
list_classes=list_classes,
char_emb_size=char_emb_size,
word_emb_size=word_emb_size,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
maxlen=maxlen,
fold_number=fold_number,
batch_size=batch_size,
transformer_name=self.transformer_name)
self.training_config = TrainingConfig(batch_size=batch_size,
optimizer=optimizer,
learning_rate=learning_rate,
lr_decay=lr_decay,
clip_gradients=clip_gradients,
max_epoch=max_epoch,
patience=patience,
use_roc_auc=use_roc_auc,
early_stop=early_stop,
class_weights=class_weights,
multiprocessing=multiprocessing)
def train(self, x_train, y_train, vocab_init=None, callbacks=None):
self.model = getModel(self.model_config, self.training_config)
print_parameters(self.model_config, self.training_config)
self.model.print_summary()
bert_data = False
if self.transformer_name is not None:
bert_data = True
if self.training_config.early_stop:
# create validation set
xtr, val_x, y, val_y = train_test_split(x_train, y_train, test_size=0.1)
training_generator = DataGenerator(xtr, y, batch_size=self.training_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=True, bert_data=bert_data, transformer_tokenizer=self.model.transformer_tokenizer)
validation_generator = DataGenerator(val_x, None, batch_size=self.training_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False, bert_data=bert_data, transformer_tokenizer=self.model.transformer_tokenizer)
else:
val_y = y_train
training_generator = DataGenerator(x_train, y_train, batch_size=self.training_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=True, bert_data=bert_data, transformer_tokenizer=self.model.transformer_tokenizer)
validation_generator = None
# uncomment to plot graph
#plot_model(self.model,
# to_file='data/models/textClassification/'+self.model_config.model_name+'_'+self.model_config.architecture+'.png')
self.model.train_model(
self.model_config.list_classes,
self.training_config.batch_size,
self.training_config.max_epoch,
self.training_config.use_roc_auc,
self.training_config.class_weights,
training_generator,
validation_generator,
val_y,
patience=self.training_config.patience,
multiprocessing=self.training_config.multiprocessing,
callbacks=callbacks)
def train_nfold(self, x_train, y_train, vocab_init=None, callbacks=None):
self.models = train_folds(x_train, y_train, self.model_config, self.training_config, self.embeddings, callbacks=callbacks)
def predict(self, texts, output_format='json', use_main_thread_only=False, batch_size=None):
bert_data = False
if self.transformer_name != None:
bert_data = True
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_config.fold_number == 1:
if self.model != None:
predict_generator = DataGenerator(texts, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False, bert_data=bert_data, transformer_tokenizer=self.model.transformer_tokenizer)
result = self.model.predict(predict_generator, use_main_thread_only=use_main_thread_only)
else:
raise (OSError('Could not find a model.'))
else:
if self.models != None:
# just a warning: n classifiers using BERT layer for prediction might be heavy in term of model sizes
predict_generator = DataGenerator(texts, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False, bert_data=bert_data, transformer_tokenizer=self.model.transformer_tokenizer)
result = predict_folds(self.models,
predict_generator,
self.model_config,
self.training_config,
use_main_thread_only=use_main_thread_only)
else:
raise (OSError('Could not find nfolds models.'))
if output_format == 'json':
res = {
"software": "DeLFT",
"date": datetime.datetime.now().isoformat(),
"model": self.model_config.model_name,
"classifications": []
}
i = 0
for text in texts:
classification = {
"text": text
}
the_res = result[i]
j = 0
for cl in self.model_config.list_classes:
classification[cl] = float(the_res[j])
j += 1
res["classifications"].append(classification)
i += 1
return res
else:
return result
def eval(self, x_test, y_test, use_main_thread_only=False):
print_parameters(self.model_config, self.training_config)
bert_data = False
if self.transformer_name is not None:
bert_data = True
if self.model_config.fold_number == 1:
if self.model != None:
self.model.print_summary()
test_generator = DataGenerator(x_test, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False, bert_data=bert_data, transformer_tokenizer=self.model.transformer_tokenizer)
result = self.model.predict(test_generator, use_main_thread_only=use_main_thread_only)
else:
raise (OSError('Could not find a model.'))
else:
if self.models is None:
raise (OSError('Could not find nfolds models.'))
self.models[0].print_summary()
# just a warning: n classifiers using BERT layer for prediction might be heavy in term of model sizes
test_generator = DataGenerator(x_test, None, batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen, list_classes=self.model_config.list_classes,
embeddings=self.embeddings, shuffle=False, bert_data=bert_data, transformer_tokenizer=self.models[0].transformer_tokenizer)
result = predict_folds(self.models, test_generator, self.model_config, self.training_config, use_main_thread_only=use_main_thread_only)
print("-----------------------------------------------")
print("\nEvaluation on", x_test.shape[0], "instances:")
total_accuracy = 0.0
total_f1 = 0.0
total_loss = 0.0
total_roc_auc = 0.0
'''
def normer(t):
if t < 0.5:
return 0
else:
return 1
vfunc = np.vectorize(normer)
result_binary = vfunc(result)
'''
result_intermediate = np.asarray([np.argmax(line) for line in result])
def vectorize(index, size):
result = np.zeros(size)
if index < size:
result[index] = 1
return result
result_binary = np.array([vectorize(xi, len(self.model_config.list_classes)) for xi in result_intermediate])
precision, recall, fscore, support = precision_recall_fscore_support(y_test, result_binary, average=None)
print('{:>14} {:>12} {:>12} {:>12} {:>12}'.format(" ", "precision", "recall", "f-score", "support"))
p = 0
for the_class in self.model_config.list_classes:
the_class = the_class[:14]
print('{:>14} {:>12} {:>12} {:>12} {:>12}'.format(the_class, "{:10.4f}"
.format(precision[p]), "{:10.4f}".format(recall[p]), "{:10.4f}".format(fscore[p]), support[p]))
p += 1
# macro-average (average of class scores)
# we distinguish 1-class and multiclass problems
if len(self.model_config.list_classes) == 1:
total_accuracy = accuracy_score(y_test, result_binary)
total_f1 = f1_score(y_test, result_binary)
# sklearn will complain if log(0)
total_loss = log_loss(y_test, result, labels=[0,1])
if len(np.unique(y_test)) == 1:
# roc_auc_score sklearn implementation is not working in this case, it needs more balanced batches
# a simple fix is to return the r2_score instead in this case (which is a regression score and not a loss)
total_roc_auc = r2_score(y_test, result)
if total_roc_auc < 0:
total_roc_auc = 0
else:
total_roc_auc = roc_auc_score(y_test, result)
else:
for j in range(0, len(self.model_config.list_classes)):
accuracy = accuracy_score(y_test[:, j], result_binary[:, j])
total_accuracy += accuracy
f1 = f1_score(y_test[:, j], result_binary[:, j], average='micro')
total_f1 += f1
loss = log_loss(y_test[:, j], result[:, j], labels=[0,1])
total_loss += loss
if len(np.unique(y_test[:, j])) == 1:
# roc_auc_score sklearn implementation is not working in this case, it needs more balanced batches
# a simple fix is to return the r2_score instead in this case (which is a regression score and not a loss)
roc_auc = r2_score(y_test[:, j], result[:, j])
if roc_auc < 0:
roc_auc = 0
else:
roc_auc = roc_auc_score(y_test[:, j], result[:, j], labels=[0,1])
total_roc_auc += roc_auc
'''
print("\nClass:", self.model_config.list_classes[j])
print("\taccuracy at 0.5 =", accuracy)
print("\tf-1 at 0.5 =", f1)
print("\tlog-loss =", loss)
print("\troc auc =", roc_auc)
'''
total_accuracy /= len(self.model_config.list_classes)
total_f1 /= len(self.model_config.list_classes)
total_loss /= len(self.model_config.list_classes)
total_roc_auc /= len(self.model_config.list_classes)
'''
if len(self.model_config.list_classes) != 1:
print("\nMacro-average:")
print("\taverage accuracy at 0.5 =", "{:10.4f}".format(total_accuracy))
print("\taverage f-1 at 0.5 =", "{:10.4f}".format(total_f1))
print("\taverage log-loss =","{:10.4f}".format( total_loss))
print("\taverage roc auc =", "{:10.4f}".format(total_roc_auc))
'''
# micro-average (average of scores for each instance)
# make sense only if we have more than 1 class, otherwise same as
# macro-avergae
if len(self.model_config.list_classes) != 1:
total_accuracy = 0.0
total_f1 = 0.0
total_loss = 0.0
total_roc_auc = 0.0
for i in range(0, result.shape[0]):
accuracy = accuracy_score(y_test[i,:], result_binary[i,:])
total_accuracy += accuracy
f1 = f1_score(y_test[i,:], result_binary[i,:], average='micro')
total_f1 += f1
loss = log_loss(y_test[i,:], result[i,:], labels=[0.0, 1.0])
total_loss += loss
if len(np.unique(y_test[i,:])) == 1:
# roc_auc_score sklearn implementation is not working in this case, it needs more balanced batches
# a simple fix is to return the r2_score instead in this case (which is a regression score and not a loss)
roc_auc = r2_score(y_test[i,:], result[i,:])
if roc_auc < 0:
roc_auc = 0
else:
roc_auc = roc_auc_score(y_test[i,:], result[i,:], labels=[0.0, 1.0])
total_roc_auc += roc_auc
total_accuracy /= result.shape[0]
total_f1 /= result.shape[0]
total_loss /= result.shape[0]
total_roc_auc /= result.shape[0]
'''
print("\nMicro-average:")
print("\taverage accuracy at 0.5 =", "{:10.4f}".format(total_accuracy))
print("\taverage f-1 at 0.5 =", "{:10.4f}".format(total_f1))
print("\taverage log-loss =", "{:10.4f}".format(total_loss))
print("\taverage roc auc =", "{:10.4f}".format(total_roc_auc))
'''
def save(self, dir_path='data/models/textClassification/'):
# 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')
if self.model_config.fold_number == 1:
if self.model != None:
self.model.save(os.path.join(directory, self.weight_file))
print('model saved')
else:
print('Error: model has not been built')
else:
if self.models == None:
print('Error: nfolds models have not been built')
else:
# fold models having a transformer layers are already saved
if self.model_config.transformer_name is None:
for i in range(0, self.model_config.fold_number):
self.models[i].save(os.path.join(directory, "model{0}_weights.hdf5".format(i)))
print('nfolds model saved')
# save pretrained transformer config and tokenizer if used in the model and if single fold (otherwise it is saved in the nfold process)
if self.transformer_name is not None and self.model_config.fold_number == 1:
if self.model.transformer_config is not None:
self.model.transformer_config.to_json_file(os.path.join(directory, TRANSFORMER_CONFIG_FILE_NAME))
if self.model.transformer_tokenizer is not None:
self.model.transformer_tokenizer.save_pretrained(os.path.join(directory, DEFAULT_TRANSFORMER_TOKENIZER_DIR))
def load(self, dir_path='data/models/textClassification/'):
model_path = os.path.join(dir_path, self.model_config.model_name)
self.model_config = ModelConfig.load(os.path.join(model_path, self.config_file))
if self.model_config.transformer_name is None:
# load embeddings
# Do not use cache in 'production' mode
self.embeddings = Embeddings(self.model_config.embeddings_name, resource_registry=self.registry, use_cache=False)
self.model_config.word_embedding_size = self.embeddings.embed_size
else:
self.transformer_name = self.model_config.transformer_name
self.embeddings = None
self.model = getModel(self.model_config,
self.training_config,
load_pretrained_weights=False,
local_path=model_path)
print_parameters(self.model_config, self.training_config)
self.model.print_summary()
if self.model_config.fold_number == 1:
print("load weights from", os.path.join(model_path, self.weight_file))
self.model.load(os.path.join(model_path, self.weight_file))
else:
self.models = []
if self.model_config.transformer_name is None:
for i in range(0, self.model_config.fold_number):
local_model = getModel(self.model_config,
self.training_config,
load_pretrained_weights=False,
local_path=model_path)
local_model.load(os.path.join(model_path, "model{0}_weights.hdf5".format(i)))
self.models.append(local_model)
else:
# only init first fold one, the other will be init at prediction time, all weights will be loaded at prediction time
local_model = getModel(self.model_config,
self.training_config,
load_pretrained_weights=False,
local_path=model_path)
self.models.append(local_model)
class Classifier(object):
config_file = 'config.json'
weight_file = 'model_weights.hdf5'
def __init__(self,
model_name="",
model_type="gru",
embeddings_name=None,
list_classes=[],
char_emb_size=25,
dropout=0.5,
recurrent_dropout=0.25,
use_char_feature=False,
batch_size=256,
optimizer='adam',
learning_rate=0.001,
lr_decay=0.9,
clip_gradients=5.0,
max_epoch=50,
patience=5,
log_dir=None,
maxlen=300,
fold_number=1,
use_roc_auc=True,
use_ELMo=False,
use_BERT=False,
embeddings=(),
class_weights=None):
self.model = None
self.models = 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
self.model_config = ModelConfig(model_name=model_name,
model_type=model_type,
embeddings_name=embeddings_name,
list_classes=list_classes,
char_emb_size=char_emb_size,
word_emb_size=word_emb_size,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
use_char_feature=use_char_feature,
maxlen=maxlen,
fold_number=fold_number,
batch_size=batch_size,
use_ELMo=use_ELMo,
use_BERT=use_BERT)
self.training_config = TrainingConfig(batch_size,
optimizer,
learning_rate,
lr_decay,
clip_gradients,
max_epoch,
patience,
use_roc_auc,
class_weights=class_weights)
def train(self, x_train, y_train, vocab_init=None):
# create validation set in case we don't use k-folds
xtr, val_x, y, val_y = train_test_split(x_train,
y_train,
test_size=0.1)
training_generator = DataGenerator(
xtr,
y,
batch_size=self.training_config.batch_size,
maxlen=self.model_config.maxlen,
list_classes=self.model_config.list_classes,
embeddings=self.embeddings,
shuffle=True)
validation_generator = DataGenerator(
val_x,
None,
batch_size=self.training_config.batch_size,
maxlen=self.model_config.maxlen,
list_classes=self.model_config.list_classes,
embeddings=self.embeddings,
shuffle=False)
self.model = getModel(self.model_config, self.training_config)
# uncomment to plot graph
#plot_model(self.model,
# to_file='data/models/textClassification/'+self.model_config.model_name+'_'+self.model_config.model_type+'.png')
self.model, best_roc_auc = train_model(
self.model,
self.model_config.list_classes,
self.training_config.batch_size,
self.training_config.max_epoch,
self.training_config.use_roc_auc,
self.training_config.class_weights,
training_generator,
validation_generator,
val_y,
use_ELMo=self.embeddings.use_ELMo,
use_BERT=self.embeddings.use_BERT)
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, vocab_init=None):
self.models = train_folds(x_train, y_train, self.model_config,
self.training_config, self.embeddings)
if self.embeddings.use_ELMo:
self.embeddings.clean_ELMo_cache()
if self.embeddings.use_BERT:
self.embeddings.clean_BERT_cache()
# classification
def predict(self, texts, output_format='json'):
if self.model_config.fold_number is 1:
if self.model is not None:
predict_generator = DataGenerator(
texts,
None,
batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen,
list_classes=self.model_config.list_classes,
embeddings=self.embeddings,
shuffle=False)
result = predict(self.model,
predict_generator,
use_ELMo=self.embeddings.use_ELMo,
use_BERT=self.embeddings.use_BERT)
else:
raise (OSError('Could not find a model.'))
else:
if self.models is not None:
predict_generator = DataGenerator(
texts,
None,
batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen,
list_classes=self.model_config.list_classes,
embeddings=self.embeddings,
shuffle=False)
result = predict_folds(self.models,
predict_generator,
use_ELMo=self.embeddings.use_ELMo,
use_BERT=self.embeddings.use_BERT)
else:
raise (OSError('Could not find nfolds models.'))
if output_format is 'json':
res = {
"software": "DeLFT",
"date": datetime.datetime.now().isoformat(),
"model": self.model_config.model_name,
"classifications": []
}
i = 0
for text in texts:
classification = {"text": text}
the_res = result[i]
j = 0
for cl in self.model_config.list_classes:
classification[cl] = float(the_res[j])
j += 1
res["classifications"].append(classification)
i += 1
return res
else:
return result
def eval(self, x_test, y_test):
if self.model_config.fold_number is 1:
if self.model is not None:
test_generator = DataGenerator(
x_test,
None,
batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen,
list_classes=self.model_config.list_classes,
embeddings=self.embeddings,
shuffle=False)
result = predict(self.model,
test_generator,
use_ELMo=self.embeddings.use_ELMo,
use_BERT=self.embeddings.use_BERT)
else:
raise (OSError('Could not find a model.'))
else:
if self.models is not None:
test_generator = DataGenerator(
x_test,
None,
batch_size=self.model_config.batch_size,
maxlen=self.model_config.maxlen,
list_classes=self.model_config.list_classes,
embeddings=self.embeddings,
shuffle=False)
result = predict_folds(self.models,
test_generator,
use_ELMo=self.embeddings.use_ELMo,
use_BERT=self.embeddings.use_BERT)
else:
raise (OSError('Could not find nfolds models.'))
print("-----------------------------------------------")
print("\nEvaluation on", x_test.shape[0], "instances:")
total_accuracy = 0.0
total_f1 = 0.0
total_loss = 0.0
total_roc_auc = 0.0
def normer(t):
if t < 0.5:
return 0
else:
return 1
vfunc = np.vectorize(normer)
result_binary = vfunc(result)
# macro-average (average of class scores)
# we distinguish 1-class and multiclass problems
if len(self.model_config.list_classes) is 1:
total_accuracy = accuracy_score(y_test, result_binary)
total_f1 = f1_score(y_test, result_binary)
total_loss = log_loss(y_test, result)
total_roc_auc = roc_auc_score(y_test, result)
else:
for j in range(0, len(self.model_config.list_classes)):
accuracy = accuracy_score(y_test[:, j], result_binary[:, j])
total_accuracy += accuracy
f1 = f1_score(y_test[:, j],
result_binary[:, j],
average='micro')
total_f1 += f1
loss = log_loss(y_test[:, j], result[:, j])
total_loss += loss
roc_auc = roc_auc_score(y_test[:, j], result[:, j])
total_roc_auc += roc_auc
print("\nClass:", self.model_config.list_classes[j])
print("\taccuracy at 0.5 =", accuracy)
print("\tf-1 at 0.5 =", f1)
print("\tlog-loss =", loss)
print("\troc auc =", roc_auc)
total_accuracy /= len(self.model_config.list_classes)
total_f1 /= len(self.model_config.list_classes)
total_loss /= len(self.model_config.list_classes)
total_roc_auc /= len(self.model_config.list_classes)
if len(self.model_config.list_classes) is not 1:
print("\nMacro-average:")
print("\taverage accuracy at 0.5 =", "{:10.4f}".format(total_accuracy))
print("\taverage f-1 at 0.5 =", "{:10.4f}".format(total_f1))
print("\taverage log-loss =", "{:10.4f}".format(total_loss))
print("\taverage roc auc =", "{:10.4f}".format(total_roc_auc))
# micro-average (average of scores for each instance)
# make sense only if we have more than 1 class, otherwise same as
# macro-avergae
if len(self.model_config.list_classes) is not 1:
total_accuracy = 0.0
total_f1 = 0.0
total_loss = 0.0
total_roc_auc = 0.0
for i in range(0, result.shape[0]):
#for j in range(0, len(self.model_config.list_classes)):
accuracy = accuracy_score(y_test[i, :], result_binary[i, :])
total_accuracy += accuracy
f1 = f1_score(y_test[i, :],
result_binary[i, :],
average='micro')
total_f1 += f1
loss = log_loss(y_test[i, :], result[i, :])
total_loss += loss
roc_auc = roc_auc_score(y_test[i, :], result[i, :])
total_roc_auc += roc_auc
total_accuracy /= result.shape[0]
total_f1 /= result.shape[0]
total_loss /= result.shape[0]
total_roc_auc /= result.shape[0]
print("\nMicro-average:")
print("\taverage accuracy at 0.5 =",
"{:10.4f}".format(total_accuracy))
print("\taverage f-1 at 0.5 =", "{:10.4f}".format(total_f1))
print("\taverage log-loss =", "{:10.4f}".format(total_loss))
print("\taverage roc auc =", "{:10.4f}".format(total_roc_auc))
def save(self, dir_path='data/models/textClassification/'):
# 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')
if self.model_config.fold_number is 1:
if self.model is not None:
self.model.save(
os.path.join(
directory,
self.model_config.model_type + "." + self.weight_file))
print('model saved')
else:
print('Error: model has not been built')
else:
if self.models is None:
print('Error: nfolds models have not been built')
else:
for i in range(0, self.model_config.fold_number):
self.models[i].save(
os.path.join(
directory, self.model_config.model_type +
".model{0}_weights.hdf5".format(i)))
print('nfolds model saved')
def load(self, dir_path='data/models/textClassification/'):
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,
use_BERT=self.model_config.use_BERT)
self.model_config.word_embedding_size = self.embeddings.embed_size
self.model = getModel(self.model_config, self.training_config)
if self.model_config.fold_number is 1:
self.model.load_weights(
os.path.join(
dir_path, self.model_config.model_name,
self.model_config.model_type + "." + self.weight_file))
else:
self.models = []
for i in range(0, self.model_config.fold_number):
local_model = getModel(self.model_config, self.training_config)
local_model.load_weights(
os.path.join(
dir_path, self.model_config.model_name,
self.model_config.model_type +
".model{0}_weights.hdf5".format(i)))
self.models.append(local_model)