def evaluate(self, test_data_dir):
from sklearn import metrics
with self.graph.as_default():
set_session(self.sess)
self.test_data_folder_path = test_data_dir
data_text_arr, data_tags_arr, data_intents = Reader.read(self.test_data_folder_path)
data_input_ids, data_input_mask, data_segment_ids, data_valid_positions, data_sequence_lengths = self.bert_vectorizer.transform(data_text_arr)
if self.use_crf:
predicted_tags, predicted_intents = self.model.predict_slots_intent(
[data_input_ids, data_input_mask, data_segment_ids, data_valid_positions, data_sequence_lengths],
self.tags_vectorizer, self.intents_label_encoder, remove_start_end=True, include_intent_prob=True)
else:
predicted_tags, predicted_intents = self.model.predict_slots_intent(
[data_input_ids, data_input_mask, data_segment_ids, data_valid_positions],
self.tags_vectorizer, self.intents_label_encoder, remove_start_end=True, include_intent_prob=True)
gold_tags = [x.split() for x in data_tags_arr]
#calculate metrics for intent and slots
slot_acc = metrics.accuracy_score(flatten(gold_tags), flatten(predicted_tags))
slot_f1 = metrics.f1_score(flatten(gold_tags), flatten(predicted_tags), average='weighted')
slot_precision = metrics.precision_score(flatten(gold_tags), flatten(predicted_tags), average='weighted')
slot_recall = metrics.recall_score(flatten(gold_tags), flatten(predicted_tags), average='weighted')
confidence = [ex[1] for ex in predicted_intents]
predicted_intents = [ex[0] for ex in predicted_intents]
intent_acc = metrics.accuracy_score(data_intents, predicted_intents)
intent_f1 = metrics.f1_score(data_intents, predicted_intents, average='weighted')
intent_precision = metrics.precision_score(data_intents, predicted_intents, average='weighted')
intent_recall = metrics.recall_score(data_intents, predicted_intents, average='weighted')
metrics = {
'slot_acc':slot_acc,
'slot_f1':slot_f1,
'slot_precision':slot_precision,
'slot_recall':slot_recall,
'intent_acc':intent_acc,
'intent_f1':intent_f1,
'intent_precision':intent_precision,
'intent_recall':intent_recall
}
predicted_tags = [ " ".join(ex)+"\n" for ex in predicted_tags]
# for report, confusion matrix & histogram
self.intent_true = data_intents
self.intent_pred = predicted_intents
self.slot_true = data_tags_arr
self.slot_pred = predicted_tags
self.confidence_score = [round(float(score),2) for score in confidence]
return [metrics, predicted_intents, data_intents, predicted_tags, data_tags_arr, confidence] #confidence score
print('Loading models ...')
if not os.path.exists(load_folder_path):
print('Folder `%s` not exist' % load_folder_path)
with open(os.path.join(load_folder_path, 'tags_vectorizer.pkl'),
'rb') as handle:
tags_vectorizer = pickle.load(handle)
slots_num = len(tags_vectorizer.label_encoder.classes_)
with open(os.path.join(load_folder_path, 'intents_label_encoder.pkl'),
'rb') as handle:
intents_label_encoder = pickle.load(handle)
intents_num = len(intents_label_encoder.classes_)
model = JointBertModel.load(load_folder_path, sess)
data_text_arr, data_tags_arr, data_intents = Reader.read(data_folder_path)
data_input_ids, data_input_mask, data_segment_ids, data_valid_positions, data_sequence_lengths = bert_vectorizer.transform(
data_text_arr)
def get_results(input_ids, input_mask, segment_ids, valid_positions,
sequence_lengths, tags_arr, intents, tags_vectorizer,
intents_label_encoder):
predicted_tags, predicted_intents = model.predict_slots_intent(
[input_ids, input_mask, segment_ids, valid_positions],
tags_vectorizer,
intents_label_encoder,
remove_start_end=True)
gold_tags = [x.split() for x in tags_arr]
#print(metrics.classification_report(flatten(gold_tags), flatten(predicted_tags), digits=3))
f1_score = metrics.f1_score(flatten(gold_tags),
args = parser.parse_args() train_data_folder_path = args.train val_data_folder_path = args.val save_folder_path = args.save epochs = args.epochs batch_size = args.batch tf.compat.v1.random.set_random_seed(7) sess = tf.compat.v1.Session() bert_model_hub_path = 'https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1' train_text_arr, train_tags_arr, train_intents = Reader.read(train_data_folder_path) val_text_arr, val_tags_arr, val_intents = Reader.read(val_data_folder_path) bert_vectorizer = BERTVectorizer(sess, bert_model_hub_path) train_input_ids, train_input_mask, train_segment_ids, train_valid_positions, train_sequence_lengths = bert_vectorizer.transform(train_text_arr) val_input_ids, val_input_mask, val_segment_ids, val_valid_positions, val_sequence_lengths = bert_vectorizer.transform(val_text_arr) tags_vectorizer = TagsVectorizer() tags_vectorizer.fit(train_tags_arr) train_tags = tags_vectorizer.transform(train_tags_arr, train_valid_positions) #from sklearn.preprocessing import OneHotEncoder #enc = OneHotEncoder(handle_unknown='ignore', sparse=False) #enc.fit(train_tags)
def train(self, train_data_dir, valid_data_dir):
self.train_data_folder_path = train_data_dir
self.val_data_folder_path = valid_data_dir
if self.type_ == 'bert':
bert_model_hub_path = 'https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1'
is_bert = True
else:
bert_model_hub_path = 'https://tfhub.dev/google/albert_base/1'
is_bert = False
#read data
train_text_arr, train_tags_arr, train_intents = Reader.read(self.train_data_folder_path)
val_text_arr, val_tags_arr, val_intents = Reader.read(self.val_data_folder_path)
#vectorise data
self.bert_vectorizer = BERTVectorizer(self.sess, is_bert, bert_model_hub_path)
train_input_ids, train_input_mask, train_segment_ids, train_valid_positions, train_sequence_lengths = self.bert_vectorizer.transform(train_text_arr)
val_input_ids, val_input_mask, val_segment_ids, val_valid_positions, val_sequence_lengths = self.bert_vectorizer.transform(val_text_arr)
#vectorise tags
if self.use_crf: # with crf
self.tags_vectorizer = TagsVectorizer()
self.tags_vectorizer.fit(train_tags_arr)
train_tags = self.tags_vectorizer.transform(train_tags_arr, train_valid_positions)
train_tags = tf.keras.utils.to_categorical(train_tags)
val_tags = self.tags_vectorizer.transform(val_tags_arr, val_valid_positions)
val_tags = tf.keras.utils.to_categorical(val_tags)
slots_num = len(self.tags_vectorizer.label_encoder.classes_)
else: # without crf
self.tags_vectorizer = TagsVectorizer()
self.tags_vectorizer.fit(train_tags_arr)
train_tags = self.tags_vectorizer.transform(train_tags_arr, train_valid_positions)
val_tags = self.tags_vectorizer.transform(val_tags_arr, val_valid_positions)
slots_num = len(self.tags_vectorizer.label_encoder.classes_)
#encode labels
self.intents_label_encoder = LabelEncoder()
train_intents = self.intents_label_encoder.fit_transform(train_intents).astype(np.int32)
val_intents = self.intents_label_encoder.transform(val_intents).astype(np.int32)
intents_num = len(self.intents_label_encoder.classes_)
#train
if self.use_crf: # with crf
self.model = JointBertCRFModel(slots_num, intents_num, bert_model_hub_path, self.sess, num_bert_fine_tune_layers=10, is_bert=is_bert, is_crf=True, learning_rate=self.learning_rate)
self.model.fit([train_input_ids, train_input_mask, train_segment_ids, train_valid_positions, train_sequence_lengths], [train_tags, train_intents],
validation_data=([val_input_ids, val_input_mask, val_segment_ids, val_valid_positions, val_sequence_lengths], [val_tags, val_intents]),
epochs=self.epochs, batch_size=self.batch_size)
else: # without crf
self.model = JointBertModel(slots_num, intents_num, bert_model_hub_path, self.sess, num_bert_fine_tune_layers=10, is_bert=is_bert, is_crf=False, learning_rate=self.learning_rate)
self.model.fit([train_input_ids, train_input_mask, train_segment_ids, train_valid_positions], [train_tags, train_intents],
validation_data=([val_input_ids, val_input_mask, val_segment_ids, val_valid_positions], [val_tags, val_intents]),
epochs=self.epochs, batch_size=self.batch_size)
#save
if not os.path.exists(self.save_folder_path):
os.makedirs(self.save_folder_path)
self.model.save(self.save_folder_path)
with open(os.path.join(self.save_folder_path, 'tags_vectorizer.pkl'), 'wb') as handle:
pickle.dump(self.tags_vectorizer, handle, protocol=pickle.HIGHEST_PROTOCOL)
with open(os.path.join(self.save_folder_path, 'intents_label_encoder.pkl'), 'wb') as handle:
pickle.dump(self.intents_label_encoder, handle, protocol=pickle.HIGHEST_PROTOCOL)
