with open(word_voc_path, 'rb') as infile:
word_voc = pickle.load(infile)
bert_encoder = BertEncoder(model_root=bert_model_path,
bert_config_file=bert_config_file,
init_checkpoint=bert_checkpoint_path,
vocab_file=bert_voc_path,
max_sequence_len=max_sequence_len,
embedding_batch=3,
embedding_matrix_path=None,
sen2id_path=sen2id_path,
vec_dim=768)
instances_judge_dev = make_instances(judge_samples,
char_voc,
word_voc,
sentiment_words_path,
ner_dict_path=ner_dict_path,
pos_dict_path=pos_dict_path,
use_extra_feature=use_extra_feature,
question2targets=question2targets,
is_training=False,
need_augment=False)
# instances_judge_template = make_instances(
# judge_template,
# char_voc,
# word_voc,
# sentiment_words_path,
# ner_dict_path=ner_dict_path,
# pos_dict_path=pos_dict_path,
# use_extra_feature=use_extra_feature,
# question2targets=question2targets,
# is_training=False,
# need_augment=False)
def test():
with open('config_judge.json', encoding='utf-8') as infile:
config = json.load(infile)
int2bool = {1: True, 0: False}
sentiment_words_path = config["train_config"]["SENTIMENT_WORDS_PATH"]
batch_size = config["train_config"]["BATCH_SIZE"]
is_loop = int2bool[config["train_config"]["Is_LOOP"]]
is_sort = int2bool[config["train_config"]["IS_SORT"]]
dropout_rate = config["train_config"]["DROPOUT_RATE"]
nb_classes = config["train_config"]["NB_CLASSES"]
attention_dim = config["train_config"]["ATTENTION_DIM"]
nb_hops = config["train_config"]["NB_HOPS"]
drop_template_path = config["train_config"]["DROP_JUDGE_TEMPLATE_PATH"]
use_bert = int2bool[config["train_config"]["USE_BERT"]]
optimizer = config["train_config"]["OPTIMIZER"]
learning_rate = config["train_config"]["LEARNING_RATE"]
grad_clipper = config["train_config"]["GRAD_CLIPPER"]
drop_judge_dev_path = config["train_config"]["DROP_JUDGE_DEV_PATH"]
best_path = config["train_config"]["BEST_PATH"]
question2targets_path = config["train_config"]["QUESTION2TARGETS_PATH"]
use_extra_feature = config["train_config"]["USE_EXTRA_FEATURE"]
ner_dict_size = config["train_config"]["NER_DICT_SIZE"]
pos_dict_size = config["train_config"]["POS_DICT_SIZE"]
extra_feature_dim = config["train_config"]["EXTRA_FEATURE_DIM"]
ner_dict_path = config["train_config"]["NER_DICT_PATH"]
pos_dict_path = config["train_config"]["POS_DICT_PATH"]
rnn_dim = config["train_config"]["RNN_DIM"]
lambda_l2 = config["train_config"]["LAMBDA_L2"]
ans_max_len = config["train_config"]["ANS_MAX_LEN"]
que_max_len = config["train_config"]["QUE_MAX_LEN"]
sentiment_polarity_multiple = config["train_config"]["POLARITY_MULTIPLE"]
use_w2v = True
if use_bert:
use_w2v = False
char_voc_path = config["w2v_config"]["CHAR_VOC_PATH"]
char_embedding_matrix_path = config["w2v_config"][
"CHAR_EMBEDDING_MATRIX_PATH"]
word_voc_path = config["w2v_config"]["WORD_VOC_PATH"]
word_embedding_matrix_path = config["w2v_config"][
"WORD_EMBEDDING_MATRIX_PATH"]
bert_model_path = config["bert_config"]["BERT_MODEL_PATH"]
bert_config_file = config["bert_config"]["CONFIG_FILE"]
bert_checkpoint_path = config["bert_config"]["INIT_CHECKPOINT"]
bert_voc_path = config["bert_config"]["VOC_FILE"]
sen2id_path = config["bert_config"]["SEN2ID_PATH"]
judge_samples, _, _ = read_file(drop_judge_dev_path)
judge_template, _, _ = read_file(drop_template_path)
max_sequence_len = max(
max([len(sample['question']) for sample in judge_samples]),
max([len(sample['answer']) for sample in judge_samples]))
with open(char_voc_path, 'rb') as infile:
char_voc = pickle.load(infile)
with open(word_voc_path, 'rb') as infile:
word_voc = pickle.load(infile)
bert_encoder = BertEncoder(model_root=bert_model_path,
bert_config_file=bert_config_file,
init_checkpoint=bert_checkpoint_path,
vocab_file=bert_voc_path,
max_sequence_len=max_sequence_len,
embedding_batch=3,
embedding_matrix_path=None,
sen2id_path=sen2id_path,
vec_dim=768)
instances_judge_dev = make_instances(judge_samples,
char_voc,
word_voc,
sentiment_words_path,
ner_dict_path=ner_dict_path,
pos_dict_path=pos_dict_path,
use_extra_feature=use_extra_feature,
question2targets=question2targets,
is_training=False,
need_augment=False)
instances_judge_dev_with_match_result(instances_judge_dev)
data_stream_judge_dev = DataStream(instances=instances_judge_dev,
is_shuffle=False,
is_loop=is_loop,
batch_size=batch_size,
ans_max_len=ans_max_len,
que_max_len=que_max_len,
use_bert=use_bert,
bert_encoder=bert_encoder,
is_sort=is_sort)
with tf.Graph().as_default():
with tf.variable_scope("Model",
reuse=False,
initializer=tf.glorot_uniform_initializer()):
answer_understander_dev = AnswerUnderstander(
use_bert=use_bert,
use_w2v=use_w2v,
rnn_unit='lstm',
dropout_rate=dropout_rate,
optimizer=optimizer,
learning_rate=learning_rate,
grad_clipper=grad_clipper,
global_step=None,
attention_dim=attention_dim,
nb_hops=nb_hops,
rnn_dim=rnn_dim,
lambda_l2=lambda_l2,
is_training=False,
sentiment_polarity_multiple=sentiment_polarity_multiple,
nb_classes=nb_classes,
use_extra_feature=use_extra_feature,
ner_dict_size=ner_dict_size,
pos_dict_size=pos_dict_size,
extra_feature_dim=extra_feature_dim,
ans_max_len=ans_max_len,
que_max_len=que_max_len,
char_w2v_embedding_matrix_path=char_embedding_matrix_path,
word_w2v_embedding_matrix_path=word_embedding_matrix_path)
saver = tf.train.Saver()
sess = tf.Session()
initializer = tf.global_variables_initializer()
sess.run(initializer)
saver.restore(sess, best_path)
judge_acc = evaluation(sess, answer_understander_dev,
data_stream_judge_dev,
'result_{}.txt'.format(loop_index))
print("the final judge accuracy:{}".format(judge_acc))
return judge_acc
# ins_template = make_instances(samples_template,
# w2v_encoder.char_voc,
# w2v_encoder.word_voc,
# sentiment_words_path,
# ner_dict_path=ner_dict_path,
# pos_dict_path=pos_dict_path,
# use_extra_feature=use_extra_feature,
# question2targets=question2targets,
# is_training=True,
# need_augment=True)
instances = make_instances(samples,
w2v_encoder.char_voc,
w2v_encoder.word_voc,
sentiment_words_path,
ner_dict_path=ner_dict_path,
pos_dict_path=pos_dict_path,
use_extra_feature=use_extra_feature,
question2targets=question2targets,
is_training=True,
need_augment=True)
instances_train, instances_dev = split_train_dev(instances, dev_size=0.2)
drop_instances_to_excel(instances_train, drop_train_path)
drop_instances_to_excel(instances_dev, drop_path)
drop_instances_to_excel(instances, 'temp.xlsx')
# drop_instances_to_excel(ins_template, drop_template_path)
instances_train, instances_valid = split_train_dev(instances_train,
dev_size=0.1)
# data_stream_train = DataStream(instances=instances_train,
# is_shuffle=is_shuffle,
# is_loop=is_loop,