import pandas as pd
import urllib.request
import urllib.parse
import tensorflow as tf
# 连接数据库
from Data.load_dbdata import upload_data
from global_config import Logger
from run_similarity import BertSim
# 模块导入 https://blog.csdn.net/xiongchengluo1129/article/details/80453599
loginfo = Logger("recommend_articles.log", "info")
file = "./Data/NER_Data/q_t_a_testing_predict.txt"
bs = BertSim()
bs.set_mode(tf.estimator.ModeKeys.PREDICT)
def dataset_test():
'''
用训练问答对中的实体+属性，去知识库中进行问答测试准确率上限
:return:
'''
with open(file) as f:
total = 0
recall = 0
correct = 0
for line in f:
question, entity, attribute, answer, ner = line.split("\t")
ner = ner.replace("#", "").replace("[UNK]", "%")
def predict_online():
"""
do online prediction. each time make prediction for one instance.
you can change to a batch if you want.
:param line: a list. element is: [dummy_label,text_a,text_b]
:return:
"""
#driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "Nic180319"))
def convert(line):
feature = convert_single_example(0, line, label_list,
FLAGS.max_seq_length, tokenizer, 'p')
input_ids = np.reshape([feature.input_ids],
(batch_size, FLAGS.max_seq_length))
input_mask = np.reshape([feature.input_mask],
(batch_size, FLAGS.max_seq_length))
segment_ids = np.reshape([feature.segment_ids],
(batch_size, FLAGS.max_seq_length))
label_ids = np.reshape([feature.label_ids],
(batch_size, FLAGS.max_seq_length))
return input_ids, input_mask, segment_ids, label_ids
global graph
with graph.as_default():
print(id2label)
while True:
print('input the test sentence:')
sentence_l = input()
sentence = str(sentence_l)
start = datetime.now()
if len(sentence) < 2:
print(sentence)
continue
sentence = tokenizer.tokenize(sentence)
# print('your input is:{}'.format(sentence))
input_ids, input_mask, segment_ids, label_ids = convert(sentence)
feed_dict = {
input_ids_p: input_ids,
input_mask_p: input_mask,
segment_ids_p: segment_ids,
label_ids_p: label_ids
}
# run session get current feed_dict result
pred_ids_result = sess.run([pred_ids], feed_dict)
pred_label_result = convert_id_to_label(pred_ids_result, id2label)
print(pred_label_result)
#todo: 组合策略
result = strage_combined_link_org_loc(sentence,
pred_label_result[0], True)
print('识别的实体有：{}'.format(' '.join(result)))
#print('Time used: {} sec'.format((datetime.now() - start).seconds))
# yueuu
#driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "Nic180319"))
hd_graph = Klg()
all_rel = hd_graph.old_allrel(driver, name=result[0])
print('知识图谱中跟实体关联的所有关系为：', all_rel)
time.sleep(5)
sim = BertSim()
sim.set_mode(tf.estimator.ModeKeys.PREDICT)
sim_score = []
for j in range(len(all_rel)):
sim_score.append(sim.predict(sentence_l, all_rel[j])[0][1])
for j in range(len(sim_score)):
print(all_rel[j], f'similarity：{sim_score[j]}')
max_idx = sim_score.index(max(sim_score))
print('相似度最高的关系为：', all_rel[max_idx])
answer = hd_graph.find(driver, result[0], all_rel[max_idx])
if answer:
print("answer:", answer)
else:
print("不知道")