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kbqa_test.py
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kbqa_test.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import io
import re
import time
import jieba
import numpy as np
import pandas as pd
import urllib.request
import urllib.parse
import tensorflow as tf
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]", "%")
# case1: entity and attribute Exact Match
sql_e1_a1 = "select * from nlpccQA where entity='"+entity+"' and attribute='"+attribute+"' limit 10"
result_e1_a1 = upload_data(sql_e1_a1)
# case2: entity Fuzzy Match and attribute Exact Match
sql_e0_a1 = "select * from nlpccQA where entity like '%" + entity + "%' and attribute='" + attribute + "' limit 10"
#result_e0_a1 = upload_data(sql_e0_a1, True)
# case3: entity Exact Match and attribute Fuzzy Match
sql_e1_a0 = "select * from nlpccQA where entity like '" + entity + "' and attribute='%" + attribute + "%' limit 10"
#result_e1_a0 = upload_data(sql_e1_a0)
if len(result_e1_a1) > 0:
recall += 1
for l in result_e1_a1:
if l[2] == answer:
correct += 1
else:
result_e0_a1 = upload_data(sql_e0_a1)
if len(result_e0_a1) > 0:
recall += 1
for l in result_e0_a1:
if l[2] == answer:
correct += 1
else:
result_e1_a0 = upload_data(sql_e1_a0)
if len(result_e1_a0) > 0:
recall += 1
for l in result_e1_a0:
if l[2] == answer:
correct += 1
else:
loginfo.logger.info(sql_e1_a0)
if total > 100:
break
total += 1
time.sleep(1)
loginfo.logger.info("total: {}, recall: {}, correct:{}, accuracy: {}%".format(total, recall, correct, correct * 100.0 / recall))
#loginfo.logger.info("total: {}, recall: {}, correct:{}, accuracy: {}%".format(total, recall, correct, correct*100.0/recall))
def estimate_answer(candidate, answer):
'''
:param candidate:
:param answer:
:return:
'''
candidate = candidate.strip().lower()
answer = answer.strip().lower()
if candidate == answer:
return True
if not answer.isdigit() and candidate.isdigit():
candidate_temp = "{:.5E}".format(int(candidate))
if candidate_temp == answer:
return True
candidate_temp == "{:.4E}".format(int(candidate))
if candidate_temp == answer:
return True
return False
def kb_fuzzy_classify_test():
'''
进行问答测试：
1、 实体检索:输入问题，ner得出实体集合，在数据库中检索与输入实体相关的所有三元组
2、 属性映射——bert分类/文本相似度
+ 非语义匹配：如果所得三元组的关系(attribute)属性是 输入问题 字符串的子集，将所得三元组的答案(answer)属性与正确答案匹配，correct +1
+ 语义匹配：利用bert计算输入问题(input question)与所得三元组的关系(attribute)属性的相似度，将最相似的三元组的答案作为答案，并与正确
的答案进行匹配，correct +1
3、 答案组合
:return:
'''
with open(file, encoding='utf-8') as f:
total = 0
recall = 0
correct = 0
ambiguity = 0 # 属性匹配正确但是答案不正确
for line in f:
try:
total += 1
question, entity, attribute, answer, ner = line.split("\t")
ner = ner.replace("#", "").replace("[UNK]", "%").replace("\n", "")
# case: entity Fuzzy Match
# 找出所有包含这些实体的三元组
sql_e0_a1 = "select * from nlpccQA where entity like '%" + ner + "%' order by length(entity) asc limit 20"
# sql查出来的是tuple，要转换成list才不会报错
result_e0_a1 = list(upload_data(sql_e0_a1))
if len(result_e0_a1) > 0:
recall += 1
flag_fuzzy = True
# 非语义匹配，加快速度
# l1[0]: entity
# l1[1]: attribute
# l1[2]: answer
flag_ambiguity = True
for l in result_e0_a1:
if l[1] in question or l[1].lower() in question or l[1].upper() in question:
flag_fuzzy = False
if estimate_answer(l[2], answer):
correct += 1
flag_ambiguity = False
else:
loginfo.logger.info("\t".join(l))
# 非语义匹配成功，继续下一次
if not flag_fuzzy:
if flag_ambiguity:
ambiguity += 1
time.sleep(1)
loginfo.logger.info("total: {}, recall: {}, correct:{}, accuracy: {}%, ambiguity：{}".format(total, recall, correct, correct * 100.0 / recall, ambiguity))
continue
# 语义匹配
result_df = pd.DataFrame(result_e0_a1, columns=['entity', 'attribute', 'value'])
# loginfo.logger.info(result_df.head(100))
attribute_candicate_sim = [(k, bs.predict(question, k)[0][1]) for k in result_df['attribute'].tolist()]
attribute_candicate_sort = sorted(attribute_candicate_sim, key=lambda candicate: candicate[1], reverse=True)
loginfo.logger.info("\n".join([str(k)+" "+str(v) for (k, v) in attribute_candicate_sort]))
answer_candicate_df = result_df[result_df["attribute"] == attribute_candicate_sort[0][0]]
for row in answer_candicate_df.index:
if estimate_answer(answer_candicate_df.loc[row, "value"], answer):
correct += 1
else:
loginfo.logger.info("\t".join(answer_candicate_df.loc[row].tolist()))
time.sleep(1)
loginfo.logger.info("total: {}, recall: {}, correct:{}, accuracy: {}%, ambiguity：{}".format(total, recall, correct, correct * 100.0 / recall, ambiguity))
except Exception as e:
loginfo.logger.info("the question id % d occur error %s" % (total, repr(e)))
if __name__ == '__main__':
kb_fuzzy_classify_test()