Datawhale 知识图谱组队学习 Task 4 用户输入->知识库的查询语句

 def build_actree(self, wordlist):
        """
        构造actree，加速过滤
        :param wordlist:
        :return:
        """
        actree = ahocorasick.Automaton()
        # 向树中添加单词
        for index, word in enumerate(wordlist):
            actree.add_word(word, (index, word))
        actree.make_automaton()
        return actree

index, (index, word)如下：
0 (0, '长春海外制药接骨续筋片')
1 (1, '香菇炖甲鱼')
2 (2, '三鹤药业黄柏胶囊')
3 (3, '上海衡山熊去氧胆酸片')
4 (4, '升和药业依托泊苷注射液')
5 (5, '怡诺思')
6 (6, '人格障碍')
7 (7, '转铁蛋白饱和度')
8 (8, '脾囊肿')
9 (9, '素烧白萝卜')
10 (10, '利君现代冠脉宁片')
......
43422 (43422, '弯曲菌感染')
43423 (43423, '丝瓜蘑菇肉片汤')
43424 (43424, '长春银诺克清咽片')
43425 (43425, '肝叶萎缩')
43426 (43426, '迪皿盐酸左西替利嗪口服溶液')
43427 (43427, '华润天和麝香壮骨膏')
43428 (43428, '湖北恒安曲咪新乳膏')
43429 (43429, '子宫小')
#############################
import ahocorasick
actree = ahocorasick.Automaton()
for index, word in enumerate(wordlist):
    actree.add_word(word, (index, word))
actree.make_automaton()
#其中wordlist就是上面的那个长度为43430的列表

for i in actree.iter('昨天发烧，服用了阿司匹林,并且还吃了牛黄清胃丸，饭是吃了瓜烧白菜，大便有点色浅'):
    print(i)

def entity_reg(self, question):
        """
        模式匹配, 得到匹配的词和类型。如疾病，疾病别名，并发症，症状
        :param question:str
        :return:
        """
        self.result = {}

        for i in self.disease_tree.iter(question):
            word = i[1][1]
            if "Disease" not in self.result:
                self.result["Disease"] = [word]
            else:
                self.result["Disease"].append(word)

        for i in self.alias_tree.iter(question):
            word = i[1][1]
            if "Alias" not in self.result:
                self.result["Alias"] = [word]
            else:
                self.result["Alias"].append(word)

        for i in self.symptom_tree.iter(question):
            wd = i[1][1]
            if "Symptom" not in self.result:
                self.result["Symptom"] = [wd]
            else:
                self.result["Symptom"].append(wd)

        for i in self.complication_tree.iter(question):
            wd = i[1][1]
            if "Complication" not in self.result:
                self.result["Complication"] = [wd]
            else:
                self.result["Complication"] .append(wd)

        return self.result

def find_sim_words(self, question):
    """
    当全匹配失败时，就采用相似度计算来找相似的词
    :param question:
    :return:
    """
    import re
    import string
    from gensim.models import KeyedVectors
    
    # 使用结巴加载自定义词典
    jieba.load_userdict(self.vocab_path)
    # 加载词向量
    self.model = KeyedVectors.load_word2vec_format(self.word2vec_path, binary=False)
    
    # 数据预处理，正则去除特殊符号
    sentence = re.sub("[{}]", re.escape(string.punctuation), question)
    sentence = re.sub("[，。‘’；：？、！【】]", " ", sentence)
    sentence = sentence.strip()
    
    # 使用结巴进行分词
    words = [w.strip() for w in jieba.cut(sentence) if w.strip() not in self.stopwords and len(w.strip()) >= 2]

    alist = []
    
    # 对每个词，都让其与每类实体词典进行相似对比，
    # 最终选取分数最高的实体和其属于的实体类型
    for word in words:
        temp = [self.disease_entities, self.alias_entities, self.symptom_entities, self.complication_entities]
        for i in range(len(temp)):
            flag = ''
            if i == 0:
                flag = "Disease"
            elif i == 1:
                flag = "Alias"
            elif i == 2:
                flag = "Symptom"
            else:
                flag = "Complication"
            scores = self.simCal(word, temp[i], flag)
            alist.extend(scores)
    temp1 = sorted(alist, key=lambda k: k[1], reverse=True)
    if temp1:
        self.result[temp1[0][2]] = [temp1[0][0]]

# 计算词语和字典中的词的相似度
def simCal(self, word, entities, flag):
    """
    计算词语和字典中的词的相似度
    相同字符的个数/min(|A|,|B|)   +  余弦相似度
    :param word: str
    :param entities:List
    :return:
    """
    a = len(word)
    scores = []
    for entity in entities:
        sim_num = 0
        b = len(entity)
        c = len(set(entity+word))
        temp = []
        for w in word:
            if w in entity:
                sim_num += 1
        if sim_num != 0:
            score1 = sim_num / c  # overlap score
            temp.append(score1)
        try:
            score2 = self.model.similarity(word, entity)  # 余弦相似度分数
            temp.append(score2)
        except:
            pass
        score3 = 1 - self.editDistanceDP(word, entity) / (a + b)  # 编辑距离分数
        if score3:
            temp.append(score3)

        score = sum(temp) / len(temp)
        if score >= 0.7:
            scores.append((entity, score, flag))

    scores.sort(key=lambda k: k[1], reverse=True)
    return scores

# 提取问题的TF-IDF特征
def tfidf_features(self, text, vectorizer):
    """
    提取问题的TF-IDF特征
    :param text:
    :param vectorizer:
    :return:
    """
    jieba.load_userdict(self.vocab_path)
    words = [w.strip() for w in jieba.cut(text) if w.strip() and w.strip() not in self.stopwords]
    sents = [' '.join(words)]

    tfidf = vectorizer.transform(sents).toarray()
    return tfidf

self.symptom_qwds = ['什么症状', '哪些症状', '症状有哪些', '症状是什么', '什么表征', '哪些表征', '表征是什么',
                     '什么现象', '哪些现象', '现象有哪些', '症候', '什么表现', '哪些表现', '表现有哪些',
                     '什么行为', '哪些行为', '行为有哪些', '什么状况', '哪些状况', '状况有哪些', '现象是什么',
                     '表现是什么', '行为是什么']  # 询问症状
self.cureway_qwds = ['药', '药品', '用药', '胶囊', '口服液', '炎片', '吃什么药', '用什么药', '怎么办',
                     '买什么药', '怎么治疗', '如何医治', '怎么医治', '怎么治', '怎么医', '如何治',
                     '医治方式', '疗法', '咋治', '咋办', '咋治', '治疗方法']  # 询问治疗方法
self.lasttime_qwds = ['周期', '多久', '多长时间', '多少时间', '几天', '几年', '多少天', '多少小时',
                      '几个小时', '多少年', '多久能好', '痊愈', '康复']  # 询问治疗周期
self.cureprob_qwds = ['多大概率能治好', '多大几率能治好', '治好希望大么', '几率', '几成', '比例',
                      '可能性', '能治', '可治', '可以治', '可以医', '能治好吗', '可以治好吗', '会好吗',
                      '能好吗', '治愈吗']  # 询问治愈率
self.check_qwds = ['检查什么', '检查项目', '哪些检查', '什么检查', '检查哪些', '项目', '检测什么',
                   '哪些检测', '检测哪些', '化验什么', '哪些化验', '化验哪些', '哪些体检', '怎么查找',
                   '如何查找', '怎么检查', '如何检查', '怎么检测', '如何检测']  # 询问检查项目
self.belong_qwds = ['属于什么科', '什么科', '科室', '挂什么', '挂哪个', '哪个科', '哪些科']  # 询问科室
self.disase_qwds = ['什么病', '啥病', '得了什么', '得了哪种', '怎么回事', '咋回事', '回事',
                    '什么情况', '什么问题', '什么毛病', '啥毛病', '哪种病']  # 询问疾病
 
def other_features(self, text):
    """
    提取问题的关键词特征
    :param text:
    :return:
    """
    features = [0] * 7
    for d in self.disase_qwds:
        if d in text:
            features[0] += 1
    
    for s in self.symptom_qwds:
        if s in text:
            features[1] += 1
    
    for c in self.cureway_qwds:
        if c in text:
            features[2] += 1
    
    for c in self.check_qwds:
        if c in text:
            features[3] += 1
    for p in self.lasttime_qwds:
        if p in text:
            features[4] += 1
    
    for r in self.cureprob_qwds:
        if r in text:
            features[5] += 1
    
    for d in self.belong_qwds:
        if d in text:
            features[6] += 1
    
    m = max(features)
    n = min(features)
    normed_features = []
    if m == n:
        normed_features = features
    else:
        for i in features:
            j = (i - n) / (m - n)
            normed_features.append(j)
    
    return np.array(normed_features)

 # 项目没有给出训练过程，可参考下面sklearn的例子
    from sklearn.naive_bayes import MultinomialNB 

    mnb = MultinomialNB()   
    mnb.fit(X_train,y_train)   
    y_predict = mnb.predict(X_test)

    # 意图分类模型文件
    self.tfidf_path = os.path.join(cur_dir, 'model/tfidf_model.m')
    self.nb_path = os.path.join(cur_dir, 'model/intent_reg_model.m')  #朴素贝叶斯模型
    self.tfidf_model = joblib.load(self.tfidf_path)
    self.nb_model = joblib.load(self.nb_path)

    # 意图预测
    tfidf_feature = self.tfidf_features(question, self.tfidf_model)

    other_feature = self.other_features(question)
    m = other_feature.shape
    other_feature = np.reshape(other_feature, (1, m[0]))
    feature = np.concatenate((tfidf_feature, other_feature), axis=1)
    predicted = self.model_predict(feature, self.nb_model)
    intentions.append(predicted[0])

# 已知疾病，查询症状
if self.check_words(self.symptom_qwds, question) and ('Disease' in types or 'Alia' in types):
    intention = "query_symptom"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病或症状，查询治疗方法
if self.check_words(self.cureway_qwds, question) and \
        ('Disease' in types or 'Symptom' in types or 'Alias' in types or 'Complication' in types):
    intention = "query_cureway"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病或症状，查询治疗周期
if self.check_words(self.lasttime_qwds, question) and ('Disease' in types or 'Alia' in types):
    intention = "query_period"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病，查询治愈率
if self.check_words(self.cureprob_qwds, question) and ('Disease' in types or 'Alias' in types):
    intention = "query_rate"
    if intention not in intentions:
        intentions.append(intention)
# 已知疾病，查询检查项目
if self.check_words(self.check_qwds, question) and ('Disease' in types or 'Alias' in types):
    intention = "query_checklist"
    if intention not in intentions:
        intentions.append(intention)
# 查询科室
if self.check_words(self.belong_qwds, question) and \
        ('Disease' in types or 'Symptom' in types or 'Alias' in types or 'Complication' in types):
    intention = "query_department"
    if intention not in intentions:
        intentions.append(intention)
# 已知症状，查询疾病
if self.check_words(self.disase_qwds, question) and ("Symptom" in types or "Complication" in types):
    intention = "query_disease"
    if intention not in intentions:
        intentions.append(intention)

# 若没有检测到意图，且已知疾病，则返回疾病的描述
if not intentions and ('Disease' in types or 'Alias' in types):
    intention = "disease_describe"
    if intention not in intentions:
        intentions.append(intention)
# 若是疾病和症状同时出现，且出现了查询疾病的特征词，则意图为查询疾病
if self.check_words(self.disase_qwds, question) and ('Disease' in types or 'Alias' in types) \
        and ("Symptom" in types or "Complication" in types):
    intention = "query_disease"
    if intention not in intentions:
        intentions.append(intention)
# 若没有识别出实体或意图则调用其它方法
if not intentions or not types:
    intention = "QA_matching"
    if intention not in intentions:
        intentions.append(intention)

self.result["intentions"] = intentions