From 01275145378e9554ce02739d58163ede5b009d03 Mon Sep 17 00:00:00 2001
From: TeacherZhu <813664462@qq.com>
Date: Mon, 21 Sep 2020 17:39:31 +0800
Subject: [PATCH] Upload New File

---
 课件/0920聊天机器人main.py | 304 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 304 insertions(+)
 create mode 100644 课件/0920聊天机器人main.py

diff --git "a/\350\257\276\344\273\266/0920\350\201\212\345\244\251\346\234\272\345\231\250\344\272\272main.py" "b/\350\257\276\344\273\266/0920\350\201\212\345\244\251\346\234\272\345\231\250\344\272\272main.py"
new file mode 100644
index 0000000..7776dfe
--- /dev/null
+++ "b/\350\257\276\344\273\266/0920\350\201\212\345\244\251\346\234\272\345\231\250\344\272\272main.py"
@@ -0,0 +1,304 @@
+# coding=utf-8
+
+import pandas as pd
+import fool
+import re
+import random
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+
+# -----------------------------------------------------
+# 加载停用词词典
+stopwords = {}
+with open(r'stopword.txt', 'r', encoding='utf-8') as fr:
+    for word in fr:
+        stopwords[word.strip()] = 0
+# -----------------------------------------------------
+
+
+# 定义类
+class CLF_MODEL:
+    # 类目标:该类将所有模型训练、预测、数据预处理、意图识别的函数包括其中
+
+    # 初始化模块
+    def __init__(self):
+        self.model = LogisticRegression()  # 成员变量,用于存储模型
+        self.vectorizer = TfidfVectorizer()  # 成员变量,用于存储tfidf统计值
+
+    # 训练模块
+    def train(self):
+        # 函数目标:读取训练数据,训练意图分类模型,并将训练好的分类模型赋值给成员变量self.model
+        # input:无
+        # output:无
+
+        # 从excel文件读取训练样本
+        d_train = pd.read_excel("data_train.xlsx")
+        # 对训练数据进行预处理
+        d_train.sentence_train = d_train.sentence_train.apply(self.fun_clean)
+        print("训练样本 = %d" % len(d_train))
+
+        """
+        TODO:利用sklearn中的函数进行训练,将句子转化为特征features
+        """
+
+        features = self.vectorizer.fit_transform(d_train.sentence_train.to_list())
+        self.model.fit(features, d_train.label)
+
+    # 预测模块(使用模型预测)
+    def predict_model(self, sentence):
+        # 函数目标:使用意图分类模型预测意图
+        #  input:sentence(用户输入)
+        # output:clf_result(意图类别),score(意图分数)
+
+        # --------------
+        # 对样本中没有的特殊情况做特别判断
+        if sentence in ["好的", "需要", "是的", "要的", "好", "要", "是"]:
+            return 1, 0.8
+        # --------------
+
+        """
+        TODO:利用已训练好的意图分类模型进行意图识别
+        """
+        features = self.vectorizer.transform([self.fun_clean(sentence)])
+        scores = self.model.predict_proba(features)[0]
+        clf_result = np.argmax(scores)
+        score = scores[clf_result]
+        return clf_result, score
+
+    # 预测模块(使用规则)
+    def predict_rule(self, sentence):
+        # 函数目标:如果模型训练出现异常,可以使用规则进行预测,同时也可以让学员融合"模型"及"规则"的预测方式
+        # input:sentence(用户输入)
+        # output:clf_result(意图类别),score(意图分数)
+
+        sentence = sentence.replace(' ', '')
+        if re.findall(r'不需要|不要|停止|终止|退出|不买|不定|不订', sentence):
+            return 2, 0.8
+        elif re.findall(r'订|定|预定|买|购', sentence) or sentence in ["好的","需要","是的","要的","好","要","是"]:
+            return 1, 0.8
+        else:
+            return 0, 0.8
+
+    # 预处理函数
+    def fun_clean(self, sentence):
+        # 函数目标:预处理函数,将必要的实体转换成统一符号(利于分类准确),去除停用词等
+        # input:sentence(用户输入语句)
+        # output:sentence(预处理结果)
+
+        """
+        TODO:预处理函数,将必要的实体转换成统一符号(利于分类准确),去除停用词等
+        """
+        tokens = fool.cut(sentence)[0]
+        tokens = filter(lambda x:x not in stopwords, tokens)
+
+        sentence = ' '.join(tokens)
+
+        return sentence
+
+    # 分类主函数
+    def fun_clf(self, sentence):
+        # 函数目标:意图识别主函数
+        # input:sentence( 用户输入语句)
+        # output:clf_result(意图类别),score(意图分数)
+
+        # 对用户输入进行预处理
+        sentence = self.fun_clean(sentence)
+        # 得到意图分类结果(0为“查询”类别,1为“订票”类别,2为“终止服务”类别)
+        clf_result, score = self.predict_model(sentence)  # 使用训练的模型进行意图预测
+        # clf_result, score = self.predict_rule(sentence)  # 使用规则进行意图预测(可与用模型进行意图识别的方法二选一)
+        return clf_result, score
+
+
+def fun_replace_num(sentence):
+    # 函数目标:替换时间中的数字(目的是便于实体识别包fool对实体的识别)
+    # input:sentence
+    # output:sentence
+
+    # 定义要替换的数字
+    time_num = {"一":"1","二":"2","三":"3","四":"4","五":"5","六":"6","七":"7","八":"8","九":"9","十":"10","十一":"11","十二":"12"}
+    for k, v in time_num.items():
+        sentence = sentence.replace(k, v)
+    return sentence
+
+
+FROM_INDICATOR = {'从', '由'}
+TO_INDICATOR = {'到', '去', '抵', '达', '飞', '往', '回', '出', '查', '询'}
+def extract_location(sentence, slot, key):
+    # 抽取地点
+    # 找到location命名实体
+    # 判断是出发还是到达
+    words, ners = fool.analysis(sentence)
+    print(words, ners)
+    for start_idx, end_idx, label, content in ners[0]:
+        if label == 'location' or label == "company":
+            if key is None:
+                if 'from_city' in slot:
+                    slot['to_city'] = content
+                elif 'to_city' in slot:
+                    slot['from_city'] = content
+                else:
+                    if start_idx > 0 and sentence[start_idx-1] in FROM_INDICATOR or \
+                            end_idx < len(sentence) and sentence[end_idx] in TO_INDICATOR:
+                        slot['from_city'] = content
+                    if start_idx > 0 and sentence[start_idx-1] in TO_INDICATOR:
+                        slot['to_city'] = content
+                if (not 'from_city' in slot) and (not 'to_city' in slot):
+                    slot['from_city'] = content
+            else:
+                slot[key] = content
+    return slot
+
+
+TEMP_TIME = "\d{1,2}(点|\.|时|:)\d{0,2}(分|:|秒|\.|刻)?(\d{1,2})*"
+def extract_time(sentence, slot):
+    # 抽取时间
+    # 利用正则表达式
+    time = re.search(TEMP_TIME, sentence)
+    if not time is None:
+        slot['time'] = sentence[time.start():time.end()+1]
+    return slot
+
+
+TEMP_DATE = r'\d{4}年\d{1,2}月\d{1,2}日|明天|后天|今天|\d{1,2}月\d{1,2}日|\d{4}[-|/|.]\d{1,2}[-|/|.]\d{1,2}|周\d|星期\d|礼拜\d'
+def extract_date(sentence, slot):
+    # 抽取日期
+    # 利用正则表达式
+    date = re.search(TEMP_DATE, sentence)
+    if not date is None:
+        slot['date'] = sentence[date.start():date.end()+1]
+    return slot
+
+
+def slot_fill(sentence, key=None):
+    # 函数目标:填槽函数(该函数从sentence中寻找需要的内容,完成填槽工作)
+    # input:sentence(用户输入), key(指定槽位,只对该句话提取指定槽位的信息)
+    # output:slot(返回填槽的结果,以json格式返回,key为槽位名,value为值)
+
+    slot = {}
+    # 进行实体识别
+
+    """
+    TODO:从sentence中寻找需要的内容,完成填槽工作
+    """
+    # 地点抽取
+    extract_location(sentence, slot, key)
+
+    # 时间抽取
+    extract_time(sentence, slot)
+
+    # 日期抽取
+    extract_date(sentence, slot)
+
+    return slot
+
+
+def fun_wait(clf_obj):
+    # 函数目标:等待,获取用户输入问句
+    # input:CLF_MODEL类实例化对象
+    # output:clf_result(用户输入意图类别), score(意图识别分数), sentence(用户输入)
+
+    # 等待用户输入
+    print("\n\n\n")
+    print("-------------------------------------------------------------")
+    print("----*------*-----*-----*----*-----*-----*-----*-----*------")
+    print("Starting ...")
+    sentence = input("客服:请问需要什么服务?(时间请用12小时制表示)\n")
+    # 对用户输入进行意图识别
+    clf_result, score = clf_obj.fun_clf(sentence)
+    return clf_result, score, sentence
+
+
+def fun_search(clf_result, sentence):
+    # 函数目标:为用户查询余票
+    # input:clf_result(意图分类结果), sentence(用户输入问句)
+    # output:是否有票
+
+    # 定义槽存储空间
+    name = {"time":"出发时间", "date":"出发日期", "from_city":"出发城市", "to_city":"到达城市"}
+    slot = {"time": "", "date":"", "from_city":"", "to_city":""}
+    # 使用用户第一句话进行填槽
+    sentence = fun_replace_num(sentence)
+    slot_init = slot_fill(sentence)
+    for key in slot_init.keys():
+        slot[key] = slot_init[key]
+    # 对未填充对槽位,向用户提问,进行针对性填槽
+    while "" in slot.values():
+        for key in slot.keys():
+            while slot[key] == "":
+                print(slot)
+                sentence = input("客服:请问%s是?\n"%(name[key]))
+                sentence = fun_replace_num(sentence)
+                slot_cur = slot_fill(sentence, key)
+                print(slot_cur)
+                for _key in slot_cur.keys():
+                    if slot[_key]=="":
+                        slot[_key] = slot_cur[_key]
+
+    # 查询是否有票,并答复用户(本次查询是否有票使用随机数完成,实际情况可查询数据库返回)
+    if random.random()>0.5:
+        print("客服:%s%s从%s到%s的票充足"%(slot["date"], slot["time"], slot["from_city"], slot["to_city"]))
+        # 返回1表示有票
+        return 1
+    else:
+        print("客服:%s%s从%s到%s无票" % (slot["date"], slot["time"], slot["from_city"], slot["to_city"]))
+        print("End !!!")
+        print("----*------*-----*-----*----*-----*-----*-----*-----*------")
+        print("-------------------------------------------------------------")
+        # 返回0表示无票
+        return 0
+
+
+def fun_book():
+    # 函数目标:执行下单订票动作
+    # input:无
+    # output:无
+
+    print("客服:已为您完成订票。\n\n\n")
+    print("End !!!")
+    print("----*------*-----*-----*----*-----*-----*-----*-----*------")
+    print("-------------------------------------------------------------")
+
+
+if __name__=="__main__":
+    # 实例化对象
+    clf_obj = CLF_MODEL()
+    # 完成意图识别模型的训练
+    clf_obj.train()
+    # 用户定义阈值(当分类器分类的分数大于阈值才采纳本次意图分类结果,目的是排除分数过低的意图分类结果)
+    threshold = 0.55
+    # 循环提供服务
+    while 1:
+        clf_result, score, sentence = fun_wait(clf_obj)
+        # -------------------------------------------------------------------------------
+        # 状态转移条件(等待-->等待):用户输入未达到“查询”、“订票”类别的阈值 OR 意图被分类为“终止服务”
+        # -------------------------------------------------------------------------------
+        if score < threshold or clf_result == 2:
+            continue
+
+        # -------------------------------------------------------------------------------
+        # 状态转移条件(等待-->查询):用户输入分类为“查询” OR “订票”
+        # -------------------------------------------------------------------------------
+        else:
+            # 收集订票细节信息
+            search_result = fun_search(clf_result, sentence)
+            # 查询无票
+            # -------------------------------------------------------------------------------
+            # 状态转移条件(查询-->等待):FUN_SEARCH执行完后用户输入意图为“终止服务” OR FUN_SEARCH返回无票
+            # -------------------------------------------------------------------------------
+            if search_result==0:
+                continue
+            # 查询有票
+            else:
+                # 等待用户输入
+                sentence = input("客服:需要为您订票吗?\n")
+                # 对用户输入进行意图识别
+                clf_result, score = clf_obj.fun_clf(sentence)
+                # -------------------------------------------------------------------------------
+                # 状态转移条件(查询-->订票):FUN_SEARCH返回有票 AND 用户输入意图为“订票”
+                # -------------------------------------------------------------------------------
+                if clf_result == 1:
+                    fun_book()
+                    continue
+
--
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