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【自然语言处理篇】--Chatterbot聊天机器人

一、前述

ChatterBot是一个基于机器学习的聊天机器人引擎,构建在python上,主要特点是可以自可以从已有的对话中进行学(jiyi)习(pipei)。

二、具体

1、安装

是的,安装超级简单,用pip就可以啦

pip install chatterbot

2、流程

大家已经知道chatterbot的聊天逻辑和输入输出以及存储,是由各种adapter来限定的,我们先看看流程图,一会软再一起看点例子,看看怎么用。

 

 

3、每个部分都设计了不同的“适配器”(Adapter)。

机器人应答逻辑 => Logic Adapters
Closest Match Adapter  字符串模糊匹配(编辑距离)

Closest Meaning Adapter  借助nltk的WordNet,近义词评估
Time Logic Adapter 处理涉及时间的提问
Mathematical Evaluation Adapter 涉及数学运算

存储器后端 => Storage Adapters
 Read Only Mode 只读模式,当有输入数据到chatterbot的时候,数
据库并不会发生改变
 Json Database Adapter 用以存储对话数据的接口,对话数据以Json格式
进行存储。
Mongo Database Adapter  以MongoDB database方式来存储对话数据

输入形式 => Input Adapters

Variable input type adapter 允许chatter bot接收不同类型的输入的,如strings,dictionaries和Statements
Terminal adapter 使得ChatterBot可以通过终端进行对话
 HipChat Adapter 使得ChatterBot 可以从HipChat聊天室获取输入语句,通过HipChat 和 ChatterBot 进行对话
Speech recognition 语音识别输入,详见chatterbot-voice

输出形式 => Output Adapters
Output format adapter支持text,json和object格式的输出
Terminal adapter
HipChat Adapter
Mailgun adapter允许chat bot基于Mailgun API进行邮件的发送
Speech synthesisTTS(Text to speech)部分,详见chatterbot-voice

4、代码

基础版本

# -*- coding: utf-8 -*-
from chatterbot import ChatBot


# 构建ChatBot并指定Adapter
bot = ChatBot(
    'Default Response Example Bot',
    storage_adapter='chatterbot.storage.JsonFileStorageAdapter',#存储的Adapter
    logic_adapters=[
        {
            'import_path': 'chatterbot.logic.BestMatch'#回话逻辑
        },
        {
            'import_path': 'chatterbot.logic.LowConfidenceAdapter',#回话逻辑
            'threshold': 0.65,#低于置信度,则默认回答
            'default_response': 'I am sorry, but I do not understand.'
        }
    ],
    trainer='chatterbot.trainers.ListTrainer'#给定的语料是个列表
)

# 手动给定一点语料用于训练
bot.train([
    'How can I help you?',
    'I want to create a chat bot',
    'Have you read the documentation?',
    'No, I have not',
    'This should help get you started: http://chatterbot.rtfd.org/en/latest/quickstart.html'
])

# 给定问题并取回结果
question = 'How do I make an omelette?'
print(question)
response = bot.get_response(question)
print(response)

print("\n")
question = 'how to make a chat bot?'
print(question)
response = bot.get_response(question)
print(response)

 

结果:

How do I make an omelette?
I am sorry, but I do not understand.


how to make a chat bot?
Have you read the documentation?

 

处理时间和数学计算的Adapter

# -*- coding: utf-8 -*-
from chatterbot import ChatBot


bot = ChatBot(
    "Math & Time Bot",
    logic_adapters=[
        "chatterbot.logic.MathematicalEvaluation",
        "chatterbot.logic.TimeLogicAdapter"
    ],
    input_adapter="chatterbot.input.VariableInputTypeAdapter",
    output_adapter="chatterbot.output.OutputAdapter"
)

# 进行数学计算
question = "What is 4 + 9?"
print(question)
response = bot.get_response(question)
print(response)

print("\n")

# 回答和时间相关的问题
question = "What time is it?"
print(question)
response = bot.get_response(question)
print(response)

 

 结果:

What is 4 + 9?
( 4 + 9 ) = 13

What time is it?
The current time is 05:08 PM

 导出语料到json文件

# -*- coding: utf-8 -*-
from chatterbot import ChatBot

'''
如果一个已经训练好的chatbot,你想取出它的语料,用于别的chatbot构建,可以这么做
'''

chatbot = ChatBot(
    'Export Example Bot',
    trainer='chatterbot.trainers.ChatterBotCorpusTrainer'
)

# 训练一下咯
chatbot.train('chatterbot.corpus.english')

# 把语料导出到json文件中
chatbot.trainer.export_for_training('./my_export.json')

反馈式学习聊天机器人

# -*- coding: utf-8 -*-
from chatterbot import ChatBot
import logging

"""
反馈式的聊天机器人,会根据你的反馈进行学习
"""

# 把下面这行前的注释去掉,可以把一些信息写入日志中
# logging.basicConfig(level=logging.INFO)

# 创建一个聊天机器人
bot = ChatBot(
    'Feedback Learning Bot',
    storage_adapter='chatterbot.storage.JsonFileStorageAdapter',
    logic_adapters=[
        'chatterbot.logic.BestMatch'
    ],
    input_adapter='chatterbot.input.TerminalAdapter',#命令行端
    output_adapter='chatterbot.output.TerminalAdapter'
)

DEFAULT_SESSION_ID = bot.default_session.id


def get_feedback():
    from chatterbot.utils import input_function

    text = input_function()

    if 'Yes' in text:
        return True
    elif 'No' in text:
        return False
    else:
        print('Please type either "Yes" or "No"')
        return get_feedback()


print('Type something to begin...')

# 每次用户有输入内容,这个循环就会开始执行
while True:
    try:
        input_statement = bot.input.process_input_statement()
        statement, response = bot.generate_response(input_statement, DEFAULT_SESSION_ID)

        print('\n Is "{}" this a coherent response to "{}"? \n'.format(response, input_statement))

        if get_feedback():
            bot.learn_response(response,input_statement)

        bot.output.process_response(response)

        # 更新chatbot的历史聊天数据
        bot.conversation_sessions.update(
            bot.default_session.id_string,
            (statement, response, )
        )

    # 直到按ctrl-c 或者 ctrl-d 才会退出
    except (KeyboardInterrupt, EOFError, SystemExit):
        break

 使用Ubuntu数据集构建聊天机器人

from chatterbot import ChatBot
import logging


'''
这是一个使用Ubuntu语料构建聊天机器人的例子
'''

# 允许打日志
logging.basicConfig(level=logging.INFO)

chatbot = ChatBot(
    'Example Bot',
    trainer='chatterbot.trainers.UbuntuCorpusTrainer'
)

# 使用Ubuntu数据集开始训练
chatbot.train()

# 我们来看看训练后的机器人的应答
response = chatbot.get_response('How are you doing today?')
print(response)

借助微软的聊天机器人

 

# -*- coding: utf-8 -*-
from chatterbot import ChatBot
from settings import Microsoft

'''
关于获取微软的user access token请参考以下的文档
https://docs.botframework.com/en-us/restapi/directline/
'''

chatbot = ChatBot(
    'MicrosoftBot',
    directline_host = Microsoft['directline_host'],
    direct_line_token_or_secret = Microsoft['direct_line_token_or_secret'],
    conversation_id = Microsoft['conversation_id'],
    input_adapter='chatterbot.input.Microsoft',
    output_adapter='chatterbot.output.Microsoft',
    trainer='chatterbot.trainers.ChatterBotCorpusTrainer'
)

chatbot.train('chatterbot.corpus.english')

# 是的,会一直聊下去
while True:
    try:
        response = chatbot.get_response(None)

    # 直到按ctrl-c 或者 ctrl-d 才会退出
    except (KeyboardInterrupt, EOFError, SystemExit):
        break

HipChat聊天室Adapter

# -*- coding: utf-8 -*-
from chatterbot import ChatBot
from settings import HIPCHAT

'''
炫酷一点,你可以接到一个HipChat聊天室,你需要一个user token,下面文档会告诉你怎么做
https://developer.atlassian.com/hipchat/guide/hipchat-rest-api/api-access-tokens
'''

chatbot = ChatBot(
    'HipChatBot',
    hipchat_host=HIPCHAT['HOST'],
    hipchat_room=HIPCHAT['ROOM'],
    hipchat_access_token=HIPCHAT['ACCESS_TOKEN'],
    input_adapter='chatterbot.input.HipChat',
    output_adapter='chatterbot.output.HipChat',
    trainer='chatterbot.trainers.ChatterBotCorpusTrainer'
)

chatbot.train('chatterbot.corpus.english')

# 没错,while True,会一直聊下去!
while True:
    try:
        response = chatbot.get_response(None)

    # 直到按ctrl-c 或者 ctrl-d 才会退出
    except (KeyboardInterrupt, EOFError, SystemExit):
        break

邮件回复的聊天系统

# -*- coding: utf-8 -*-
from chatterbot import ChatBot
from settings import MAILGUN

'''
这个功能需要你新建一个文件settings.py,并在里面写入如下的配置:
MAILGUN = {
    "CONSUMER_KEY": "my-mailgun-api-key",
    "API_ENDPOINT": "https://api.mailgun.net/v3/my-domain.com/messages"
}
'''

# 下面这个部分可以改成你自己的邮箱
FROM_EMAIL = "mailgun@salvius.org"
RECIPIENTS = ["gunthercx@gmail.com"]

bot = ChatBot(
    "Mailgun Example Bot",
    mailgun_from_address=FROM_EMAIL,
    mailgun_api_key=MAILGUN["CONSUMER_KEY"],
    mailgun_api_endpoint=MAILGUN["API_ENDPOINT"],
    mailgun_recipients=RECIPIENTS,
    input_adapter="chatterbot.input.Mailgun",
    output_adapter="chatterbot.output.Mailgun",
    storage_adapter="chatterbot.storage.JsonFileStorageAdapter",
    database="../database.db"
)

# 简单的邮件回复
response = bot.get_response("How are you?")
print("Check your inbox at ", RECIPIENTS)

一个中文的例子

注意chatterbot,中文聊天机器人的场景下一定要用python3.X,用python2.7会有编码问题。

#!/usr/bin/python
# -*- coding: utf-8 -*-

#手动设置一些语料
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
 
 
Chinese_bot = ChatBot("Training demo")
Chinese_bot.set_trainer(ListTrainer)
Chinese_bot.train([
    '你好',
    '你好',
    '有什么能帮你的?',
    '想买数据科学的课程',
    '具体是数据科学哪块呢?'
    '机器学习',
])
 
# 测试一下
question = '你好'
print(question)
response = Chinese_bot.get_response(question)
print(response)

print("\n")

question = '请问哪里能买数据科学的课程'
print(question)
response = Chinese_bot.get_response(question)
print(response)

结果:

你好
你好


请问哪里能买数据科学的课程
具体是数据科学哪块呢?

利用已经提供好的小中文语料库

#!/usr/bin/python
# -*- coding: utf-8 -*-
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer
 
chatbot = ChatBot("ChineseChatBot")
chatbot.set_trainer(ChatterBotCorpusTrainer)
 
# 使用中文语料库训练它
chatbot.train("chatterbot.corpus.chinese")
 
# 开始对话
while True:
    print(chatbot.get_response(input(">")))

 

posted @ 2018-07-10 23:07  L先生AI课堂  阅读(...)  评论(... 编辑 收藏