#!/usr/bin/env python

# -*- coding:utf-8 -*-

import jieba

import os

import re

import time

from jpype import *

'''

title：利用结巴分词进行文本语料的批量处理

    1 首先对文本进行遍历查找

    2 创建原始文本的保存结构

    3 对原文本进行结巴分词和停用词处理

    4 对预处理结果进行标准化格式，并保存原文件结构路径

author：白宁超

myblog：http://www.cnblogs.com/baiboy/

time：2017年4月28日10:03:09

'''

'''

创建文件目录

path:根目录下创建子目录

'''

def mkdir(path):

    # 判断路径是否存在

    isExists=os.path.exists(path)

    # 判断结果

    if not isExists:

        os.makedirs(path)

        print(path+' 创建成功')

        return True

    else:

        pass

    print('-->请稍后，文本正在预处理中...')

'''

结巴分词工具进行中文分词处理：

read_folder_path：待处理的原始语料根路径

write_folder_path 中文分词经数据清洗后的语料

'''

def CHSegment(read_folder_path,write_folder_path):

    stopwords ={}.fromkeys([line.strip() for line in open('../Database/stopwords/CH_stopWords.txt','r',encoding='utf-8')]) # 停用词表

    # 获取待处理根目录下的所有类别

    folder_list = os.listdir(read_folder_path)

    # 类间循环

    # print(folder_list)

    for folder in folder_list:

        #某类下的路径

        new_folder_path = os.path.join(read_folder_path, folder)

        # 创建一致的保存文件路径

        mkdir(write_folder_path+folder)

         #某类下的保存路径

        save_folder_path = os.path.join(write_folder_path, folder)

        #某类下的全部文件集

        # 类内循环

        files = os.listdir(new_folder_path)

        j = 1

        for file in files:

            if j > len(files):

                break

            # 读取原始语料

            raw = open(os.path.join(new_folder_path, file),'r',encoding='utf-8').read()

            # 只保留汉字

            # raw1 = re.sub("[A-Za-z0-9\[\`\~\!\@\#\$\^\&\*\(\)\=\|\{\}\'\:\;\'\,\[\]\.\<\>\/\?\~\！\@\#\\\&\*\%]", "", raw)

            # jieba分词

            wordslist = jieba.cut(raw, cut_all=False) # 精确模式

            # 停用词处理

            cutwordlist=''

            for word in wordslist:

                if word not in stopwords and word=="\n":

                    cutwordlist+="\n" # 保持原有文本换行格式

                elif len(word)>1 :

                        cutwordlist+=word+"/" #去除空格

            #保存清洗后的数据

            with open(os.path.join(save_folder_path,file),'w',encoding='utf-8') as f:

                f.write(cutwordlist)

                j += 1

'''

结巴分词工具进行中文分词处理：

read_folder_path：待处理的原始语料根路径

write_folder_path 中文分词经数据清洗后的语料

'''

def HanLPSeg(read_folder_path,write_folder_path):

    startJVM(getDefaultJVMPath(), "-Djava.class.path=C:\hanlp\hanlp-1.3.2.jar;C:\hanlp", "-Xms1g", "-Xmx1g") # 启动JVM，Linux需替换分号;为冒号:

    stopwords ={}.fromkeys([line.strip() for line in open('../Database/stopwords/CH_stopWords.txt','r',encoding='utf-8')]) # 停用词表

    # 获取待处理根目录下的所有类别

    folder_list = os.listdir(read_folder_path)

    # 类间循环

    # print(folder_list)

    for folder in folder_list:

        #某类下的路径

        new_folder_path = os.path.join(read_folder_path, folder)

        # 创建一致的保存文件路径

        mkdir(write_folder_path+folder)

         #某类下的保存路径

        save_folder_path = os.path.join(write_folder_path, folder)

        #某类下的全部文件集

        # 类内循环

        files = os.listdir(new_folder_path)

        j = 1

        for file in files:

            if j > len(files):

                break

            # 读取原始语料

            raw = open(os.path.join(new_folder_path, file),'r',encoding='utf-8').read()

            # HanLP分词

            HanLP = JClass('com.hankcs.hanlp.HanLP')

            wordslist = HanLP.segment(raw)

            #保存清洗后的数据

            wordslist1=str(wordslist).split(",")

            # print(wordslist1[1:len(wordslist1)-1])

            flagresult=""

            # 去除标签

            for v in wordslist1[1:len(wordslist1)-1]:

                if "/" in v:

                    slope=v.index("/")

                    letter=v[1:slope]

                    if len(letter)>0 and '\n\u3000\u3000' in letter:

                        flagresult+="\n"

                    else:flagresult+=letter +"/" #去除空格

            # print(flagresult)

            with open(os.path.join(save_folder_path,file),'w',encoding='utf-8') as f:

                f.write(flagresult.replace(' /',''))

            j += 1

    shutdownJVM()

if __name__ == '__main__' :

    print('开始进行文本分词操作：\n')

    t1 = time.time()

    dealpath="../Database/SogouC/FileTest/"

    savepath="../Database/SogouCCut/FileTest/"

    # 待分词的语料类别集根目录

    read_folder_path = '../Database/SogouC/FileNews/'

    write_folder_path = '../Database/SogouCCut/'

    #jieba中文分词

    CHSegment(read_folder_path,write_folder_path) #300个txtq其中结巴分词使用3.31秒

    HanLPSeg(read_folder_path,write_folder_path) #300个txt其中hanlp分词使用1.83秒

    t2 = time.time()

    print('完成中文文本切分: '+str(t2-t1)+"秒。")