money-mining-python/src/money.py

# -*- coding:utf-8 -*-
import math
import mmap
import os
import re
import time
from concurrent.futures import ProcessPoolExecutor, as_completed

import redis
from bitmap import BitMap
from tqdm import tqdm

import utils
import jieba
import zipfile
import logging

# 文件后缀：长尾词.txt
FILE_SUFFIX_LONG_TAIL = "_长尾词.txt"

# 文件后缀：长尾词_合并.txt
FILE_LONG_TAIL_MERGE = "长尾词_合并.txt"

# 文件：长尾词_合并_分词.txt
FILE_LONG_TAIL_MERGE_SPLIT = "长尾词_合并_分词.txt"

# 文件：长尾词_合并_分词倒排索引.txt
FILE_LONG_TAIL_MERGE_REVERSE_INDEX = "长尾词_合并_倒排索引.txt"

# 文件：长尾词_合并_聚合.txt
FILE_LONG_TAIL_MERGE_AGG = "长尾词_合并_聚合.txt"

# 子文件：长尾词_合并_聚合_%s.txt
FILE_LONG_TAIL_MERGE_AGG_PID = "长尾词_合并_聚合_%s.txt"

# 缓存前缀：分词词根
CACHE_WORD_STEM = "word:stem"

# 缓存前缀：倒排索引
CACHE_WORD_REVERSE_INDEX = "word:reverse_index"

# 缓存：长尾词缓存
CACHE_WORD = "word"

# 缓存：聚合位图
CACHE_UNUSED_BITMAP = "unused_bitmap"

# 字符集：UTF-8
CHARSET_UTF_8 = "UTF-8"


def extract_word_from_5118(file_path: str):
    """
    从5118关键词压缩文件中提取数据
    :param file_path: 待处理文件夹路径
    :return: None
    """
    file_list = []
    for file in os.listdir(file_path):
        file_list.append(os.path.join(file_path, file))

    for i, file in enumerate(file_list):
        zfile = zipfile.ZipFile(file)
        filenames = zfile.namelist()
        for filename in filenames:
            # 重新编码文件名为正确形式
            real_name = filename.encode('cp437').decode('gbk')

            # 排除无效文件
            if real_name in ['打开乱码如何处理？.txt']:
                continue

            # 关键词存放容器
            word_container = set()

            # 读取压缩文件中的文件
            with zfile.open(filename) as file_content:
                lines = file_content.readlines()
                # 跳过开头两行
                for line in lines[2:]:
                    split = line.decode("gbk").split(",")
                    # 只需要第一列的数据
                    word_container.add(split[0])

            output_file_name = real_name[0:real_name.index("--")]
            output_file_path = os.path.join(file_path, output_file_name + FILE_SUFFIX_LONG_TAIL)
            with open(output_file_path, "w", encoding="utf-8") as f:
                for item in word_container:
                    f.write(item)
                    f.write("\n")


def merge_word(file_path: str):
    """
    合并长尾词（带去重）
    :param file_path: 待处理文件夹路径
    :return: None
    """
    # 获取文件列表
    file_list = []
    for file in os.listdir(file_path):
        if file.endswith(FILE_SUFFIX_LONG_TAIL):
            file_list.append(os.path.join(file_path, file))

    # 长尾词集合容器
    word_set = set()

    # 读取数据并排重
    for i, file in enumerate(file_list):
        with open(file, "r", encoding="utf-8") as f:
            for word in f:
                word_set.add(word.replace("\n", ""))

    # 保存合并结果
    with open(os.path.join(file_path, FILE_LONG_TAIL_MERGE), "w", encoding="utf-8") as f:
        for item in word_set:
            f.write(item)
            f.write("\n")


def word_split_statistics(file_path: str):
    """
    分词统计
    :param file_path: 待处理文件夹路径
    :return: None
    """

    file_list = []
    for file in os.listdir(file_path):
        file_list.append(os.path.join(file_path, file))

    stop_word_dict = utils.load_stop_word()

    for i, file in enumerate(file_list):
        if not file.endswith(FILE_SUFFIX_LONG_TAIL):
            continue

        # 分词结果容器
        key_dict = {}

        with open(file, "r", encoding="utf-8") as f:
            for tmp_word in f:
                # 分词
                word_list = jieba.cut_for_search(tmp_word.replace("\n", ""))
                # 统计
                for word in word_list:
                    # 过滤停用词
                    if word in stop_word_dict:
                        continue

                    if word in key_dict:
                        key_dict[word] = key_dict[word] + 1
                    else:
                        key_dict[word] = 1

        # 根据词频进行倒序排列
        sorted_key_list = sorted(key_dict.items(), key=lambda x: x[1], reverse=True)

        output_file_name = file[file.rindex("\\") + 1:file.index(FILE_SUFFIX_LONG_TAIL)]
        output_file_path = os.path.join(file_path, output_file_name + "_长尾词_分词统计.csv")
        with open(output_file_path, "w", encoding="UTF-8") as f:
            for key, count in sorted_key_list:
                f.write("%s,%d\n" % (key, count))


def word_split_reverse(input_file: str, start_pos: int, end_pos: int):
    """
    分词和建立倒排索引
    :param input_file: 待处理的文件
    :param start_pos: 处理的开始位置
    :param end_pos: 处理的结束位置
    :return: (开始位置，分词结果，倒排索引)
    """

    # 加载停用词
    stop_word_dict = utils.load_stop_word()
    # 关键词存放容器
    word_arr_list = []
    # 倒排索引
    word_reverse_index = dict()

    with open(input_file, "r", encoding="utf-8") as fr:
        for i, tmp_word in enumerate(fr):

            # start_pos是行数，而i要从0开始
            if i + 1 < start_pos:
                continue

            # 当前位置
            cur_pos = i + 1

            # 到达任务边界，结束
            if cur_pos == end_pos:
                break

            # 分词
            word_list = jieba.cut_for_search(tmp_word.replace("\n", ""))

            # 分词过滤结果
            word_filter_arr = []

            # 过滤停用词
            for word in word_list:
                if word in stop_word_dict:
                    continue

                word_index_arr = word_reverse_index.get(word)
                if word_index_arr:
                    word_index_arr.append(cur_pos)
                else:
                    word_reverse_index[word] = [cur_pos]

                word_filter_arr.append(word)

            if len(word_filter_arr) == 0:
                word_arr_list.append([])
            else:
                word_arr_list.append(word_filter_arr)

    return start_pos, word_arr_list, word_reverse_index


def prepare_word_split_and_reverse_index(file_path: str):
    """
    预处理：长尾词分词、建立倒排索引
    :param file_path: 待处理文件夹路径
    :return:
    """

    # 判断文件是否存在
    word_input_file = os.path.join(file_path, FILE_LONG_TAIL_MERGE)
    if os.path.exists(word_input_file) and not os.path.isfile(word_input_file):
        print("文件不存在！ " + word_input_file)
        return

    # 总文本数量
    total_line_num = 0
    with open(word_input_file, "r", encoding="utf-8") as fi:
        total_line_num = sum(1 for line in fi)

    if total_line_num == 0:
        print("没有待处理的数据，文本量为0")
        return

    # 分割任务数量
    task_list = utils.avg_split_task(total_line_num, math.ceil(total_line_num / os.cpu_count()))

    # 任务进程处理结果
    p_result_list = []

    # 多进程处理
    with ProcessPoolExecutor(os.cpu_count()) as process_pool:
        # 提交任务
        process_futures = [process_pool.submit(word_split_reverse, word_input_file, task[0], task[1]) for task in
                           task_list]

        # 处理返回结果
        for p_future in as_completed(process_futures):
            p_result = p_future.result()
            if p_result:
                p_result_list.append(p_result)

        # 分词结果排序
        p_result_list = sorted(p_result_list, key=lambda v: v[0])
        # 输出分词结果
        split_output_file = os.path.join(file_path, FILE_LONG_TAIL_MERGE_SPLIT)
        with open(split_output_file, "w", encoding="UTF-8") as fo:
            for start_pos, word_arr_list, reverse_index in p_result_list:
                for word_arr in word_arr_list:
                    fo.write("%s\n" % ",".join([str(i) for i in word_arr]))

        # 关键词倒排索引
        word_reverse_index_dict = dict()
        # 合并倒排索引
        for start_pos, word_arr, reverse_index_dict in p_result_list:
            for key, value in reverse_index_dict.items():
                reverse_index_arr = word_reverse_index_dict.get(key)
                if reverse_index_arr:
                    reverse_index_arr.extend(value)
                else:
                    word_reverse_index_dict[key] = value
        # 输出倒排索引
        with open(os.path.join(file_path, FILE_LONG_TAIL_MERGE_REVERSE_INDEX), "w", encoding="UTF-8") as fo:
            for key, value in word_reverse_index_dict.items():
                fo.write("%s,%s\n" % (key, value))

        # 关闭进程池
        process_pool.shutdown()


def agg_word_process(file_path: str, agg_threshold: float, start_pos: int, end_pos: int, final_pos:int, skip_line: int):
    """
    长尾词聚合处理
    :param file_path: 文件路径
    :param word_file: 长尾词文件路径
    :param agg_threshold: 聚合阈值
    :param start_pos: 任务处理开始边界（包含）
    :param end_pos: 任务处理结束边界（不包含）
    :param final_pos: 总任务边界
    :param skip_line: 进度条显示位置
    :return:
    """

    # 生成临时结果文件
    word_agg_file = os.path.join(file_path, FILE_LONG_TAIL_MERGE_AGG_PID % os.getpid())

    # redis缓存池
    redis_pool = redis.ConnectionPool(host='127.0.0.1', port=6379, max_connections=1)

    # redis缓存
    redis_cache = redis.StrictRedis(connection_pool=redis_pool)

    # 进度长度
    process_len = 0
    if end_pos == -1:
        process_len = final_pos - start_pos
    else:
        process_len = end_pos - start_pos

    with (open(word_agg_file, "a", encoding="UTF-8") as fo,
            tqdm(total=process_len, desc='子进程-%s：文本聚合进度' % os.getpid(), unit='份', unit_scale=True, position=skip_line) as pbar):

        for main_word_position in range(start_pos, end_pos):
            try:
                # 判断主词是否为已使用，是则跳过，否则设置为已使用
                if redis_cache.getbit(CACHE_UNUSED_BITMAP, main_word_position):
                    continue
                else:
                    redis_cache.setbit(CACHE_UNUSED_BITMAP, main_word_position, 1)

                # 获取主词,移除换行符
                main_word = redis_cache.hget(CACHE_WORD, main_word_position)
                if not main_word:
                    continue
                main_word = main_word.decode(CHARSET_UTF_8)

                # 获取主词分词结果
                main_word_stem = redis_cache.hget(CACHE_WORD_STEM, main_word_position)
                # 为空则跳过
                if not main_word_stem:
                    continue
                main_word_stem_list = main_word_stem.decode(CHARSET_UTF_8).split(",")

                # 从倒排索引中获取候选词的位置索引
                candidate_position_set = set()
                temp_candidate_position_list = redis_cache.hmget(CACHE_WORD_REVERSE_INDEX, main_word_stem_list)
                for temp_candidate_position in temp_candidate_position_list:
                    if not temp_candidate_position:
                        continue
                    candidate_position_set.update(temp_candidate_position.decode(CHARSET_UTF_8).split(","))

                # 没有找到需要计算的候选词则跳过
                if not candidate_position_set:
                    continue

                # 结果列表
                result_list = []

                # 计算相似度
                for candidate_position in candidate_position_set:
                    # 跳过重复
                    if main_word_position == candidate_position:
                        continue

                    # 获取关键词
                    candidate_word = redis_cache.hget(CACHE_WORD, candidate_position)
                    if not candidate_word:
                        continue
                    candidate_word = candidate_word.decode(CHARSET_UTF_8)

                    # 获取分词结果
                    candidate_word_stem = redis_cache.hget(CACHE_WORD_STEM, candidate_position)
                    # 为空则跳过
                    if not candidate_word_stem:
                        continue
                    candidate_word_stem_list = candidate_word_stem.decode(CHARSET_UTF_8).split(",")

                    # 计算相关性
                    try:
                        val = utils.cal_cos_sim(main_word, main_word_stem_list, candidate_word,
                                                candidate_word_stem_list)
                        if val >= agg_threshold:
                            redis_cache.setbit(CACHE_UNUSED_BITMAP, candidate_position, 1)
                            result_list.append(candidate_word)
                    except Exception as e:
                        logging.error("主关键词：%s 发生异常，涉及的副关键词信息-关键词：%s，分词：%s" % (
                            main_word, candidate_word, candidate_word_stem_list), e)

                # 保存结果
                if len(result_list) > 0:
                    fo.write("%s\n" % main_word)
                    for candidate_word in result_list:
                        fo.write("%s\n" % candidate_word)
                    fo.write("\n")

                # 清除容器数据
                candidate_position_set.clear()
                result_list.clear()

                # 更新发呆进度
                pbar.update(1)
            except Exception as e:
                logging.error("子进程发生异常", e)

    return


def agg_word(file_path: str):
    """
    长尾词聚合
    :param file_path:
    :return:
    """

    # 总长尾词数量
    word_total_num = 0

    # 聚合阈值
    agg_threshold = 0.8

    # 每份任务计算量
    task_cal_num = 10000

    # 工作现成(减1是为了留一个处理器给redis)
    worker_num = os.cpu_count() - 1
    # worker_num = 1

    # 正则表达式：聚合文件分文件
    agg_file_pattern = re.compile(r"长尾词_合并_聚合_\d+.txt", re.I)

    # redis缓存
    redis_cache = redis.StrictRedis(host='127.0.0.1', port=6379)

    # 判断文件是否存在
    for file_name in [FILE_LONG_TAIL_MERGE, FILE_LONG_TAIL_MERGE_SPLIT, FILE_LONG_TAIL_MERGE_REVERSE_INDEX]:
        input_file = os.path.join(file_path, file_name)
        if os.path.exists(input_file) and not os.path.isfile(input_file):
            raise Exception("文件不存在！文件路径：" + input_file)

    # 删除历史数据文件
    for file in os.listdir(file_path):
        if agg_file_pattern.match(file):
            os.remove(os.path.join(file_path, file))

    # 缓存关键词位置
    word_file = os.path.join(filePath, FILE_LONG_TAIL_MERGE)
    word_dict = {}
    with open(word_file, "r", encoding="utf-8") as f:
        for position, word in enumerate(f, start=1):
            word = utils.remove_line_break(word)
            if not word:
                continue
            word_dict[position] = word
    redis_cache.hset(CACHE_WORD, mapping=word_dict)
    # 记录总关键词数
    word_total_num = len(word_dict)
    # 释放内存
    del word_dict
    #
    # # 缓存分词
    # word_split_file = os.path.join(file_path, FILE_LONG_TAIL_MERGE_SPLIT)
    # word_split_dict = {}
    # with open(word_split_file, "r", encoding=CHARSET_UTF_8) as f:
    #     for position, word_split_line in enumerate(f, start=1):
    #         word_split_line = utils.remove_line_break(word_split_line)
    #         if not word_split_line:
    #             continue
    #         word_split_dict[position] = word_split_line
    # redis_cache.hset(CACHE_WORD_STEM, mapping=word_split_dict)
    # # 释放内存
    # del word_split_dict
    #
    # # 缓存倒排索引
    # word_reverse_index_file = os.path.join(file_path, FILE_LONG_TAIL_MERGE_REVERSE_INDEX)
    # word_reverse_index_dict = {}
    # # 分词
    # key_pattern = re.compile(r"([^,]+),\[", re.I)
    # # 索引
    # index_pattern = re.compile(r"\d+", re.I)
    # with open(word_reverse_index_file, "r", encoding="utf-8") as f:
    #     for word_split_line in f:
    #         key_m = key_pattern.match(word_split_line)
    #         key = key_m.group(1)
    #         val = index_pattern.findall(word_split_line[word_split_line.index(","):])
    #         word_reverse_index_dict[key] = ",".join(val)
    # redis_cache.hset(CACHE_WORD_REVERSE_INDEX, mapping=word_reverse_index_dict)
    # # 释放内存
    # del word_reverse_index_dict

    # 构建长尾词使用位图
    redis_cache.setbit(CACHE_UNUSED_BITMAP, word_total_num + 1, 1)

    # 提交任务 并输出结果
    word_agg_file = os.path.join(file_path, FILE_LONG_TAIL_MERGE_AGG)

    with ProcessPoolExecutor(max_workers=worker_num) as process_pool:
        # 计算任务边界
        task_list = utils.avg_split_task(word_total_num, task_cal_num, 1)

        # 提交任务
        process_futures = []
        for skip_line, pos in enumerate(task_list, start=1):
            # skip_line =  (skip_line % 4 ) + 1
            skip_line = 1
            p_future = process_pool.submit(agg_word_process, file_path, agg_threshold, pos[0], pos[1], word_total_num, skip_line)
            process_futures.append(p_future)

        # 显示任务进度
        with tqdm(total=len(process_futures), desc='文本聚合进度', unit='份', unit_scale=True, position=0) as pbar:
            for p_future in as_completed(process_futures):
                p_result = p_future.result()
                # 更新发呆进度
                pbar.update(1)

        # 关闭线程
        process_pool.shutdown()

    # 获取子进程结果文件列表，并合并
    with open(word_agg_file, "w", encoding="UTF-8") as fo:
        for file in os.listdir(file_path):
            # 不是处理结果部分跳过
            if not agg_file_pattern.match(file):
                continue

            with open(os.path.join(file_path, file), "r", encoding="UTF-8") as fi:
                for word in fi:
                    fo.write(word)


if __name__ == "__main__":
    print("开始时间" + time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()))
    # filePath = "../data"
    filePath = "../data/test"
    # extract_word_from_5118(filePath)
    # merge_word(filePath)
    # prepare_word_split_and_reverse_index(filePath)
    agg_word(filePath)
    # word_split_statistics(file_path)
    # tasks = utils.avg_split_task(100, 12, 1)
    # 两者计算余弦值等于：0.8
    # val = utils.cal_cos_sim("QQ邮箱格式怎么写", ["QQ", "邮箱", "格式", "怎么", "写"], "QQ邮箱格式如何写",
    #                         ["QQ", "邮箱", "格式", "如何", "写"])
    print("结束时间" + time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()))