BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/channel/re_channel_103.py

import pandas as pd
import re

# 各投标人
# 各潜在投标人
# 各潜在投标人：
# 致各招标文件持有者：
# 致各投标人
# 各潜在投标供应商：

# 修改、澄清(答疑)纪要内容如下: 1、
# 答疑澄清与修改的主要内容：
# 对文件澄清与修改的主要内容
# 澄清、修改内容要点
# 答疑纪要
# 答疑如下
# 招标文件答疑和招标文件修改通知
# 招标文件答疑通知
# 答疑及补遗通知
# 答疑回复如下：
# 现对投标人提出的质疑回复如下：
# 对文件澄清与修改的主要内容 详见招标文件
# 修改的主要内容 详见附件
# 澄清或修改事项：

# 第1次答疑
# 第1次答疑澄清

# 答疑补遗文件
# 补遗书澄清文件 答疑澄清
# 质疑1
# 问题
# 答疑文件1
# 具体补遗内容详见附件
# 请问 答
# 问题 回复
# 答疑澄清公告 1：
# 现对招标文件作如下澄清：
# 详见答疑澄清文件
# 详见答疑文件。


channel_103 = '(澄清|答疑|补遗|修改)'
channel_103_0 = '(致|至|)(各|各个)(潜在|)(投标|招标|招标文件持有|报价|竞选|)(人|者|供应商|单位)(:|：)'
channel_103_1 = '(澄清|答疑|补遗|修改|质疑)(.?)(具体内容|主要内容|内容|回复|发布|纪要|事项|如下){1,2}(.?)' \
                '(如下|[：:]|详见|点击下载附件|[1一][:：、]|（1）|\\(1\\)|一)'
channel_103_2 = '第(.?)次(答疑|澄清)'
channel_103_3 = '(澄清|答疑|补遗|修改)(公告|文件)'
channel_103_after = '(请问|提问|问题|答复|回复|质疑|答|问){1,2}[12一]?[:：]|[一1][:：、]|（1）|\\(1\\)|(详见|见)(附件|答疑文件|澄清文件|答疑澄清文件)'
channel_103_4 = '(补充答疑|提疑内容|请问|提问|问题|回复|答复|答疑|质疑|答|问)[12一]?[:：]'
channel_103_5 = '(见|详见)(答疑澄清文件|澄清文件|答疑文件)|补遗内容详见附件'

# 答疑澄清时间
# 对文件澄清与修改的主要内容 无澄清文件
# 对文件澄清与修改的主要内容 无
# 请各投标单位自行下载
not_channel_103 = '答疑澄清时间|主要内容.?无|请各投标单位'


def re_standard_channel_103(_str):
    channel_103_list = []

    if not re.search(channel_103, _str):
        print("not")
        return channel_103_list

    reg_standard = "(?P<value>" + channel_103_0 + ")"
    match = re.finditer(reg_standard, _str)
    for m in match:
        m_dict = m.groupdict()
        m_span = m.span()
        keyword_index = [m_span[0], m_span[1]]
        keyword = m_dict.get('value')
        channel_103_list.append([keyword, keyword_index])
    if channel_103_list:
        print("0", channel_103_list)
        return channel_103_list

    reg_standard = "(?P<value>" + channel_103_1 + ")"
    match = re.finditer(reg_standard, _str)
    for m in match:
        m_dict = m.groupdict()
        m_span = m.span()
        keyword_index = [m_span[0], m_span[1]]
        keyword = m_dict.get('value')
        channel_103_list.append([keyword, keyword_index])
    if channel_103_list:
        print("1", channel_103_list)
        return channel_103_list

    reg_standard = "(?P<value>" + channel_103_2 + ")"
    match = re.finditer(reg_standard, _str)
    for m in match:
        m_dict = m.groupdict()
        m_span = m.span()
        keyword_index = [m_span[0], m_span[1]]
        keyword = m_dict.get('value')
        if re.search(channel_103_after, _str[keyword_index[1]:keyword_index[1]+50]):
            channel_103_list.append([keyword, keyword_index])
    if channel_103_list:
        print("2", channel_103_list)
        return channel_103_list

    reg_standard = "(?P<value>" + channel_103_3 + ")"
    match = re.finditer(reg_standard, _str)
    for m in match:
        m_dict = m.groupdict()
        m_span = m.span()
        keyword_index = [m_span[0], m_span[1]]
        keyword = m_dict.get('value')
        if re.search(channel_103_after, _str[keyword_index[1]:keyword_index[1]+50]):
            channel_103_list.append([keyword, keyword_index])
    if channel_103_list:
        print("3", channel_103_list)
        return channel_103_list

    reg_standard = "(?P<value>" + channel_103_4 + ")"
    match = re.finditer(reg_standard, _str)
    for m in match:
        m_dict = m.groupdict()
        m_span = m.span()
        keyword_index = [m_span[0], m_span[1]]
        keyword = m_dict.get('value')
        channel_103_list.append([keyword, keyword_index])
    if channel_103_list:
        print("4", channel_103_list)
        return channel_103_list

    reg_standard = "(?P<value>" + channel_103_5 + ")"
    match = re.finditer(reg_standard, _str)
    for m in match:
        m_dict = m.groupdict()
        m_span = m.span()
        keyword_index = [m_span[0], m_span[1]]
        keyword = m_dict.get('value')
        channel_103_list.append([keyword, keyword_index])
    if channel_103_list:
        print("5", channel_103_list)
        return channel_103_list

    return channel_103_list


def re_not_channel_103(_str):
    match = re.findall(not_channel_103, _str)
    if match:
        for word in match:
            instead = "#" * len(word)
            _str = re.sub(word, instead, _str)
    return _str


def re_channel_103(text):
    # 替换易混淆词
    clean_text = re_not_channel_103(text)

    # 查找符合标准形式的
    channel_103_list = re_standard_channel_103(clean_text)
    return channel_103_list


def extract_channel_103(text):
    result_list = []
    channel_103_list = re_channel_103(text)
    if channel_103_list:
        for word, text_index in channel_103_list:
            if word is not None:
                if text_index[1]-text_index[0] != len(word) \
                        or text_index[1]-text_index[0] >= 20:
                    return []
                d = {"body": word, "begin_index": text_index[0], "end_index": text_index[1]}
                result_list.append(d)
    return result_list


def test_csv(_path):
    df = pd.read_csv(_path)

    predict_list = []
    for index, row in df.iterrows():
        word_list = re_channel_103(row["doctextcon"], "")
        if word_list:
            predict = word_list
        else:
            predict = []
        print("predict", predict)
        predict_list.append(str(predict))

    predict_df = pd.DataFrame(predict_list)
    df = pd.concat([df, predict_df], axis=1)

    df.to_csv(_path)
    print("finish write!")


def test_str():
    s = '政府采购项目招标方式：公开招标，联系人：黎明。代理机构地址：广州市天河区'
    s = '''
    (第1次澄清） 发布时间：2020-11-25 致各招标文件持有者： 招标人──舟山市
    '''
    print(extract_channel_103(s))


def test_html():
    html_path = "C:/Users/Administrator/Desktop/3.html"

    with open(html_path, "r") as f:
        s = f.read()

    print(extract_channel_103(s, title=""))


if __name__ == "__main__":
    path = "D:\\BIDI_DOC\\比地_文档\\澄清答疑_result.csv"
    # test_csv(path)
    test_str()
    # test_html(path)
    pass