def catch_trick88():
from htmlconvert2text import convert2txt
def read_train_res():
with open('/home/mm/Documents/aliyun-FDDC-2018-Financial-Challenge-/chongzu.train') as rf:
train_res = rf.read()
return train_res
train_re = read_train_res()
for i in os.listdir('/home/mm/FDDC_datasets_dir/FDDC_announcements_round2_train_html/')[
0:2000:20]:
# sss = convert2txt('/home/html/' + i)
sss, ent_str = convert2txt("/home/mm/FDDC_datasets_dir/FDDC_announcements_round2_train_html/" + i)
row_train_re = re.search(r'{}[^\n。]+\n'.format(i.split(".")[0]), train_re).group()[:-1]
print("###########################################################{}".format(i))
for index, res_enti in enumerate(row_train_re.split('\t')):
if len(res_enti) > 1:
print("@@@this is the {}th key_value{}".format(index, res_enti))
res_find = re.findall(r'{}'.format(res_enti), ent_str)
if len(res_find) > 0:
print(res_find)
else:
print("@@@")
def findall_reg():
for i in os.listdir('/home/html/')[100:2770:50]:
for l in trick_precedences:
sss = convert2txt('/home/html/'+i)
reg_out=re.findall(r'{}[^。|]*[。；|]'.format(l), sss, flags=re.X)
reg_out_final=[]
list_false_true = []
for j in reg_out:
list_false_true = [True if k in j else False for k in list_keywords]
if True not in list_false_true:
reg_out_final.append(j)
print(i)
for i in reg_out_final:
print(i)
print('\n\n')
def catch_trick888():
from htmlconvert2text import convert2txt
# def read_train_res():
# with open('/home/mm/Documents/aliyun-FDDC-2018-Financial-Challenge-/chongzu.train') as rf:
# train_res = rf.read()
# return train_res
#
# train_re = read_train_res()
for i in os.listdir('/home/mm/FDDC_datasets_dir/FDDC_announcements_round2_train_html/')[
0:2688:18]:
# sss = convert2txt('/home/html/' + i)
sss, ent_str = convert2txt("/home/mm/FDDC_datasets_dir/FDDC_announcements_round2_train_html/" + i)
if len(ent_str) > 10:
with open('checkregexentity.txt' , 'a') as af:
af.write(ent_str + "\n")
print("OOOOOOOOOOO")
else:
print(i)
def tokenize_enti(self,path11):
texx, entity_string = convert2txt(path11)
sentences = re.split(r'。', texx)
# sentences.sort(key=len, reverse=True)
entities = list(set(re.split(r'[\s~、，；/]', entity_string)))
entities.sort(key=len)
entities_arrows_list =list(set([ x if '~' in x else '' for x in re.split(r'\s', entity_string)]))
entities_arrows_list.sort(key=len, reverse=True)
entities_arrows_list = entities_arrows_list[:-1]
# 找出结果数据行并且把最后的回车符号去掉
patt_index = re.findall(r'\d{4,10}', path11)[0]
res_rows = re.findall(r'(?<=\n){}[^\n]+(?=\n)'.format(patt_index), self.train_res)
# 以下是整理train——res
# 遍历结果，发现有简称全称的，把匹配的另一半加进去。
"""主要目的是修正train——res文件，里面有简称或者全称，并不统一，为了让简称全称都出现，
使用正则提取对应的简称或全称，如果有顿号，把那些字串也分开提取，作为标注的标的，当然是先
把字符长度小的匹配出来，分词之后也是先把长度长的连起来。没问题的"""
res_paired = {} # 临时定义一个res的列表，存储修改后的train res
for x in range(len(res_rows)):
res_row = res_rows[x]
for y in range(6):
res_paired[str(x)+str(y)]= [re.split(r'\t', res_row)[y]]
for arrow_str in entities_arrows_list:
for index, result_row in enumerate(res_rows):
for indi, res_value in enumerate(re.split(r'\t', result_row)):
if indi in [0, 1, 4, 5]:
continue
res_value_list = res_value.split('、')
for res_value_split in res_value_list:
if res_value_split in entities and res_value_split in arrow_str:
# 找出配对的简称或者全称，添加,如果是股权/估值法/金额直接添加并且continue
niki, fullna = re.split(r'~', arrow_str)
fullna_first = fullna.split('，')[0]
niki_split_list = re.split(r'[/、]', niki)
# 对应的全称满足三个条件，长度/逗号 以及含有简称的几个字
if res_value_split in niki_split_list \
and len(fullna_first) < 18 \
and re.search(re.sub(r'(?<=[^屄\s])', '\s?', res_value_split), fullna_first):
res_paired[str(index)+str(indi)].append(fullna_first)
""" 由全称查简称时候要避免 公司/本公司/上市公司/发起人/申请人/,
含有这几个字的要剔除 """
if res_value_split == fullna_first:
# 对应的简称满足几个条件： 包含在全程里面，不长于4个字，不等于
for niki_split in niki_split_list:
if re.search(re.sub(r'(?<=[^屄\s])', '\s?', fullna_first), niki_split)\
and not re.search(r'(^公司$|^本公司$|环境$|^上市公司$|人$|资产|标的|交易|对方|发行|对象|股东|对手|单位)',re.sub(r'\s', '', niki_split)):
res_paired[str(index)+str(indi)].append(niki_split)
# 遍历公告的每一句，把每一句送进模型。
words_n_words = ''
for i in sentences:
words = self.segmentor.segment(i)
words = ' '.join(words)
words = words+' '+'。'+' ' # 加上句号以及句号后面的空格
# 分词要使用更好的策略,更长一些,避免太短的句子,重复循环浪费流程
# # 下面是把所有目标主体合并在一起, 把55%股权这样的先分出来,
# for ent in entities:
# # 把words中所有是实体的中间去掉空格。使用双层sub
# # 正则还是要多注释啊
# """ re.sub(r'(?<=\w)(?=\w)'','\s?',ent) 是把实体里面的每个字符中间插入“\s?”
# 表示匹配任何以此序列出现但中间可能有空格的情况,分词之后join成空格分割的。然后找出words
# 中出现这个序列的地方，将其换成没空格的"""
# if len(ent) > 1:
# if not re.search(r'([\d.]+%的?(?:股权|股份|权益))', ent): # 如果没有股权关键字，直接加上空格匹配pattern
# patt_ent = re.sub(r'(?<=\w)(?=\w)', r'\s?', ent)
# elif len(ent) > 7: # 如果有股权关键字，且长度比较长，就把前面主体提出来，单独分词
# patt_ent = re.sub(r'(?<=\w)(?=\w)',r'\s?', re.sub(r'的?[\d.]+%的?(股权|股份|权益)','', ent))
# else:
# patt_ent = re.sub(r'(?<=\w)(?=\w)', r'\s?', ent)
# # 下面一句把words中所有符合主体列表的项目，可能被分词分开的，重新合并起来，单独成行,在test时使用
# words = re.sub(r'{}'.format(patt_ent), '\s' + ent + '\s', words)
# 然后把空格都换成回车,words竖起来了。
# words = re.sub(r'\s', '\n', words)
# words = re.sub(r'\n+', '\n', words)
"""把words中所有是结果键值的，后缀上tab键和结果索引号。否则后缀tab键和字母o
目的是好的，就是让模型更容易找到目标，模型不需要判断开始和结束，
但是这样的正则太难了， 我无法将所有合适的实体
全部抽出来，而导致标注的缺失，那么还是把任务给模型了"""
# for x in range(len(res_rows)):
# for y in range(6):
# index = str(x)+str(y)
# tags_list = res_paired[index]
for index, tags_list in res_paired.items():
# 表中的小表，可能有一个或多个成员，遍历一下,包括顿号分割的那些都可以标出来了，不影响合并好的实体字符串。
for sub_res in sorted(tags_list, key=len, reverse=True):
if not index.endswith('0') and len(sub_res) > 1:
patt_sub_res = re.sub(r'(?<=[^屄\s])', '\s?', sub_res)
if re.search(r'{}'.format(patt_sub_res), words):
spliter = re.findall(patt_sub_res, words)[0]
words_split_list = re.split(spliter, words)
spliter_tagged = re.sub(r'\s', '屄{}'.format(index[1]), spliter)
words = spliter_tagged.join(words_split_list)
# print(words)
# words=re.sub(patt_sub_res, sub_res)
# words= re.sub(r'{}(?=\n)'.format(sub_res), '\n{}\t{}\n'.format(sub_res, index), words)
# train——result标注完了，现在标注o,就是把非数字结尾的行加上tab和o
words = re.sub(r'\s', '\to\n', words)
words = re.sub(r'(?<=屄\d)', '\n', words)
words = re.sub(r'屄', '\t' , words)
words_n_words += words
# print(words)
with open('/home/mm/FDDC_datasets_dir/tokenized_datasets_for_anago/chongzu/'+res_paired['00'][0]+'.txt', 'w') as af:
af.write(words_n_words)
print(path11.split("/")[-1])
recognizer.load(ner_model_path)
pos_model_path = os.path.join(LTP_DATA_DIR,
'pos.model') # 词性标注模型路径，模型名称为`pos.model`
cws_model_path = os.path.join(LTP_DATA_DIR, 'cws.model')
source_path = "/home/mm/Downloads/round1_train_20180518/dingzeng/html/"
out_path = "/home/mm/aliyunChallenge/"
listdir = os.listdir(source_path)
postagger = Postagger() # 初始化实例
postagger.load(pos_model_path) # 加载模型
segmentor = Segmentor() # 初始化实例
segmentor.load(cws_model_path) # 加载模型
for i in listdir[0:1]:
html_text = convert2txt(source_path + i)
words = segmentor.segment(html_text) # 分词
postags = postagger.postag(words) # 词性标注
netags = recognizer.recognize(words, postags) # 命名实体识别
indices = [i for i, x in enumerate(list(netags)) if x.endswith("Ni")]
temp_entity = ""
new_list = []
for i, x in enumerate(words):
if (i in indices) and ((i + 1) in indices) and (i - 1 not in indices):
temp_entity = x
elif (i - 1 in indices) and (i + 1 in indices) and (i in indices):
temp_entity += x
elif (i - 1 in indices) and (i in indices) and (i + 1 not in indices):
temp_entity += x
new_list.append(temp_entity)
def fill_table(path):
# if index not in random_index:
# continue
# list_true_res = re.findall(r'{}[^\n]+(?=\n)'.format(path.split(".")[0]), true_res_str)
# text, entity_string = convert2txt('/home/mm/FDDC_datasets_dir/FDDC_announcements_round2_train_html/762567.html')
text, entity_string = convert2txt(
'/home/mm/FDDC_datasets_dir/FDDC_announcements_round2_train_html/' +
path)
official_res_row = re.findall(
r'{path}[^\n]+\n'.format(path=path.split(".")[0]), true_res_str)
answer_dic = {"公告ID": path.split(".")[0]}
_, asset_string, eval_string, money_string = entity_string.split("|||")
entities_arrows_list = list(
set([x if 'H&#~' in x else ''
for x in re.split(r'\s', entity_string)]))
short_name_list = [re.split(r"H&#~", x)[0] for x in entities_arrows_list]
reg_short_listr = ""
for tiy in short_name_list:
for tiny in re.split(r'[，、/]', tiy):
if not re.search(
r'(^公司$|^本公司$|环境$|^上市公司$|人$|资产|标的|交易|审计|对方|发行|对象|股东|对手|单位|事务所|计划|分公司|日$|董事|独立|书$|承诺|机构|评估|交所|股|认购|局$|律|本次|国家|中央|中国|重组|重大|期$|^元$|^万元$|^亿元$|《|》|股份|股分|利润|报告)',
tiny):
reg_short_listr += tiny
reg_short_listr += "|"
reg_short_listr = re.sub(r'^\||\|$', "", reg_short_listr)
reg_short_listr = re.sub(r'\|\|', "|", reg_short_listr)
answer_dic["估值方法"] = collections.Counter(
eval_string.split(" ")).most_common(1)[0][0]
answer_dic["交易金额"] = collections.Counter(
money_string.split(" ")).most_common(1)[0][0]
answer_dic["交易标的"] = collections.Counter(
asset_string.split(" ")).most_common(1)[0][0]
answer_dic["标的公司"] = ''
answer_dic["交易对方"] = ''
for row in entities_arrows_list:
if len(row) < 2:
continue
short, long = row.split('H&#~')
list_splits_short = re.split(r'/|、', short)
for short_split in list_splits_short:
if re.match(r'交易对[手方]|发行对象|认购人', short_split):
answer_dic["交易对方"] = long
if re.match(r'标的公司|目标公司', short_split):
answer_dic["标的公司"] = long
if "、" in long:
"""ltp 识别实体， 针对顿号分开的pos，分别确认不含有动词副词，
然后在entity——string里面找相应的各自的股权信息/资产信息"""
asset_list = []
for long_split in long.split("、"):
# print("{} long_split is {}".format(path, long_split))
# asset_related_target = re.findall(r'{}[\d.%]+的?股[权分]|全部[股债分权]'.format(long_split), entity_string)
# if len(asset_related_target) > 0:
# asset_list.append(re.findall(r'[\d.%]+的?股[权分]|全部[股债分权]', asset_related_target[0])[0])
# else:
if re.findall(r'{ls}[\d.%]+的?[股债分权份]{{2}}|{ls}全部的?[股债分权份资产负利和与]{{2,6}}'.format(ls=long_split), text) \
and re.findall(r'[\d.%]+的?[股债分权份资产负利和与]{2， 5}|全部的?[股债分权份资产负利和与]{2,6}', \
re.findall(r'{ls}[\d.%]+的?股股债分权份资产负利和与]{{2,6}}|全部[股债分权份资产负利和与]{{2,6}}'.format(ls = long_split), text)[0]):
# asset_related_target = re.findall(r'{}[\d.%]+的?股[权分]|全部[股债分权]'.format(long_split), text)
asset_list.append(re.findall(r'[\d.%]+的?[股债分权份]{2}|全部的?[股债分权份资产负利和与]{2,6}',\
re.findall(r'{ls}[\d.%]+的?[股债分权份]{{2,4}}|全部的?[股债分权份资产负利和与]{{2,5}}'.format(ls =long_split), text)[0])[0])
answer_dic["交易标的"] = '|'.join(asset_list)
answer_dic["标的公司"] = "|".join(long.split("、"))
if re.match(r'本次交易|交易标的|标的资产|交易资产|目标资产|标的股权', short_split):
if re.findall(r'[\d.%]+的?[股债分权份]{2}|全部的?[股债分权份]{2}', long):
list_ass = re.findall(r'[\d.%]+的?[股债分权份]{2}|全部的?[股债分权份]{2}',
long)
# if len(list_ass) > 1:
answer_dic["交易标的"] = '|'.join(list_ass)
list_tar = re.findall(
r'({ls})(?=的?[\d.%]+的?[股债分权份]{{2,3}}|全部的?[股债分权份资产负利和与]{{2,6}})'
.format(ls=reg_short_listr), long)
answer_dic["标的公司"] = '|'.join(list_tar)
if answer_dic["标的公司"] == "":
guess_target = collections.Counter(
re.findall(r'{ls}'.format(ls=reg_short_listr),
entity_string)).most_common(8)
for tar in guess_target:
if len(tar[0]) > 2:
answer_dic["标的公司"] = guess_target
pass
# print(re.findall(r'(?<=[和及、，~的])[^\d和及、，~的股份分权]+(?=[\d.%的]+股[权分份])', entity_string))
if answer_dic["标的公司"] == "":
for post_fix in re.findall(
r'(?<=[和及、，~的])[^\d和及、，~的股份分权资产负利与]+(?=的?[\d.%]+的?[股债分权份]{{2,3}}|全部的?[股债分权份资产负利和与]{{2,6}})',
entity_string):
if len(post_fix) in [3, 4, 5, 6]:
answer_dic["标的公司"] = post_fix
if answer_dic["标的公司"] != "" and answer_dic["交易标的"] != "":
print("answer dict is ok ")
else:
print("f**k it {}".format(path))
# # 说明交易
#
# # if "|" in answer_dic['标的公司'] and "|" not in answer_dic['交易标的']:
# # answer_dic['交易标的'] == ""
# # for target_split in re.split(r'|', answer_dic['标的公司']):
# # answer_dic['交易标的'] += re.findall()
#
# if re.match(r'交易标的|标的资产|标的股权|目标资产', short_split):
# # if len(answer_dic['交易标的'])>1 and answer_dic['交易标的'] in long:
# # print("股权最频的信息就在交易标的名词解释中{}".format(path))
# if re.search(r'的?[\d.%全部]+股[权分份]]', long):
# answer_dic["标的公司"] ="|".join(re.split(r'的?[\d.%全部的]+股[权分份]、?', long))
# answer_dic["交易标的"] ="|".join(re.findall(r'[\d.%全部的]+股[权分份]]', long))
# # elif len(answer_dic['交易标的'])>1:
# # answer_dic['交易标的'] = re.findall(r'([\d.]+%的?(?:股权|股份|权益))', long)[0]
submit_string = ''
if '|' in str(answer_dic):
target_list = answer_dic["标的公司"].split("|")
asset_list = answer_dic["交易标的"].split("|")
rows_to_gen = max(len(target_list), len(asset_list))
for row in range(rows_to_gen):
index_target = row if len(
target_list) < row + 1 else len(target_list) - 1
index_asset = row if len(
asset_list) < row + 1 else len(asset_list) - 1
submit_string += answer_dic["公告ID"]+ "\t" + asset_list[index_asset] \
+ "\t" + target_list[index_target] + "\t" + answer_dic["交易对方"] \
+ "\t" + answer_dic["交易金额"] + "\t" + answer_dic["估值方法"] + "\n"
else:
submit_string = answer_dic["公告ID"]+ "\t" + answer_dic["交易标的"] \
+ "\t" + answer_dic["标的公司"] + "\t" + answer_dic["交易对方"] \
+ "\t" + answer_dic["交易金额"] + "\t" + answer_dic["估值方法"] + "\n"
return submit_string
recognizer.load(ner_model_path)
pos_model_path = os.path.join(LTP_DATA_DIR,
'pos.model') # 词性标注模型路径，模型名称为`pos.model`
cws_model_path = os.path.join(LTP_DATA_DIR, 'cws.model')
source_path = "/home/mm/FDDC_datasets_dir/FDDC_announcements_round2_train_html/"
out_path = "/home/mm/FDDC_datasets_text_dir/chongzu/"
listdir = os.listdir(source_path)
postagger = Postagger() # 初始化实例
postagger.load(pos_model_path) # 加载模型
segmentor = Segmentor() # 初始化实例
segmentor.load(cws_model_path) # 加载模型
for i in listdir[0:1]:
html_text, entity_string = convert2txt(source_path + i)
words = segmentor.segment(html_text) # 分词
postags = postagger.postag(words) # 词性标注
netags = recognizer.recognize(words, postags) # 命名实体识别
indices = [i for i, x in enumerate(list(netags)) if x.endswith("Ni")]
temp_entity = ""
new_list = []
"""以下是对字符串序列中含有实体名称的部分，重新结合在一起，去掉分词造成的间隔，然后在实体前后加缀一个特殊符号{NER#}"""
for i, x in enumerate(words):
if (i in indices) and ((i + 1) in indices) and (i - 1 not in indices):
temp_entity = x
elif (i - 1 in indices) and (i + 1 in indices) and (i in indices):
temp_entity += x
elif (i - 1 in indices) and (i in indices) and (i + 1 not in indices):
temp_entity += x
