BIDI_EXTRACTION_AI/BiddingKG/dl/predictors/prem.py

# -*- coding: utf-8 -*-
"""``PREMPredict`` / ``EPCPredict`` — 角色金额模型与联系人模型。

Phase 5 从 ``interface/predictor.py`` 迁出：
    - ``PREMPredict``（约 795-1229 行）
    - ``EPCPredict``（约 1230-1577 行）

原 ``from common.Utils import *`` / ``from interface.modelFactory import *``
已替换为显式 import。两个类均无 ``os.path.dirname(__file__)`` 实际路径引用
（仅注释中出现，保持原样未改）。
"""
from __future__ import absolute_import

import re

import numpy as np

from BiddingKG.dl.common.context_utils import spanWindow
from BiddingKG.dl.interface.Entitys import Entity
from BiddingKG.dl.interface.modelFactory import (
    Model_role_classify_word,
    Model_money_classify,
    Model_person_classify,
)
from BiddingKG.dl.predictors.role_context import build_role_contexts
from BiddingKG.dl.predictors.role_rule_engine import RoleRuleEngine
from BiddingKG.dl.services.external_inference.paieas import (
    USE_PAI_EAS,
    tf_predict_pb2,
    vpc_requests,
    role_url,
    role_authorization,
    money_url,
    money_authorization,
    person_url,
    person_authorization,
)

__all__ = ["PREMPredict", "EPCPredict"]


#角色金额模型        
class PREMPredict():

    
    def __init__(self,config=None):
        #self.model_role_file = os.path.abspath("../role/models/model_role.model.hdf5")
        # self.model_role_file = os.path.dirname(__file__)+"/../role/log/new_biLSTM-ep012-loss0.028-val_loss0.040-f10.954.h5"
        self.model_role = Model_role_classify_word(config=config)
        self.model_money = Model_money_classify(config=config)
        # Phase C：模型后修正规则外置到 dl/rules/patterns/role_context_fix.yaml，
        # 由 RoleRuleEngine 按 stage 执行（行为等价原内联 if/elif 链，Phase D 双跑验证）
        self.rule_engine = RoleRuleEngine()
        # self.role_file = open('/data/python/lsm/role_model_predict.txt', 'a', encoding='utf-8')
        # self.money_file = open('/data/python/lsm/money_model_predict.txt', 'a', encoding='utf-8')

        return
    
    def search_role_data(self,list_sentences,list_entitys,contexts=None):
        '''
        @summary:根据句子list和实体list查询角色模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
            contexts:Phase A 预计算上下文（build_role_contexts 输出），
                     None 时内部构建（角色/金额模型共享一次实体-句子配对）
        @return:角色模型的输入数据
        '''
        if contexts is None:
            contexts = build_role_contexts(list_sentences, list_entitys)
        text_list = []
        data_x = []
        points_entitys = []
        for ctx_list in contexts:
            for ctx in ctx_list:
                entity = ctx.entity
                if entity.entity_type in ['org','company']:
                    text_list.append(ctx.role_model_text)
                    # item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_ROLE_INPUT_SHAPE[1]),shape=settings.MODEL_ROLE_INPUT_SHAPE)
                    # item_x = self.model_role.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,entity_text=entity.entity_text)
                    item_x = self.model_role.encode_word(sentence_text=ctx.sentence_text, begin_index=entity.wordOffset_begin, end_index=entity.wordOffset_end, size=30)
                    data_x.append(item_x)
                    points_entitys.append(entity)

        if len(points_entitys)==0:
            return None

        return [data_x,points_entitys, text_list]
    
    
    def search_money_data(self,list_sentences,list_entitys,contexts=None):
        '''
        @summary:根据句子list和实体list查询金额模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
            contexts:Phase A 预计算上下文（build_role_contexts 输出），
                     None 时内部构建（角色/金额模型共享一次实体-句子配对）
        @return:金额模型的输入数据
        '''
        if contexts is None:
            contexts = build_role_contexts(list_sentences, list_entitys)
        text_list = []
        data_x = []
        points_entitys = []
        for ctx_list in contexts:
            for ctx in ctx_list:
                entity = ctx.entity
                if entity.entity_type=="money":
                    text_list.append(ctx.money_model_text)
                    #item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_MONEY_INPUT_SHAPE[1]),shape=settings.MODEL_MONEY_INPUT_SHAPE)
                    #item_x = embedding_word(spanWindow(tokens=sentence.tokens, begin_index=entity.begin_index, end_index=entity.end_index, size=10, center_include=True, word_flag=True),shape=settings.MODEL_MONEY_INPUT_SHAPE)
                    item_x = self.model_money.encode(tokens=ctx.sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index)
                    data_x.append(item_x)
                    points_entitys.append(entity)

        if len(points_entitys)==0:
            return None

        return [data_x,points_entitys, text_list]
    
    def predict_role(self,list_sentences, list_entitys, contexts=None):
        '''
        @param contexts: Phase A 预计算上下文（build_role_contexts 输出），
                        None 时内部构建；与 predict_money 共用同一份避免重复配对。
        '''
        datas = self.search_role_data(list_sentences, list_entitys, contexts=contexts)

        if datas is None:
            return
        points_entitys = datas[1]
        text_list = datas[2]


        if USE_PAI_EAS:
            _data = datas[0]
            _data = np.transpose(np.array(_data),(1,0,2))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input2"].array_shape.dim.extend(np.shape(_data[2]))
            request.inputs["input2"].float_val.extend(np.array(_data[2],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(role_url, role_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_role.predict(datas[0])
        else:
            predict_y = self.model_role.predict(np.array(datas[0],dtype=np.float64))
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = predict_y[i]
            # text = text_list[i]
            text_tup = text_list[i]
            front, middle, behind = text_tup
            # Phase C：内联修正规则外置到
            # dl/rules/patterns/role_context_fix.yaml（stage: global_pre/seq_check/
            # l0/l2/l5_win_yes/notify/l1/l34/l5），由 RoleRuleEngine 执行，
            # 行为等价原 if/elif 链（Phase D badcase 双跑回归验证）。
            label = self.rule_engine.correct_role(entity, label, values, front, middle, behind)
            entity.set_Role(label, values)

    def predict_money(self,list_sentences,list_entitys,contexts=None):
        '''
        @param contexts: Phase A 预计算上下文（build_role_contexts 输出），
                        None 时内部构建；与 predict_role 共用同一份避免重复配对。
        '''
        datas = self.search_money_data(list_sentences, list_entitys, contexts=contexts)
        if datas is None:
            return
        points_entitys = datas[1]
        _data = datas[0]
        text_list = datas[2]
        if USE_PAI_EAS:
            _data = np.transpose(np.array(_data),(1,0,2,3))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request.inputs["input2"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input2"].array_shape.dim.extend(np.shape(_data[2]))
            request.inputs["input2"].float_val.extend(np.array(_data[2],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(money_url, money_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_money.predict(_data)
        else:
            predict_y = self.model_money.predict(_data)
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            values = predict_y[i]
            # text = text_list[i]
            text_tup = text_list[i]
            front, middle, behind = text_tup
            # Phase C：内联修正规则外置到
            # dl/rules/patterns/role_money_fix.yaml（stage: m1/m0/m_bid），
            # 由 RoleRuleEngine 执行，行为等价原 if/elif 链
            # （Phase D badcase 双跑回归验证）。
            label = self.rule_engine.correct_money(entity, label, values, front, middle, behind)
            entity.set_Money(label, values)

    def correct_money_by_rule(self, title, list_entitys, list_articles):
        # Phase C：标题/正文类别批量修正外置到
        # dl/rules/patterns/role_money_fix.yaml（stage: doc_title），
        # 由 RoleRuleEngine 执行，行为等价原实现（Phase D 双跑回归验证）。
        self.rule_engine.correct_money_by_doc(title, list_articles[0].content, list_entitys)

    def predict(self,list_sentences,list_entitys,contexts=None):
        '''
        @param contexts: Phase A 预计算上下文（build_role_contexts 输出），
                        None 时内部构建一次，role/money 两个模型共享，
                        消除原来各自的实体-句子配对与切片构建。
        '''
        if contexts is None:
            contexts = build_role_contexts(list_sentences, list_entitys)
        self.predict_role(list_sentences,list_entitys,contexts=contexts)
        self.predict_money(list_sentences,list_entitys,contexts=contexts)
        

#联系人模型    
class EPCPredict():
    
    def __init__(self,config=None):
        self.model_person = Model_person_classify(config=config)


    
    def search_person_data(self,list_sentences,list_entitys):
        '''
        @summary:根据句子list和实体list查询联系人模型的输入数据
        @param:
            list_sentences:文章的sentences
            list_entitys:文章的entitys
        @return:联系人模型的输入数据
        '''

        data_x = []
        points_entitys = []
        pre_texts = []
        for list_entity,list_sentence in zip(list_entitys,list_sentences):
            
            p_entitys = 0
            dict_index_sentence = {}
            for _sentence in list_sentence:
                dict_index_sentence[_sentence.sentence_index] = _sentence

            _list_entity = [entity for entity in list_entity if entity.entity_type=="person"]
            while(p_entitys<len(_list_entity)):
                entity = _list_entity[p_entitys]
                if entity.entity_type=="person":
                    sentence = dict_index_sentence[entity.sentence_index]

                    item_x = self.model_person.encode(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index)
                    data_x.append(item_x)
                    points_entitys.append(entity)
                    pre_texts.append(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=20))

                p_entitys += 1

        if len(points_entitys)==0:
            return None
        
        # return [data_x,points_entitys,dianhua]
        return [data_x,points_entitys, pre_texts]

    def predict_person(self,list_sentences, list_entitys):
        datas = self.search_person_data(list_sentences, list_entitys)
        if datas is None:
            return
        points_entitys = datas[1]
        pre_texts = datas[2]
        # phone = datas[2]
        if USE_PAI_EAS:
            _data = datas[0]
            _data = np.transpose(np.array(_data),(1,0,2,3))
            request = tf_predict_pb2.PredictRequest()
            request.inputs["input0"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input0"].array_shape.dim.extend(np.shape(_data[0]))
            request.inputs["input0"].float_val.extend(np.array(_data[0],dtype=np.float64).reshape(-1))
            request.inputs["input1"].dtype = tf_predict_pb2.DT_FLOAT
            request.inputs["input1"].array_shape.dim.extend(np.shape(_data[1]))
            request.inputs["input1"].float_val.extend(np.array(_data[1],dtype=np.float64).reshape(-1))
            request_data = request.SerializeToString()
            list_outputs = ["outputs"]
            _result = vpc_requests(person_url, person_authorization, request_data, list_outputs)
            if _result is not None:
                predict_y = _result["outputs"]
            else:
                predict_y = self.model_person.predict(datas[0])
        else:
            predict_y = self.model_person.predict(datas[0])
        # assert len(predict_y)==len(points_entitys)==len(phone)
        assert len(predict_y)==len(points_entitys)
        for i in range(len(predict_y)):
            entity = points_entitys[i]
            label = np.argmax(predict_y[i])
            pre_text = ''.join(pre_texts[i][0])
            # print('pre_text', pre_text)
            if label==0 and re.search('(谈判|磋商|询价|资格审查|评审专家|(评选|议标|评标|评审)委员会?|专家|评委)(小?组|小?组成员)?(成员|名单)[：，](\w{2,4}(（组长）|（成员）)?[、，,])*$', pre_text):
                # print(entity.entity_text, re.search('(谈判|磋商|询价|资格审查|评审专家|(评选|议标|评标|评审)委员会?|专家|评委)(小?组|小?组成员)?(成员|名单)[：，](\w{2,4}(（组长）|（成员）)?[、，,])*$', pre_text).group(0))
                label = 4
            values = []
            for item in predict_y[i]:
                values.append(item)
            # phone_number = phone[i]
            # entity.set_Person(label,values,phone_number)
            entity.set_Person(label,values,[])
        # 为联系人匹配电话
        # self.person_search_phone(list_sentences, list_entitys)

    def person_search_phone(self,list_sentences, list_entitys):
        def phoneFromList(phones):
            # for phone in phones:
            #     if len(phone)==11:
            #         return re.sub('电话[：|:]|联系方式[：|:]','',phone)
            return re.sub('电话[：|:]|联系方式[：|:]', '', phones[0])

        for list_entity, list_sentence in zip(list_entitys, list_sentences):
            # p_entitys = 0
            # p_sentences = 0
            #
            # key_word = re.compile('电话[：|:].{0,4}\d{7,12}|联系方式[：|:].{0,4}\d{7,12}')
            # # phone = re.compile('1[3|4|5|7|8][0-9][-—－]?\d{4}[-—－]?\d{4}|\d{3,4}[-—]\d{7,8}/\d{3,8}|\d{3,4}[-—]\d{7,8}转\d{1,4}|\d{3,4}[-—]\d{7,8}|[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}')  # 联系电话
            # # 2020/11/25 增加发现的号码段
            # phone = re.compile('1[3|4|5|6|7|8|9][0-9][-—－]?\d{4}[-—－]?\d{4}|'
            #                    '\d{3,4}[-—][1-9]\d{6,7}/\d{3,8}|'
            #                    '\d{3,4}[-—]\d{7,8}转\d{1,4}|'
            #                    '\d{3,4}[-—]?[1-9]\d{6,7}|'
            #                    '[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}|'
            #                    '[1-9]\d{6,7}')  # 联系电话
            # dict_index_sentence = {}
            # for _sentence in list_sentence:
            #     dict_index_sentence[_sentence.sentence_index] = _sentence
            #
            # dict_context_itemx = {}
            # last_person = "####****++++$$^"
            # last_person_phone = "####****++++$^"
            # _list_entity = [entity for entity in list_entity if entity.entity_type == "person"]
            # while (p_entitys < len(_list_entity)):
            #     entity = _list_entity[p_entitys]
            #     if entity.entity_type == "person" and entity.label in [1,2,3]:
            #         sentence = dict_index_sentence[entity.sentence_index]
            #         # item_x = embedding(spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=settings.MODEL_PERSON_INPUT_SHAPE[1]),shape=settings.MODEL_PERSON_INPUT_SHAPE)
            #
            #         # s = spanWindow(tokens=sentence.tokens,begin_index=entity.begin_index,end_index=entity.end_index,size=20)
            #
            #         # 2021/5/8 取上下文的句子，解决表格处理的分句问题
            #         left_sentence = dict_index_sentence.get(entity.sentence_index - 1)
            #         left_sentence_tokens = left_sentence.tokens if left_sentence else []
            #         right_sentence = dict_index_sentence.get(entity.sentence_index + 1)
            #         right_sentence_tokens = right_sentence.tokens if right_sentence else []
            #         entity_beginIndex = entity.begin_index + len(left_sentence_tokens)
            #         entity_endIndex = entity.end_index + len(left_sentence_tokens)
            #         context_sentences_tokens = left_sentence_tokens + sentence.tokens + right_sentence_tokens
            #         s = spanWindow(tokens=context_sentences_tokens, begin_index=entity_beginIndex,
            #                        end_index=entity_endIndex, size=20)
            #
            #         _key = "".join(["".join(x) for x in s])
            #         if _key in dict_context_itemx:
            #             _dianhua = dict_context_itemx[_key][0]
            #         else:
            #             s1 = ''.join(s[1])
            #             # s1 = re.sub('，）', '-', s1)
            #             s1 = re.sub('\s', '', s1)
            #             have_key = re.findall(key_word, s1)
            #             have_phone = re.findall(phone, s1)
            #             s0 = ''.join(s[0])
            #             # s0 = re.sub('，）', '-', s0)
            #             s0 = re.sub('\s', '', s0)
            #             have_key2 = re.findall(key_word, s0)
            #             have_phone2 = re.findall(phone, s0)
            #
            #             s3 = ''.join(s[1])
            #             # s0 = re.sub('，）', '-', s0)
            #             s3 = re.sub(',|，|\s', '', s3)
            #             have_key3 = re.findall(key_word, s3)
            #             have_phone3 = re.findall(phone, s3)
            #
            #             s4 = ''.join(s[0])
            #             # s0 = re.sub('，）', '-', s0)
            #             s4 = re.sub(',|，|\s', '', s0)
            #             have_key4 = re.findall(key_word, s4)
            #             have_phone4 = re.findall(phone, s4)
            #
            #             _dianhua = ""
            #             if have_phone:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s1.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone) > 1:
            #                         _dianhua = phoneFromList(have_phone[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_phone)
            #             elif have_key:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s1.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key) > 1:
            #                         _dianhua = phoneFromList(have_key[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_key)
            #             elif have_phone2:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s0.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone2) > 1:
            #                         _dianhua = phoneFromList(have_phone2[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_phone2)
            #             elif have_key2:
            #                 if entity.entity_text != last_person and s0.find(last_person) != -1 and s0.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key2) > 1:
            #                         _dianhua = phoneFromList(have_key2[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_key2)
            #             elif have_phone3:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s3.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone3) > 1:
            #                         _dianhua = phoneFromList(have_phone3[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_phone3)
            #             elif have_key3:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s3.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key3) > 1:
            #                         _dianhua = phoneFromList(have_key3[1:])
            #                 else:
            #                     _dianhua = phoneFromList(have_key3)
            #             elif have_phone4:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s4.find(
            #                         last_person_phone) != -1:
            #                     if len(have_phone4) > 1:
            #                         _dianhua = phoneFromList(have_phone4)
            #                 else:
            #                     _dianhua = phoneFromList(have_phone4)
            #             elif have_key4:
            #                 if entity.entity_text != last_person and s4.find(last_person) != -1 and s4.find(
            #                         last_person_phone) != -1:
            #                     if len(have_key4) > 1:
            #                         _dianhua = phoneFromList(have_key4)
            #                 else:
            #                     _dianhua = phoneFromList(have_key4)
            #             else:
            #                 _dianhua = ""
            #             # dict_context_itemx[_key] = [item_x, _dianhua]
            #             dict_context_itemx[_key] = [_dianhua]
            #         # points_entitys.append(entity)
            #         # dianhua.append(_dianhua)
            #         last_person = entity.entity_text
            #         if _dianhua:
            #             # 更新联系人entity联系方式（person_phone）
            #             entity.person_phone = _dianhua
            #             last_person_phone = _dianhua
            #         else:
            #             last_person_phone = "####****++++$^"
            #     p_entitys += 1

            from scipy.optimize import linear_sum_assignment
            from BiddingKG.dl.interface.Entitys import Match
            def dispatch(match_list):
                main_roles = list(set([match.main_role for match in match_list]))
                attributes = list(set([match.attribute for match in match_list]))

                label = np.zeros(shape=(len(main_roles), len(attributes)))
                for match in match_list:
                    main_role = match.main_role
                    attribute = match.attribute
                    value = match.value
                    label[main_roles.index(main_role), attributes.index(attribute)] = value + 10000
                # print(label)
                gragh = -label
                # km算法
                row, col = linear_sum_assignment(gragh)
                max_dispatch = [(i, j) for i, j, value in zip(row, col, gragh[row, col]) if value]
                return [Match(main_roles[row], attributes[col]) for row, col in max_dispatch]
            # km算法
            key_word = re.compile('((?:电话|联系方式|联系人).{0,4}?)(\d{7,12})')
            phone = re.compile('1[3|4|5|6|7|8|9][0-9][-—－―]?\d{4}[-—－―]?\d{4}|'
                               '\+86.?1[3|4|5|6|7|8|9]\d{9}|'
                               '0\d{2,3}[-—－―][1-9]\d{6,7}/[1-9]\d{6,10}|'
                               '0\d{2,3}[-—－―]\d{7,8}转\d{1,4}|'
                               '0\d{2,3}[-—－―]?[1-9]\d{6,7}|'
                               '[\（|\(]0\d{2,3}[\）|\)]-?\d{7,8}-?\d{,4}|'
                               '[1-9]\d{6,7}')
            phone_entitys = []
            for _sentence in list_sentence:
                sentence_text = _sentence.sentence_text
                res_set = set()
                for i in re.finditer(phone,sentence_text):
                    res_set.add((i.group(),i.start(),i.end()))
                for i in re.finditer(key_word,sentence_text):
                    res_set.add((i.group(2),i.start()+len(i.group(1)),i.end()))
                for item in list(res_set):
                    phone_left = sentence_text[max(0,item[1]-10):item[1]]
                    phone_right = sentence_text[item[2]:item[2]+8]
                    # 排除传真号 和 其它错误项
                    if re.search("传，?真|信，?箱|邮，?箱",phone_left):
                        if not re.search("电，?话",phone_left):
                            continue
                    if re.search("帐，?号|编，?号|报，?价|证，?号|价，?格|[\(（]万?元[\)）]",phone_left):
                        continue
                    if re.search("[.,]\d{2,}",phone_right):
                        continue
                    _entity = Entity(_sentence.doc_id, None, item[0], "phone", _sentence.sentence_index, None, None,item[1], item[2],in_attachment=_sentence.in_attachment)
                    phone_entitys.append(_entity)
            person_entitys = []
            for entity in list_entity:
                if entity.entity_type == "person":
                    entity.person_phone = ""
                    person_entitys.append(entity)
            _list_entity = phone_entitys + person_entitys
            _list_entity = sorted(_list_entity,key=lambda x:(x.sentence_index,x.wordOffset_begin))

            words_num_dict = dict()
            last_words_num = 0
            list_sentence = sorted(list_sentence, key=lambda x: x.sentence_index)
            for sentence in list_sentence:
                _index = sentence.sentence_index
                if _index == 0:
                    words_num_dict[_index] = 0
                else:
                    words_num_dict[_index] = words_num_dict[_index - 1] + last_words_num
                last_words_num = len(sentence.sentence_text)
            match_list = []
            for index in range(len(_list_entity)):
                entity = _list_entity[index]
                if entity.entity_type=="person" and entity.label in [1,2,3]:
                    match_nums = 0
                    for after_index in range(index + 1, min(len(_list_entity), index + 5)):
                        after_entity = _list_entity[after_index]
                        if after_entity.entity_type=="phone":
                            sentence_distance = after_entity.sentence_index - entity.sentence_index
                            distance = (words_num_dict[after_entity.sentence_index] + after_entity.wordOffset_begin) - (
                                    words_num_dict[entity.sentence_index] + entity.wordOffset_end)
                            if sentence_distance < 2 and distance < 50:
                                value = (-1 / 2 * (distance ** 2)) / 10000
                                match_list.append(Match(entity, after_entity, value))
                                match_nums += 1
                            else:
                                break
                        if after_entity.entity_type=="person":
                            if after_entity.label not in [1,2,3]:
                                break
                    if not match_nums:
                        for previous_index in range(index-1, max(0,index-5), -1):
                            previous_entity = _list_entity[previous_index]
                            if previous_entity.entity_type == "phone":
                                sentence_distance = entity.sentence_index - previous_entity.sentence_index
                                distance = (words_num_dict[entity.sentence_index] + entity.wordOffset_begin) - (
                                        words_num_dict[previous_entity.sentence_index] + previous_entity.wordOffset_end)
                                if sentence_distance < 1 and distance<30:
                                    # 前向 没有 /10000
                                    value = (-1 / 2 * (distance ** 2))
                                    match_list.append(Match(entity, previous_entity, value))
                                else:
                                    break

            result = dispatch(match_list)
            for match in result:
                entity = match.main_role
                # 更新 list_entity
                entity_index = list_entity.index(entity)
                list_entity[entity_index].person_phone = match.attribute.entity_text


    def predict(self,list_sentences,list_entitys):
        self.predict_person(list_sentences,list_entitys)