How to use equal_list method in lisa

Best Python code snippet using lisa_python

domain_classify.py

Source:domain_classify.py Github

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1# -*- coding: utf-8 -*-23import os4import re5import scws6import sys7import csv8import opencc9from global_utils_do import *1011sys.path.append('../​../​trans')12from trans import trans, traditional2simplified1314# cc = opencc.OpenCC('t2s', opencc_path='/​usr/​bin/​opencc')15s = load_scws()1617def classify_by_biostring(bio_string):#根据用户bio_string划分1819 # bio_string_s = cc.convert(bio_string.decode('utf-8'))20 bio_string_s = bio_string.decode('utf-8')21 2223 kwdlist = bio_string_s.encode('utf-8')#cut(s, bio_string_s.encode('utf-8'))24 lawyerw_weight = sum([1 for keyword in lawyerw if keyword in kwdlist]) # 律师25 adminw_weight = sum([1 for keyword in adminw if keyword in kwdlist]) # 组织26 mediaw_weight = sum([1 for keyword in mediaw if keyword in kwdlist]) # 媒体27 businessw_weight = sum([1 for keyword in businessw if keyword in kwdlist]) # 商业人士28 govw_weight = sum([1 for keyword in govw if keyword in kwdlist]) # 政府官员29 mediaworkerw_weight = sum([1 for keyword in mediaworkerw if keyword in kwdlist]) # 媒体人士30 universityw_weight = sum([1 for keyword in universityw if keyword in kwdlist]) # 高校3132 max_weight = 033 label = 'other'34 #equal_list = []35 36 if max_weight < businessw_weight:37 max_weight = businessw_weight38 label = 'business'39## equal_list = ['business']40## else:41## pass4243 if max_weight < adminw_weight:44 max_weight = adminw_weight45 label = 'admin'46## equal_list = ['admin']47## elif max_weight == adminw_weight:#如果相等48## equal_list.append('admin')49## else:50## pass5152 if max_weight < mediaw_weight:53 max_weight = mediaw_weight54 label = 'media'55## equal_list = ['media']56## elif max_weight == mediaw_weight:#如果相等57## equal_list.append('media')58## else:59## pass6061 if max_weight < lawyerw_weight:62 max_weight = lawyerw_weight63 label = 'lawyer'64## equal_list = ['lawyer']65## elif max_weight == lawyerw_weight:#如果相等66## equal_list.append('lawyer')67## else:68## pass6970 if max_weight < govw_weight:71 max_weight = govw_weight72 gov = 'politician'73## equal_list = ['politician']74## elif max_weight == govw_weight:#如果相等75## equal_list.append('politician')76## else:77## pass7879 if max_weight < mediaworkerw_weight:80 max_weight = mediaworkerw_weight81 label = 'mediaworker'82## equal_list = ['mediaworker']83## elif max_weight == mediaworkerw_weight:#如果相等84## equal_list.append('mediaworker')85## else:86## pass8788 if max_weight < universityw_weight:89 max_weight = universityw_weight90 label = 'university'91## equal_list = ['university']92## elif max_weight == universityw_weight:#如果相等93## equal_list.append('university')94## else:95## pass9697## if len(equal_list) > 2:98## label = 'other'99## elif len(equal_list) == 2:100## l1 = equal_list[0]101## l2 = equal_list[1]102## if DICT_LENGTH[l1] > DICT_LENGTH[l2]:103## label = l2104## elif DICT_LENGTH[l1] < DICT_LENGTH[l2]:105## label = l1106## else:107## pass108## else:109## pass110111 return label112113def classify_inner_outer(location):114115 # bio_string_s = traditional2simplified(location.decode('utf-8'))116 bio_string_s = location.decode('utf-8')117 new_location = bio_string_s.encode('utf-8')118 flag = 0119 for city in inner_city:120 if city in new_location:121 flag = 1122 break123124 return flag125126def domain_main(user_data):#twitter用户身份分类主函数127 '''128 输入数据：129 user_data用户数据字典：{'uid':{'description':description,'username':username,'location':location,'number_of_text':number of text}...}130 description:twitter用户背景信息中的description。注意：有部分内容是英文，需要转换成中文131 username:twitter用户背景信息中的username132 location:twitter用户背景信息中的location。注意：有部分内容是英文，需要转换成中文133 number_of_text:用户最近7天发帖数量134135 输出数据：136 user_label用户身份字典:{'uid':label,'uid':label...}137 '''138 if len(user_data) == 0:139 return {}140141 user_label = dict()142 for k,v in user_data.iteritems():143 label = 'other'144 try:145 description = v['description']146 except KeyError:147 description = ''148 try:149 username = v['username']150 except KeyError:151 username = ''152 try:153 location = v['location']154 except KeyError:155 location = '' 156 try:157 number_of_text = v['number_of_text']158 except KeyError:159 number_of_text = 0160 161 bio_string = username + '_' + description162 #根据bio_string划分163 if len(bio_string) > 1:164 label = classify_by_biostring(bio_string)165166 if label == 'admin':#组织167 if location:168 flag = classify_inner_outer(location)169 if flag == 1:#境内170 label = 'inner_admin'171 else:172 label = 'outer_admin'173 else:174 label = 'admin'175 elif label == 'media':#媒体176 if location:177 flag = classify_inner_outer(location)178 if flag == 1:#境内179 label = 'inner_media'180 else:181 label = 'outer_media'182 else:183 label = 'media'184 else:185 pass186 187 if label != 'other':188 user_label[k] = label189 continue190191 #根据发帖数量判定192 if number_of_text >= ACTIVE_COUNT:193 label = 'active'194 user_label[k] = label195196 return user_label197 ...

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