config.py 44 KB

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  1. class CustConfig:
  2. FEATURE_COLUMNS = [
  3. "BB_RETAIL_CUSTOMER_CODE", # 零售户代码
  4. "BB_RTL_CUST_MARKET_TYPE_NAME", # 零售户市场类型名称
  5. "BB_RTL_CUST_BUSINESS_TYPE_NAME", # 零售客户业态名称
  6. "BB_RTL_CUST_CHAIN_FLAG", # 零售户连锁标识
  7. "MD04_MG_RTL_CUST_CREDITCLASS_NAME", # 零售户信用等级名称
  8. "MD04_DIR_SAL_STORE_FLAG", # 直营店标识
  9. "BB_CUSTOMER_MANAGER_SCOPE_NAME", # 零售户经营范围名称
  10. "BB_RTL_CUST_TERMINAL_LEVEL_NAME", # 零售户终端层级名称
  11. "OPERATOR_EDU", # 零售客户经营者文化程度
  12. "STORE_AREA", # 店铺经营面积
  13. "OPERATOR_AGE", # 经营者年龄
  14. "PRODUCT_INSALE_QTY", # 在销品规数
  15. ]
  16. ONEHOT_CAT = {
  17. "BB_RTL_CUST_MARKET_TYPE_NAME": ["城网", "农网"],
  18. "BB_RTL_CUST_BUSINESS_TYPE_NAME": ["便利店", "超市", "烟草专业店", "娱乐服务类", "其他"],
  19. "BB_RTL_CUST_CHAIN_FLAG": ["是", "否"],
  20. "MD04_MG_RTL_CUST_CREDITCLASS_NAME": ["AAA", "AA", "A", "B", "C", "D"],
  21. "MD04_DIR_SAL_STORE_FLAG": ["是", "否"],
  22. "BB_CUSTOMER_MANAGER_SCOPE_NAME": ["是", "否"],
  23. "BB_RTL_CUST_TERMINAL_LEVEL_NAME": ["普通终端", "一般现代终端", "合作终端", "加盟终端", "直营终端"],
  24. "OPERATOR_EDU": [1, 2, 3, 4, 5, 6, 7, "无数据"],
  25. "STORE_AREA": ["0-20", "21-50", "51-100", "101-150", "151-200", "201-300", "301-400", "401-600", "601-1000", "1001-2000", "2000以上"],
  26. "OPERATOR_AGE": ["19-30", "31-40", "41-50", "51-65", "66-80", "80以上"],
  27. "PRODUCT_INSALE_QTY": ["0-10", "11-20", "21-30", "31-40", "41-50", "51-60",
  28. "61-70", "71-80", "81-90", "91-100", "101-110", "111-120",
  29. "121-130", "131-140", "141-150", "151-160", "161-170", "171-180",
  30. "181-190", "191-200", "201-210", "211-220", "221-230", "231-240",
  31. "241-250", "251-260", "261-270", "271-280", "281-290", "291-350"],
  32. }
  33. class ProductConfig:
  34. FEATURE_COLUMNS = [
  35. "city_uuid", # 地市id
  36. "product_code", # 商品编码
  37. "factory_name", # 产地(工业公司名称)
  38. "brand_name", # 品牌名称
  39. "is_low_tar", # 低焦油卷烟
  40. "is_medium", # 中支烟
  41. "is_tiny", # 细支烟
  42. "is_coarse", # 粗支烟(同时非中非细)
  43. "is_exploding_beads", # 爆珠烟
  44. "no_is_exploding_beads", # 非爆珠烟
  45. "is_abnormity", # 异形包装
  46. "no_is_abnormity", # 非异形包装
  47. "is_cig", # 雪茄烟
  48. "no_is_cig", # 非雪茄烟
  49. "is_chuangxin", # 创新品类
  50. "no_is_chuangxin", # 非创新品类
  51. "direct_retail_price", # 卷烟建议零售价
  52. "tbc_total_length", # 烟支总长度
  53. "product_style", # 包装类型
  54. ]
  55. ONEHOT_CAT = {
  56. "factory_name": ["安徽中烟", "澳门云福卷烟厂", "北欧烟草集团", "博格集团", "重庆中烟", "川渝中烟", "菲利普莫里斯亚洲",
  57. "福建中烟", "甘肃工业", "广东中烟", "广西中烟", "贵州中烟", "海南红塔", "河北中烟", "河南中烟",
  58. "黑龙江工业", "红塔辽宁烟草", "湖北中烟", "湖南中烟", "吉林工业", "家源开发股份有限公司",
  59. "嘉莱赫国际有限公司", "江苏中烟", "江西中烟", "凯德控股有限公司", "力量雪茄烟草有限公司",
  60. "南洋兄弟烟草股份", "内蒙古昆明卷烟", "日本烟草(香港)有限公司", "三宝麟国际集团", "厦门调拨站",
  61. "山东中烟", "山西昆明烟草", "陕西中烟", "上海烟草(集团)公司", "上海烟草公司", "深圳工业", "四川中烟",
  62. "特富意烟草(国际)", "雪茄客烟草国际贸易有限公司", "耀莱雪茄控股有限公司", "引领国际有限公司",
  63. "英飞烽香港有限公司", "英美烟草中国有限公司", "云南中烟", "浙江中烟", "中茄国际贸易有限公司",
  64. "中烟英美烟草国际有限公司", "株式会社 KT&G"],
  65. "brand_name": ["万宝路", "555", "骆驼(国外)", "大华", "娇子", "大青山", "龙凤呈祥", "黄鹤楼", "真龙", "七匹狼",
  66. "芙蓉王", "双喜(广)", "贵烟", "钓鱼台", "红双喜(南洋)", "云烟", "蒙特", "富恩特", "拉·加莱拉", "苏烟",
  67. "丹纳曼", "黄山", "南京", "利群", "金桥", "泰山", "好日子", "石林", "美登", "红河", "嘉辉", "七星",
  68. "都彭", "天下秀", "长城", "高希霸", "钻石", "金圣", "王冠雪茄", "黄金叶", "中南海", "长白山", "红旗渠",
  69. "建牌", "大卫杜夫", "罗密欧", "茂大", "红金龙", "天子", "熊猫", "双喜(深)", "大前门", "兰州",
  70. "红双喜(沪)", "雄狮", "广州", "红玫王", "黄果树", "红塔山", "福", "小熊猫", "爱喜", "蒙特利", "玉溪",
  71. "都宝", "麦克纽杜", "卡里罗", "中华", "牡丹(沪)", "阿里山", "顺百利", "白沙", "羊城", "白云",
  72. "特美思", "国宾", "帕特加", "比德奥", "冬虫夏草", "威龙(湛江)", "香格里拉", "红梅", "延安",
  73. "特富意", "石狮", "金香港", "好猫", "登喜路", "乐迪", "林海灵芝", "椰树", "北京", "大红鹰", "大丰收",
  74. "红双喜(武汉)", "五叶神", "狮", "优民", "将军", "遵义", "恒大", "飞马", "红三环", "芙蓉", "工字",
  75. "古田", "狮牌", "君力", "哈尔滨", "梦都", "香梅(阜阳)", "哈德门", "梅州", "红山茶", "猴王", "沙龙",
  76. "潘趣", "狮子牌", "上海", "红玫", "醒宝", "广州湾", "百乐门", "关塔那摩", "威斯", "五一", "寿百年",
  77. "人民大会堂", "土楼", "三沙", "西湖", "光明", "阿诗玛", "宝亨", "恭贺新禧", "长寿", "茶花", "迎客松",
  78. "龙烟", "金澳门", "宝岛", "多米尼加之花", "国喜", "金驼", "君特欧", "上游", "幸福", "春城", "吉庆",
  79. "黄山松", "黄金龙", "紫气东来", "彼亚赛", "银辉", "潮牌", "庐山", "三峡", "壹支笔", "双叶"],
  80. "is_low_tar": ["是", "否"],
  81. "is_medium": ["是", "否"],
  82. "is_tiny": ["是", "否"],
  83. "is_coarse": ["是", "否"],
  84. "is_exploding_beads": ["是", "否"],
  85. "no_is_exploding_beads": ["是", "否"],
  86. "is_abnormity": ["是", "否"],
  87. "no_is_abnormity": ["是", "否"],
  88. "is_cig": ["是", "否"],
  89. "no_is_cig": ["是", "否"],
  90. "is_chuangxin": ["是", "否"],
  91. "no_is_chuangxin": ["是", "否"],
  92. "direct_retail_price": ["0-10", "10-30", "31-50", "51-100", "10-19.9", "250-499.9", "200-249.9",
  93. "5-9.9", "0-5", "100-109.9", "150-199.9", "101-150", "120-129.9", "大于500",
  94. "20-29.9", "30-39.9", "140-149.9", "50-59.9", "40-49.9", "80-89.9", "60-69.9",
  95. "70-79.9", "大于150", "130-139.9", "90-99.9", "110-119.9"],
  96. "tbc_total_length": ["小于79", "80-89", "90-100", "大于120"],
  97. "product_style": ["包装类型(条盒硬盒)", "包装类型(条包硬盒)", "包装类型(条盒软盒)", "包装类型(条包软盒)", "包装类型(铁盒)", "包装类型(其它)"],
  98. }
  99. class OrderConfig:
  100. FEATURE_COLUMNS = [
  101. "cust_uuid", # 零售户uuid
  102. "cust_code", # 零售户编码
  103. "product_code", # 品牌规格编码
  104. "sale_qty", # 销量包
  105. "sale_qty_l", # 销量上期
  106. "sale_qty_hb", # 销量环比
  107. "sale_amt", # 销售额包
  108. ]
  109. class ShopConfig:
  110. FEATURE_COLUMNS = [
  111. "cust_code", # 客户编码
  112. "r_home_num", # 常驻人口_居住人数
  113. "r_work_num", # 常驻人口_工作人数
  114. "r_resident_num", # 常驻人口_工作或居住人数
  115. "r_urban_cons_middle", # 常驻人口_城市消费水平_中
  116. "r_urban_cons_low", # 常驻人口_城市消费水平_低
  117. "r_urban_cons_lower", # 常驻人口_城市消费水平_次低
  118. "r_urban_cons_secondhigh", # 常驻人口_城市消费水平_次高
  119. "r_urban_cons_high", # 常驻人口_城市消费水平_高
  120. "r_edu_junior_middle", # 常驻人口_学历_初中
  121. "r_edu_doctor", # 常驻人口_学历_博士
  122. "r_edu_specialty", # 常驻人口_学历_大专
  123. "r_edu_primary", # 常驻人口_学历_小学
  124. "r_edu_college", # 常驻人口_学历_本科
  125. "r_edu_postgraduate", # 常驻人口_学历_硕士
  126. "r_edu_senior_middle", # 常驻人口_学历_高中
  127. "r_house_price79999", # 常驻人口_居住社区房价_60000_79999
  128. "r_house_price59999", # 常驻人口_居住社区房价_40000_59999
  129. "r_house_price39999", # 常驻人口_居住社区房价_20000_39999
  130. "r_house_price19999", # 常驻人口_居住社区房价_10000_19999
  131. "r_house_price9999", # 常驻人口_居住社区房价_8000_9999
  132. "r_house_price7999", # 常驻人口_居住社区房价_5000_7999
  133. "r_house_price4999", # 常驻人口_居住社区房价_2000_4999
  134. "r_age_17", # 常驻人口_年龄_0_17
  135. "r_age_24", # 常驻人口_年龄_18_24
  136. "r_age_30", # 常驻人口_年龄_25_30
  137. "r_age_35", # 常驻人口_年龄_31_35
  138. "r_age_40", # 常驻人口_年龄_36_40
  139. "r_age_45", # 常驻人口_年龄_41_45
  140. "r_age_60", # 常驻人口_年龄_46_60
  141. "r_age_over_60", # 常驻人口_年龄_61以上
  142. "r_sex_woman", # 常驻人口_性别_女
  143. "r_sex_man", # 常驻人口_性别_男
  144. "r_catering_50", # 常驻人口_餐饮消费水平_50
  145. "r_catering_100", # 常驻人口_餐饮消费水平_100
  146. "r_catering_150", # 常驻人口_餐饮消费水平_150
  147. "r_catering_200", # 常驻人口_餐饮消费水平_200
  148. "r_catering_500", # 常驻人口_餐饮消费水平_500
  149. "r_catering_over_500", # 常驻人口_餐饮消费水平_500以上
  150. "r_catering_times_2", # 常驻人口_餐饮消费频次_1_2
  151. "r_catering_times_4", # 常驻人口_餐饮消费频次_2_4
  152. "r_catering_times_6", # 常驻人口_餐饮消费频次_4_6
  153. "r_catering_times_8", # 常驻人口_餐饮消费频次_6_8
  154. "r_catering_times_10", # 常驻人口_餐饮消费频次_8_10
  155. "r_catering_times_11", # 常驻人口_餐饮消费频次_11以上
  156. "r_native_beijing", # 常驻人口_家乡地_北京市
  157. "r_native_tianjing", # 常驻人口_家乡地_天津市
  158. "r_native_hebei", # 常驻人口_家乡地_河北省
  159. "r_native_shanxi", # 常驻人口_家乡地_山西省
  160. "r_native_neimeng", # 常驻人口_家乡地_内蒙古
  161. "r_native_liaoning", # 常驻人口_家乡地_辽宁省
  162. "r_native_jilin", # 常驻人口_家乡地_吉林省
  163. "r_native_heilongjiang", # 常驻人口_家乡地_黑龙江省
  164. "r_native_shanghai", # 常驻人口_家乡地_上海市
  165. "r_native_jiangsu", # 常驻人口_家乡地_江苏省
  166. "r_native_zhejiang", # 常驻人口_家乡地_浙江省
  167. "r_native_anhui", # 常驻人口_家乡地_安徽省
  168. "r_native_fujian", # 常驻人口_家乡地_福建省
  169. "r_native_jiangix", # 常驻人口_家乡地_江西省
  170. "r_native_shandong", # 常驻人口_家乡地_山东省
  171. "r_native_henan", # 常驻人口_家乡地_河南省
  172. "r_native_hubei", # 常驻人口_家乡地_湖北省
  173. "r_native_hunan", # 常驻人口_家乡地_湖南省
  174. "r_native_guangdong", # 常驻人口_家乡地_广东省
  175. "r_native_hainan", # 常驻人口_家乡地_海南省
  176. "r_native_sichuan", # 常驻人口_家乡地_四川省
  177. "r_native_guizhou", # 常驻人口_家乡地_贵州省
  178. "r_native_yunnan", # 常驻人口_家乡地_云南省
  179. "r_native_shan", # 常驻人口_家乡地_陕西省
  180. "r_native_gansu", # 常驻人口_家乡地_甘肃省
  181. "r_native_qinghai", # 常驻人口_家乡地_青海省
  182. "r_native_guangxi", # 常驻人口_家乡地_广西壮族自治区
  183. "r_native_ningxia", # 常驻人口_家乡地_宁夏回族自治区
  184. "r_native_xinjiang", # 常驻人口_家乡地_新疆维吾尔自治区
  185. "r_native_xizang", # 常驻人口_家乡地_西藏自治区
  186. "r_native_chongqing", # 常驻人口_家乡地_重庆市
  187. "r_native_hongkong", # 常驻人口_家乡地_香港
  188. "r_native_macao", # 常驻人口_家乡地_澳门
  189. "r_native_taiwan", # 常驻人口_家乡地_台湾
  190. "r_native_other", # 常驻人口_家乡地_其它
  191. "f_flow_num", # 流动人口_工作日_日均流动人口数量
  192. "f_holiday_flow_num", # 流动人口_节假日_日均流动人口数量
  193. "f_workday_flow_num", # 流动人口_日均流动人口数量
  194. "f_flowurban_cons_middle", # 日均流动_城市消费水平_中
  195. "f_flowurban_cons_low", # 日均流动_城市消费水平_低
  196. "f_flowurban_cons_lower", # 日均流动_城市消费水平_次低
  197. "f_flowurban_cons_second_high", # 日均流动_城市消费水平_次高
  198. "f_flowurban_cons_high", # 日均流动_城市消费水平_高
  199. "f_flowedu_junior_middle", # 日均流动_学历_初中
  200. "f_flowedu_doctor", # 日均流动_学历_博士
  201. "f_flowedu_specialty", # 日均流动_学历_大专
  202. "f_flowedu_primary", # 日均流动_学历_小学
  203. "f_flowedu_college", # 日均流动_学历_本科
  204. "f_flowedu_postgraduate", # 日均流动_学历_硕士
  205. "f_flowedu_senior_middle", # 日均流动_学历_高中
  206. "f_flowhouse_middle", # 日均流动_居住社区房价_中
  207. "f_flowhouse_low", # 日均流动_居住社区房价_低
  208. "f_flowhouse_lower", # 日均流动_居住社区房价_次低
  209. "f_flowhouse_second_high", # 日均流动_居住社区房价_次高
  210. "f_flowhouse_high", # 日均流动_居住社区房价_高
  211. "f_flowage_17", # 日均流动_年龄_0_17
  212. "f_flowage_24", # 日均流动_年龄_18_24
  213. "f_flowage_30", # 日均流动_年龄_25_30
  214. "f_flowage_35", # 日均流动_年龄_31_35
  215. "f_flowage_40", # 日均流动_年龄_36_40
  216. "f_flowage_45", # 日均流动_年龄_41_45
  217. "f_flowage_60", # 日均流动_年龄_46_60
  218. "f_flowage_over_60", # 日均流动_年龄_61以上
  219. "f_flowsex_woman", # 日均流动_性别_女
  220. "f_flowsex_man", # 日均流动_性别_男
  221. "f_holidayurban_cons_middle", # 节假日流动_城市消费水平_中
  222. "f_holidayurban_cons_low", # 节假日流动_城市消费水平_低
  223. "f_holidayurban_cons_lower", # 节假日流动_城市消费水平_次低
  224. "f_holidayurban_cons_secondhigh", # 节假日流动_城市消费水平_次高
  225. "f_holidayurban_cons_high", # 节假日流动_城市消费水平_高
  226. "f_holidayedu_junior_middle", # 节假日流动_学历_初中
  227. "f_holidayedu_doctor", # 节假日流动_学历_博士
  228. "f_holidayedu_specialty", # 节假日流动_学历_大专
  229. "f_holidayedu_primary", # 节假日流动_学历_小学
  230. "f_holidayedu_college", # 节假日流动_学历_本科
  231. "f_holidayedu_postgraduate", # 节假日流动_学历_硕士
  232. "f_holidayedu_senior_middle", # 节假日流动_学历_高中
  233. "f_holidayhouse_middle", # 节假日流动_居住社区房价_中
  234. "f_holidayhouse_low", # 节假日流动_居住社区房价_低
  235. "f_holidayhouse_lower", # 节假日流动_居住社区房价_次低
  236. "f_holidayhouse_second_high", # 节假日流动_居住社区房价_次高
  237. "f_holidayhouse_high", # 节假日流动_居住社区房价_高
  238. "f_holidayage_17", # 节假日流动_年龄_0_17
  239. "f_holidayage_24", # 节假日流动_年龄_18_24
  240. "f_holidayage_30", # 节假日流动_年龄_25_30
  241. "f_holidayage_35", # 节假日流动_年龄_31_35
  242. "f_holidayage_40", # 节假日流动_年龄_36_40
  243. "f_holidayage_45", # 节假日流动_年龄_41_45
  244. "f_holidayage_60", # 节假日流动_年龄_46_60
  245. "f_holidayage_over_60", # 节假日流动_年龄_61以上
  246. "f_holidaysex_woman", # 节假日流动_性别_女
  247. "f_holidaysex_man", # 节假日流动_性别_男
  248. "f_workday_urban_cons_middle", # 工作日流动_城市消费水平_中
  249. "f_workday_urban_cons_low", # 工作日流动_城市消费水平_低
  250. "f_workday_urban_cons_lower", # 工作日流动_城市消费水平_次低
  251. "f_workday_urban_cons_secondhigh",# 工作日流动_城市消费水平_次高
  252. "f_workday_urban_cons_high", # 工作日流动_城市消费水平_高
  253. "f_workday_edu_junior_middle", # 工作日流动_学历_初中
  254. "f_workday_edu_doctor", # 工作日流动_学历_博士
  255. "f_workday_edu_specialty", # 工作日流动_学历_大专
  256. "f_workday_edu_primary", # 工作日流动_学历_小学
  257. "f_workday_edu_college", # 工作日流动_学历_本科
  258. "f_workday_edu_postgraduate", # 工作日流动_学历_硕士
  259. "f_workday_edu_senior_middle", # 工作日流动_学历_高中
  260. "f_workday_house_middle", # 工作日流动_居住社区房价_中
  261. "f_workday_house_low", # 工作日流动_居住社区房价_低
  262. "f_workday_house_lower", # 工作日流动_居住社区房价_次低
  263. "f_workday_house_second_high", # 工作日流动_居住社区房价_次高
  264. "f_workday_house_high", # 工作日流动_居住社区房价_高
  265. "f_workday_age_17", # 工作日流动_年龄_0_17
  266. "f_workday_age_24", # 工作日流动_年龄_18_24
  267. "f_workday_age_30", # 工作日流动_年龄_25_30
  268. "f_workday_age_35", # 工作日流动_年龄_31_35
  269. "f_workday_age_40", # 工作日流动_年龄_36_40
  270. "f_workday_age_45", # 工作日流动_年龄_41_45
  271. "f_workday_age_60", # 工作日流动_年龄_46_60
  272. "f_workday_age_over_60", # 工作日流动_年龄_61以上
  273. "f_workday_sex_woman", # 工作日流动_性别_女
  274. "f_workday_sex_man", # 工作日流动_性别_男
  275. ]
  276. ONEHOT_CAT = {
  277. "r_home_num": ["0-100", "101-500", "501-2000", "2001-5000", "5001-10000", "10000以上"],
  278. "r_work_num": ["0-100", "101-500", "501-2000", "2001-5000", "5001-10000", "10000以上"],
  279. "r_resident_num": ["0-100", "101-500", "501-2000", "2001-5000", "5001-10000", "10001-20000", "20000以上"],
  280. "r_urban_cons_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  281. "r_urban_cons_low": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  282. "r_urban_cons_lower": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  283. "r_urban_cons_secondhigh": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  284. "r_urban_cons_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  285. "r_edu_junior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  286. "r_edu_doctor": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  287. "r_edu_specialty": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  288. "r_edu_primary": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  289. "r_edu_college": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  290. "r_edu_postgraduate": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  291. "r_edu_senior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  292. "r_house_price79999": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  293. "r_house_price59999": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  294. "r_house_price39999": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  295. "r_house_price19999": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  296. "r_house_price9999": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  297. "r_house_price7999": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  298. "r_house_price4999": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  299. "r_age_17": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  300. "r_age_24": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  301. "r_age_30": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  302. "r_age_35": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  303. "r_age_40": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  304. "r_age_45": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  305. "r_age_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  306. "r_age_over_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  307. "r_sex_woman": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  308. "r_sex_man": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  309. "r_catering_50": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  310. "r_catering_100": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  311. "r_catering_150": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  312. "r_catering_200": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  313. "r_catering_500": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  314. "r_catering_over_500": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  315. "r_catering_times_2": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  316. "r_catering_times_4": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  317. "r_catering_times_6": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  318. "r_catering_times_8": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  319. "r_catering_times_10": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  320. "r_catering_times_11": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  321. "r_native_beijing": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  322. "r_native_tianjing": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  323. "r_native_hebei": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  324. "r_native_shanxi": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  325. "r_native_neimeng": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  326. "r_native_liaoning": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  327. "r_native_jilin": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  328. "r_native_heilongjiang": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  329. "r_native_shanghai": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  330. "r_native_jiangsu": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  331. "r_native_zhejiang": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  332. "r_native_anhui": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  333. "r_native_fujian": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  334. "r_native_jiangix": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  335. "r_native_shandong": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  336. "r_native_henan": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  337. "r_native_hubei": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  338. "r_native_hunan": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  339. "r_native_guangdong": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  340. "r_native_hainan": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  341. "r_native_sichuan": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  342. "r_native_guizhou": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  343. "r_native_yunnan": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  344. "r_native_shan": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  345. "r_native_gansu": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  346. "r_native_qinghai": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  347. "r_native_guangxi": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  348. "r_native_ningxia": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  349. "r_native_xinjiang": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  350. "r_native_xizang": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  351. "r_native_chongqing": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  352. "r_native_hongkong": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  353. "r_native_macao": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  354. "r_native_taiwan": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  355. "r_native_other": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  356. "f_flow_num": ["0-100", "101-500", "501-2000", "2001-5000", "5001-10000", "10001-50000", "50001-100000", "100000以上"],
  357. "f_holiday_flow_num": ["0-100", "101-500", "501-2000", "2001-5000", "5001-10000", "10001-50000", "50001-100000", "100000以上"],
  358. "f_workday_flow_num": ["0-100", "101-500", "501-2000", "2001-5000", "5001-10000", "10001-50000", "50001-100000", "100000以上"],
  359. "f_flowurban_cons_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  360. "f_flowurban_cons_low": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  361. "f_flowurban_cons_lower": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  362. "f_flowurban_cons_second_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  363. "f_flowurban_cons_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  364. "f_flowedu_junior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  365. "f_flowedu_doctor": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  366. "f_flowedu_specialty": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  367. "f_flowedu_primary": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  368. "f_flowedu_college": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  369. "f_flowedu_postgraduate": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  370. "f_flowedu_senior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  371. "f_flowhouse_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  372. "f_flowhouse_low": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  373. "f_flowhouse_lower": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  374. "f_flowhouse_second_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  375. "f_flowhouse_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  376. "f_flowage_17": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  377. "f_flowage_24": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  378. "f_flowage_30": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  379. "f_flowage_35": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  380. "f_flowage_40": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  381. "f_flowage_45": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  382. "f_flowage_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  383. "f_flowage_over_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  384. "f_flowsex_woman": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  385. "f_flowsex_man": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  386. "f_holidayurban_cons_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  387. "f_holidayurban_cons_low": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  388. "f_holidayurban_cons_lower": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  389. "f_holidayurban_cons_secondhigh": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  390. "f_holidayurban_cons_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  391. "f_holidayedu_junior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  392. "f_holidayedu_doctor": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  393. "f_holidayedu_specialty": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  394. "f_holidayedu_primary": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  395. "f_holidayedu_college": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  396. "f_holidayedu_postgraduate": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  397. "f_holidayedu_senior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  398. "f_holidayhouse_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  399. "f_holidayhouse_low": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  400. "f_holidayhouse_lower": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  401. "f_holidayhouse_second_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  402. "f_holidayhouse_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  403. "f_holidayage_17": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  404. "f_holidayage_24": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  405. "f_holidayage_30": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  406. "f_holidayage_35": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  407. "f_holidayage_40": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  408. "f_holidayage_45": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  409. "f_holidayage_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  410. "f_holidayage_over_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  411. "f_holidaysex_woman": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  412. "f_holidaysex_man": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  413. "f_workday_urban_cons_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  414. "f_workday_urban_cons_low": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  415. "f_workday_urban_cons_lower": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  416. "f_workday_urban_cons_secondhigh": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  417. "f_workday_urban_cons_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  418. "f_workday_edu_junior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  419. "f_workday_edu_doctor": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  420. "f_workday_edu_specialty": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  421. "f_workday_edu_primary": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  422. "f_workday_edu_college": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  423. "f_workday_edu_postgraduate": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  424. "f_workday_edu_senior_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  425. "f_workday_house_middle": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  426. "f_workday_house_low": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  427. "f_workday_house_lower": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  428. "f_workday_house_second_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  429. "f_workday_house_high": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  430. "f_workday_age_17": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  431. "f_workday_age_24": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  432. "f_workday_age_30": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  433. "f_workday_age_35": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  434. "f_workday_age_40": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  435. "f_workday_age_45": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  436. "f_workday_age_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  437. "f_workday_age_over_60": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  438. "f_workday_sex_woman": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  439. "f_workday_sex_man": ["0-10", "10-20", "20-30", "30-40", "40-50", "50-60", "60-70", "70-80", "80-90", "90-100"],
  440. }