mvp.py 18 KB

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  1. from mysql_db import MysqlDB
  2. from excel_util import ExcelUtil
  3. class Mvp:
  4. """
  5. ce mvp 答题数据统计
  6. 城市特例 北京市,上海市, 重庆市,天津市
  7. """
  8. age_dict = {
  9. '00-04年生': '00后',
  10. '05-09年生': '05后',
  11. '50-59年生': '50后',
  12. '60-69年生': '60后',
  13. '70-74年生': '70后',
  14. '75-79年生': '75后',
  15. '80-84年生': '80后',
  16. '85-89年生': '85后',
  17. '90-94年生': '90后',
  18. '95-99年生': '95后'
  19. }
  20. crowd = ['A', 'B', 'C', 'D', 'E', 'F']
  21. # 获取答题记录中城市列表
  22. sql_1 = 'select city from f_t_daren_score_2 group by city'
  23. # 获取父选项和父题id
  24. sql_2 = 'select a.id, a.content, b.id, b.name from bq_option a left join bq_question b on a.question_id = b.id ' \
  25. 'where a.serial_number = %s and b.serial_number = %s and a.status = b.status = 1 '
  26. # 获取答题人的年龄段集合
  27. sql_4 = 'select nld from f_t_daren_score_2 group by nld'
  28. # 根据城市,年龄段,人群分类统计答题记录数
  29. sql_5 = 'select group_type, COUNT(uuid) from f_t_daren_score_2 where (city = %s or province = %s) and nld ' \
  30. '= %s and uuid in %s group by group_type '
  31. # 根据父选项获取子选项id列表
  32. sql_6 = 'SELECT c.id, c.sub_question_id, c.content FROM bq_sub_option c WHERE c.father_id in (SELECT a.id FROM ' \
  33. 'bq_option a ' \
  34. 'LEFT JOIN bq_question b ON a.question_id = b.id WHERE a.serial_number = %s AND b.serial_number = %s ' \
  35. 'and a.status = 1 and b.status = 1) and c.status = 1 '
  36. # 根据子题id获取包含子题id的测试
  37. sql_7 = 'select group_type from bq_testcase where status = 1 and FIND_IN_SET(%s, question_ids)'
  38. # 根据子选项id统计答题数
  39. sql_8 = 'SELECT count(1) FROM f_t_daren_score_2 a LEFT JOIN d_shangju_tiku_02 b ON a.sub_question_id = ' \
  40. 'b.sub_question_id AND (a.score = b.score or a.score = b.sub_option_id) and a.testcase_id = ' \
  41. 'b.testcase_id WHERE b.sub_option_id in %s' \
  42. 'and (a.city = %s or a.province = %s) and a.nld = %s and a.uuid in %s'
  43. # 获取一个uuid下答题的子选项id列表
  44. sql_10 = 'select DISTINCT uuid, GROUP_CONCAT(DISTINCT b.sub_option_id) from f_t_daren_score_2 a left join ' \
  45. 'd_shangju_tiku_02 b on a.sub_question_id = b.sub_question_id and (a.score = b.score or a.score = ' \
  46. 'b.sub_option_id) where a.status = ' \
  47. 'b.status = 1 group by uuid '
  48. # 向表mvp_crowd_info插入数据
  49. sql_11 = 'insert into mvp_crowd_info(age_area, city_name, crowd_type, status) values(%s, %s, %s, 1)'
  50. # 向表mvp_crowd_info_behavior中插入数据
  51. sql_12 = 'insert into mvp_crowd_info_behavior(crowd_info_id, behavioral_interest, standard_value, status) values(' \
  52. '%s, %s, ' \
  53. '%s, 1) '
  54. # 向表mvp_crowd_info_module中插入数据
  55. sql_13 = 'insert into mvp_crowd_info_module(crowd_info_id, module_name, standard_value, status) values (%s, %s, ' \
  56. '%s, 1) '
  57. sql_14 = 'select a.id, a.age_area, a.city_name, a.crowd_type from mvp_crowd_info a where a.status = 1'
  58. def __init__(self, path=None):
  59. self.shangju_db = MysqlDB('shangju')
  60. self.marketing_db = MysqlDB('bi_report')
  61. # self.shangju_db.truncate('mvp_standard_score')
  62. self.tag_data = ExcelUtil(file_name=path).init_mvp_data()
  63. self.crowd_info = ExcelUtil(file_name=path, sheet_name='选项-人群分类对应表').init_crowd_info()
  64. self.citys = self.init_city()
  65. self.age = self.init_age()
  66. self.people_sub_option_ids = self.marketing_db.select(self.sql_10)
  67. self.crowd_contain_sub_option_ids = self.get_crowd_contain_sub_option_ids()
  68. self.module_scores = ExcelUtil(file_name='set-behavior-tag.xlsx', sheet_name='算法关系表').init_module_info()
  69. # self.scores_tag = ExcelUtil(file_name='行为与模块分值汇总.xlsx', sheet_name='行为').init_scores()
  70. # self.score_module = ExcelUtil(file_name='行为与模块分值汇总.xlsx', sheet_name='模块').init_scores()
  71. self.scores_tag = None
  72. self.score_module = None
  73. def close(self):
  74. self.shangju_db.close()
  75. self.marketing_db.close()
  76. def init_city(self):
  77. """
  78. 获取答题数据中的城市。
  79. :return:
  80. """
  81. citys = ['宁波市', '上海市', '苏州市', '无锡市', '宁波市']
  82. # citys_info = self.marketing_db.select(self.sql_1)
  83. # citys.extend([x[0] for x in citys_info if x[0] is not None])
  84. return citys
  85. def query_behavioral_info(self, city=None, age=None, crowd=None):
  86. """
  87. 查询行为兴趣信息
  88. :return:
  89. """
  90. # datas = []
  91. # for key in self.tag_data.keys():
  92. # values = self.tag_data[key]
  93. # for value in values:
  94. # question = value[0].split('-')[0]
  95. # option = value[0].split('-')[1]
  96. # corr = value[1]
  97. # data = self.shangju_db.select(self.sql_2, [option, question])
  98. # if len(data) > 0:
  99. # print([question, option, data[0][3], data[0][1], key, corr])
  100. # datas.append([question, option, data[0][3], data[0][1], key, corr])
  101. # self.shangju_db.truncate('mvp_question_classification')
  102. # self.shangju_db.add_some(self.sql_3, datas)
  103. scores_behavioral = self.city_age_crowd(city, age, crowd)
  104. # scores_module = self.module_score(crowd, city, age, scores_behavioral['score'])
  105. # result = {'行为兴趣分值': scores_behavioral['score'], '模块分值': scores_module}
  106. print('update finished!!!')
  107. return scores_behavioral
  108. def module_score(self, crowd, city, age, scores):
  109. """
  110. 模块分数计算
  111. 城市 年龄 人群分类 模块名称 分数
  112. :return:
  113. """
  114. import json
  115. print(json.dumps(scores, ensure_ascii=False))
  116. modules = self.module_scores[crowd]
  117. result = []
  118. for key in modules.keys():
  119. values = modules[key]
  120. module_name = key
  121. score = 0
  122. for value in values:
  123. behavioral_name = value[0]
  124. weight = float(value[2])
  125. standard_score = [x[4] for x in scores if x[2] == behavioral_name]
  126. if len(standard_score) > 0:
  127. score += standard_score[0] * weight
  128. result.append([city, age, crowd, module_name, score])
  129. return result
  130. # def insert_data(self, scores_behavioral, scores_module):
  131. def insert(self):
  132. """
  133. 计算数据写入数据库中,供接口查看
  134. :return:
  135. """
  136. infos = []
  137. for city in ['上海市', '宁波市', '苏州市', '杭州市', ' 无锡市']:
  138. for age in ['50-59年生', '60-69年生', '70-74年生', '75-79年生', '80-84年生', '85-89年生', '90-94年生', '95-99年生', '00'
  139. '-04年生',
  140. '05-09年生']:
  141. for c_type in ['A', 'B', 'C', 'D', 'E', 'F']:
  142. age_area = self.age_dict.get(age)
  143. if age_area:
  144. infos.append([age_area, city, c_type])
  145. self.shangju_db.add_some(self.sql_11, infos)
  146. def query_data(self):
  147. ids = self.shangju_db.select(self.sql_14)
  148. return ids
  149. def shanghai_85_module_score_insert(self):
  150. """
  151. 上海市,85后模块分数计算
  152. :return:
  153. """
  154. result = []
  155. for crowd in self.crowd:
  156. modules = self.module_scores[crowd]
  157. for key in modules.keys():
  158. values = modules[key]
  159. module_name = key
  160. score = 0
  161. for value in values:
  162. behavioral_name = value[0]
  163. weight = float(value[2])
  164. # standard_score = [x[4] for x in scores if x[2] == behavioral_name]
  165. standard_score = float(value[1])
  166. if standard_score is not None:
  167. score += standard_score * weight
  168. result.append(['上海市', '85后', crowd, module_name, score])
  169. return {'score': result, 'data': self.module_scores}
  170. def tag_module_score_insert(self):
  171. """
  172. 标签模块分数写入数据库
  173. :return:
  174. """
  175. ids = self.query_data()
  176. insert_data = []
  177. insert_data_1 = []
  178. for tag, module in zip(self.scores_tag, self.score_module):
  179. city = tag[0]
  180. age = tag[1]
  181. crowd = tag[2]
  182. tag_name = tag[3]
  183. tag_score = tag[4]
  184. city_2 = module[0]
  185. age_2 = module[1]
  186. crowd_2 = module[2]
  187. module_name_2 = module[3]
  188. module_score_2 = module[4]
  189. for id in ids:
  190. city_1 = id[2]
  191. age_1 = id[1]
  192. crowd_1 = id[3]
  193. id_1 = id[0]
  194. if city == city_1 and self.age_dict[age] == age_1 and crowd == crowd_1:
  195. insert_data.append([id_1, tag_name, tag_score])
  196. if city_2 == city_1 and self.age_dict[age_2] == age_1 and crowd_2 == crowd_1:
  197. insert_data_1.append([id_1, module_name_2, module_score_2])
  198. self.shangju_db.add_some(self.sql_12, insert_data)
  199. self.shangju_db.add_some(self.sql_13, insert_data_1)
  200. def init_age(self):
  201. """
  202. 获取答题数据中的年龄
  203. """
  204. age_info = self.marketing_db.select(self.sql_4)
  205. # print([x[0] for x in age_info])
  206. return [x[0] for x in age_info if x[0] is not None]
  207. def city_age_crowd(self, city=None, age=None, crowd=None):
  208. data_start = []
  209. result = []
  210. module_scores = []
  211. if city is not None and age is not None and crowd is not None:
  212. print('获取指定城市,年龄段,人群类型的数据...')
  213. people_uuids = self.get_people_uuid_by_type(crowd)
  214. behavior_data = None
  215. if len(people_uuids) > 0:
  216. print('{}-{}-{}'.format(city, age, crowd))
  217. datas = self.behavior_tag_init(city, age, people_uuids)
  218. data_start.append(datas)
  219. all_data, behavior_data_1 = self.calculation_standard_score(datas, city, age, crowd)
  220. result.append(all_data)
  221. behavior_data = behavior_data_1
  222. if behavior_data:
  223. module_scores.extend(self.module_score(crowd, city, age, behavior_data))
  224. pass
  225. else:
  226. print('获取所有case的数据...')
  227. # for city in self.citys:
  228. # for city in [city]:
  229. for age in self.age:
  230. for crowd_type in self.crowd:
  231. if age == '85-89年生' and city == '上海市':
  232. print('上海市85后数据导入人工值,无需计算...')
  233. pass
  234. else:
  235. # print(' {}{}'.format(city, age))
  236. people_uuids = self.get_people_uuid_by_type(crowd_type)
  237. behavior_data = None
  238. if len(people_uuids) > 0:
  239. print('{}-{}-{}'.format(city, age, crowd_type))
  240. datas = self.behavior_tag_init(city, age, people_uuids)
  241. data_start.append(datas)
  242. all_data, behavior_data_1 = self.calculation_standard_score(datas, city, age, crowd)
  243. result.append(all_data)
  244. behavior_data = behavior_data_1
  245. if behavior_data:
  246. module_scores.extend(self.module_score(crowd_type, city, age, behavior_data))
  247. # return result
  248. # data_list = []
  249. # for e in data_start:
  250. # for key in e.keys():
  251. # values = e[key]
  252. # for sub_e in values:
  253. # ele = [key]
  254. # ele.extend(sub_e)
  255. # data_list.append(ele)
  256. # pass
  257. return {'tag_score': result, 'module_score': module_scores}
  258. # return {'score': result, 'data': data_list}
  259. def behavior_tag_init(self, city, age, people_uuids):
  260. result = {}
  261. self.group_type_count = self.marketing_db.select(self.sql_5, [city, city, age, people_uuids])
  262. # 表名
  263. for key in self.tag_data:
  264. values = self.tag_data[key]
  265. result_sub = {}
  266. # 标签
  267. for key_tag_name in values.keys():
  268. questions = values[key_tag_name]
  269. elements = []
  270. for value in questions:
  271. question = value[0].split('-')[0]
  272. option = value[0].split('-')[1]
  273. corr = value[1]
  274. fz, fm = self.molecular_value(question, option, city, age, people_uuids)
  275. if fm == 0:
  276. c = 0
  277. else:
  278. c = fz / fm
  279. elements.append([question, option, corr, fz, fm, c])
  280. result_sub[key_tag_name] = elements
  281. result[key] = self.indicator_calculation_d_e(result_sub)
  282. return result
  283. def molecular_value(self, queston, option, city, age, people_uuids):
  284. # 获取当前父选项包含的子选项id和子题id列表
  285. result = self.shangju_db.select(self.sql_6, [option, queston])
  286. sub_option_ids = []
  287. group_types = []
  288. for rt in result:
  289. sub_option_id, sub_question_id, content = rt[0], rt[1], rt[2]
  290. grouptypes = self.shangju_db.select(self.sql_7, [sub_question_id])
  291. for g_t in grouptypes:
  292. if g_t[0] not in group_types:
  293. group_types.append(g_t[0])
  294. sub_option_ids.append(sub_option_id)
  295. # 计算子选项在答题记录中的点击数
  296. sub_options_count = 0
  297. if len(sub_option_ids) > 0:
  298. result_1 = self.marketing_db.select(self.sql_8, [sub_option_ids, city, city, age, people_uuids])
  299. sub_options_count = result_1[0][0]
  300. # 计算父选项包含的子选项对应的子题所在的测试gt包含的点击数。
  301. denominator_value = 0
  302. for info in self.group_type_count:
  303. if info[0] in group_types:
  304. denominator_value += info[1]
  305. return sub_options_count, denominator_value
  306. def indicator_calculation_d_e(self, data):
  307. result = {}
  308. for key in data.keys():
  309. values = data[key]
  310. c_list = []
  311. for x in values:
  312. _x = x[5]
  313. if _x is not None and x != 0:
  314. c_list.append(_x)
  315. fm_list = [x[4] for x in values]
  316. sum_c = sum(fm_list)
  317. if len(c_list) == 0:
  318. min_c = 0
  319. else:
  320. min_c = min(c_list)
  321. elements = []
  322. for value in values:
  323. _value = []
  324. c = value[5]
  325. if sum_c == 0:
  326. d = 0
  327. else:
  328. d = c / sum_c
  329. e = c - min_c
  330. _value.extend(value)
  331. _value.append(d)
  332. _value.append(e)
  333. elements.append(_value)
  334. result[key] = elements
  335. return result
  336. def calculation_standard_score(self, datas, city, age, crowd_type):
  337. scores = {}
  338. for key_tag_type in datas.keys():
  339. print(key_tag_type)
  340. tag_type_data = datas[key_tag_type]
  341. scores_sub = []
  342. for key_tag in tag_type_data.keys():
  343. key_tag_data = tag_type_data[key_tag]
  344. print(key_tag)
  345. print(' 父题序号 父选项序号 相关系系数 分子值 分母值 百分比 人数权重 偏离值')
  346. values = [x[5] for x in key_tag_data]
  347. min_c = min(values)
  348. f = min_c
  349. for value in key_tag_data:
  350. print(' {}'.format(value))
  351. if value[2] is not None and value[7] is not None:
  352. f += float(value[2] * value[7])
  353. print(' 标准分:{}'.format(f))
  354. scores_sub.append([city, age, key_tag, crowd_type, f])
  355. scores[key_tag_type] = scores_sub
  356. # self.shangju_db.add_some(self.sql_9, scores)
  357. return scores, scores['用户画像-行为兴趣']
  358. def get_crowd_people(self):
  359. result = {}
  360. for type in self.crowd:
  361. uuids = self.get_people_uuid_by_type(type)
  362. result[type] = len(uuids)
  363. return result
  364. def get_people_uuid_by_type(self, type):
  365. uuids = []
  366. type_sub_option_ids = self.crowd_contain_sub_option_ids[type]
  367. for people in self.people_sub_option_ids:
  368. uuid = people[0]
  369. sub_option_ids = list(map(int, str(people[1]).split(',')))
  370. # list(set(a).intersection(set(b)))
  371. if len(list(set(sub_option_ids).intersection(set(type_sub_option_ids)))) > 0 and uuid not in uuids:
  372. uuids.append(uuid)
  373. return uuids
  374. def get_crowd_contain_sub_option_ids(self):
  375. """
  376. 获取ABCDEF人群包含的子选项id
  377. :return:
  378. """
  379. infos = {}
  380. for key in self.crowd_info.keys():
  381. values = self.crowd_info[key]
  382. sub_option_ids = []
  383. for value in values:
  384. if value is not None:
  385. vals = str(value).split('-')
  386. option, question = vals[1], vals[0]
  387. query_result = self.shangju_db.select(self.sql_6, [option, question])
  388. for qr in query_result:
  389. sub_option_id, sub_question_id, content = qr[0], qr[1], qr[2]
  390. sub_option_ids.append(int(sub_option_id))
  391. infos[key] = sub_option_ids
  392. print(infos)
  393. return infos