region_rule_rank_h.py 15 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360
  1. # -*- coding: utf-8 -*-
  2. # @ModuleName: region_rule_rank_h
  3. # @Author: Liqian
  4. # @Time: 2022/5/5 15:54
  5. # @Software: PyCharm
  6. import datetime
  7. import pandas as pd
  8. import math
  9. from odps import ODPS
  10. from threading import Timer
  11. from utils import MysqlHelper, RedisHelper, get_data_from_odps
  12. from config import set_config
  13. from log import Log
  14. config_, _ = set_config()
  15. log_ = Log()
  16. region_code = {
  17. '河北省': '130000',
  18. '山西省': '140000',
  19. '辽宁省': '210000',
  20. '吉林省': '220000',
  21. '黑龙江省': '230000',
  22. '江苏省': '320000',
  23. '浙江省': '330000',
  24. '安徽省': '340000',
  25. '福建省': '350000',
  26. '江西省': '360000',
  27. '山东省': '370000',
  28. '河南省': '410000',
  29. '湖北省': '420000',
  30. '湖南省': '430000',
  31. '广东省': '440000',
  32. '海南省': '460000',
  33. '四川省': '510000',
  34. '贵州省': '520000',
  35. '云南省': '530000',
  36. '陕西省': '610000',
  37. '甘肃省': '620000',
  38. '青海省': '630000',
  39. '台湾省': '710000',
  40. '北京': '110000',
  41. '天津': '120000',
  42. '内蒙古': '150000',
  43. '上海': '310000',
  44. '广西': '450000',
  45. '重庆': '500000',
  46. '西藏': '540000',
  47. '宁夏': '640000',
  48. '新疆': '650000',
  49. '香港': '810000',
  50. '澳门': '820000',
  51. 'None': '-1'
  52. }
  53. features = [
  54. 'code',
  55. 'videoid',
  56. 'lastonehour_preview', # 过去1小时预曝光人数
  57. 'lastonehour_view', # 过去1小时曝光人数
  58. 'lastonehour_play', # 过去1小时播放人数
  59. 'lastonehour_share', # 过去1小时分享人数
  60. 'lastonehour_return', # 过去1小时分享,过去1小时回流人数
  61. 'lastonehour_preview_total', # 过去1小时预曝光次数
  62. 'lastonehour_view_total', # 过去1小时曝光次数
  63. 'lastonehour_play_total', # 过去1小时播放次数
  64. 'lastonehour_share_total', # 过去1小时分享次数
  65. ]
  66. def get_region_code(region):
  67. """获取省份对应的code"""
  68. mysql_helper = MysqlHelper(mysql_info=config_.MYSQL_INFO)
  69. sql = f"SELECT ad_code FROM region_adcode WHERE parent_id = 0 AND region LIKE '{region}%';"
  70. ad_code = mysql_helper.get_data(sql=sql)
  71. return ad_code[0][0]
  72. def h_data_check(project, table, now_date):
  73. """检查数据是否准备好"""
  74. odps = ODPS(
  75. access_id=config_.ODPS_CONFIG['ACCESSID'],
  76. secret_access_key=config_.ODPS_CONFIG['ACCESSKEY'],
  77. project=project,
  78. endpoint=config_.ODPS_CONFIG['ENDPOINT'],
  79. connect_timeout=3000,
  80. read_timeout=500000,
  81. pool_maxsize=1000,
  82. pool_connections=1000
  83. )
  84. try:
  85. dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
  86. sql = f'select * from {project}.{table} where dt = {dt}'
  87. with odps.execute_sql(sql=sql).open_reader() as reader:
  88. data_count = reader.count
  89. except Exception as e:
  90. data_count = 0
  91. return data_count
  92. def get_rov_redis_key(now_date):
  93. """获取rov模型结果存放key"""
  94. redis_helper = RedisHelper()
  95. now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
  96. key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{now_dt}'
  97. if not redis_helper.key_exists(key_name=key_name):
  98. pre_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
  99. key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{pre_dt}'
  100. return key_name
  101. def get_feature_data(project, table, now_date):
  102. """获取特征数据"""
  103. dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
  104. # dt = '2022041310'
  105. records = get_data_from_odps(date=dt, project=project, table=table)
  106. feature_data = []
  107. for record in records:
  108. item = {}
  109. for feature_name in features:
  110. item[feature_name] = record[feature_name]
  111. feature_data.append(item)
  112. feature_df = pd.DataFrame(feature_data)
  113. return feature_df
  114. def cal_score(df):
  115. """
  116. 计算score
  117. :param df: 特征数据
  118. :return:
  119. """
  120. # score计算公式: sharerate*backrate*logback*ctr
  121. # sharerate = lastonehour_share/(lastonehour_play+1000)
  122. # backrate = lastonehour_return/(lastonehour_share+10)
  123. # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值:K2 = 0.6 if ctr > 0.6 else ctr
  124. # score = sharerate * backrate * LOG(lastonehour_return+1) * K2
  125. df = df.fillna(0)
  126. df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
  127. df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
  128. df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
  129. df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
  130. df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
  131. df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']
  132. df = df.sort_values(by=['score'], ascending=False)
  133. return df
  134. def video_rank(df, now_date, now_h, rule_key, param, region):
  135. """
  136. 获取符合进入召回源条件的视频,与每日更新的rov模型结果视频列表进行合并
  137. :param df:
  138. :param now_date:
  139. :param now_h:
  140. :param rule_key: 小时级数据进入条件
  141. :param param: 小时级数据进入条件参数
  142. :param region: 所属地域
  143. :return:
  144. """
  145. redis_helper = RedisHelper()
  146. # # 获取rov模型结果
  147. # key_name = get_rov_redis_key(now_date=now_date)
  148. # initial_data = redis_helper.get_data_zset_with_index(key_name=key_name, start=0, end=-1, with_scores=True)
  149. # log_.info(f'initial data count = {len(initial_data)}')
  150. # 获取符合进入召回源条件的视频,进入条件:小时级回流>=20 && score>=0.005
  151. return_count = param.get('return_count', 1)
  152. score_value = param.get('score_rule', 0)
  153. h_recall_df = df[(df['lastonehour_return'] >= return_count) & (df['score'] >= score_value)]
  154. # videoid重复时,保留分值高
  155. h_recall_df = h_recall_df.sort_values(by=['score'], ascending=False)
  156. h_recall_df = h_recall_df.drop_duplicates(subset=['videoid'], keep='first')
  157. h_recall_videos = h_recall_df['videoid'].to_list()
  158. log_.info(f'h_recall videos count = {len(h_recall_videos)}')
  159. # 写入对应的redis
  160. h_video_ids =[]
  161. h_recall_result = {}
  162. for video_id in h_recall_videos:
  163. score = h_recall_df[h_recall_df['videoid'] == video_id]['score']
  164. # print(score)
  165. h_recall_result[int(video_id)] = float(score)
  166. h_video_ids.append(int(video_id))
  167. h_recall_key_name = \
  168. f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  169. if len(h_recall_result) > 0:
  170. redis_helper.add_data_with_zset(key_name=h_recall_key_name, data=h_recall_result, expire_time=23 * 3600)
  171. # 清空线上过滤应用列表
  172. redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{rule_key}")
  173. # 去重更新rov模型结果,并另存为redis中
  174. # initial_data_dup = {}
  175. # for video_id, score in initial_data:
  176. # if int(video_id) not in h_video_ids:
  177. # initial_data_dup[int(video_id)] = score
  178. # log_.info(f"initial data dup count = {len(initial_data_dup)}")
  179. # initial_key_name = \
  180. # f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  181. # if len(initial_data_dup) > 0:
  182. # redis_helper.add_data_with_zset(key_name=initial_key_name, data=initial_data_dup, expire_time=23 * 3600)
  183. # 与其他召回视频池去重,存入对应的redis
  184. dup_to_redis(h_video_ids=h_video_ids, now_date=now_date, now_h=now_h, rule_key=rule_key, region=region)
  185. def dup_to_redis(h_video_ids, now_date, now_h, rule_key, region):
  186. """将地域分组小时级数据与其他召回视频池去重,存入对应的redis"""
  187. redis_helper = RedisHelper()
  188. # ##### 去重更新地域分组天级列表,并另存为redis中
  189. region_day_key_name = \
  190. f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_DAY}{region}.rule1." \
  191. f"{datetime.datetime.strftime(now_date, '%Y%m%d')}"
  192. if redis_helper.key_exists(key_name=region_day_key_name):
  193. region_day_data = redis_helper.get_data_zset_with_index(
  194. key_name=region_day_key_name, start=0, end=-1, with_scores=True)
  195. log_.info(f'region day data count = {len(region_day_data)}')
  196. region_day_dup = {}
  197. for video_id, score in region_day_data:
  198. if int(video_id) not in h_video_ids:
  199. region_day_dup[int(video_id)] = score
  200. h_video_ids.append(int(video_id))
  201. log_.info(f"region day data dup count = {len(region_day_dup)}")
  202. region_day_dup_key_name = \
  203. f"{config_.RECALL_KEY_NAME_PREFIX_DUP1_REGION_DAY_H}{region}.{rule_key}." \
  204. f"{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  205. if len(region_day_dup) > 0:
  206. redis_helper.add_data_with_zset(key_name=region_day_dup_key_name, data=region_day_dup, expire_time=23 * 3600)
  207. # ##### 去重小程序天级更新结果,并另存为redis中
  208. day_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_DAY}rule2.{datetime.datetime.strftime(now_date, '%Y%m%d')}"
  209. if redis_helper.key_exists(key_name=day_key_name):
  210. day_data = redis_helper.get_data_zset_with_index(
  211. key_name=day_key_name, start=0, end=-1, with_scores=True)
  212. log_.info(f'day data count = {len(day_data)}')
  213. day_dup = {}
  214. for video_id, score in day_data:
  215. if int(video_id) not in h_video_ids:
  216. day_dup[int(video_id)] = score
  217. h_video_ids.append(int(video_id))
  218. log_.info(f"day data dup count = {len(day_dup)}")
  219. day_dup_key_name = \
  220. f"{config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_DAY_H}{region}.{rule_key}." \
  221. f"{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  222. if len(day_dup) > 0:
  223. redis_helper.add_data_with_zset(key_name=day_dup_key_name, data=day_dup, expire_time=23 * 3600)
  224. # ##### 去重小程序模型更新结果,并另存为redis中
  225. model_key_name = get_rov_redis_key(now_date=now_date)
  226. model_data = redis_helper.get_data_zset_with_index(key_name=model_key_name, start=0, end=-1, with_scores=True)
  227. log_.info(f'model data count = {len(model_data)}')
  228. model_data_dup = {}
  229. for video_id, score in model_data:
  230. if int(video_id) not in h_video_ids:
  231. model_data_dup[int(video_id)] = score
  232. h_video_ids.append(int(video_id))
  233. log_.info(f"model data dup count = {len(model_data_dup)}")
  234. model_data_dup_key_name = \
  235. f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H}{region}.{rule_key}." \
  236. f"{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  237. if len(model_data_dup) > 0:
  238. redis_helper.add_data_with_zset(key_name=model_data_dup_key_name, data=model_data_dup, expire_time=23 * 3600)
  239. def rank_by_h(project, table, now_date, now_h, rule_params, region_code_list):
  240. # 获取特征数据
  241. feature_df = get_feature_data(project=project, table=table, now_date=now_date)
  242. # 获取所有的region
  243. # region_code_list = list(set(feature_df[''].to_list()))
  244. # rank
  245. for key, value in rule_params.items():
  246. log_.info(f"rule = {key}, param = {value}")
  247. for region in region_code_list:
  248. log_.info(f"region = {region}")
  249. # 计算score
  250. region_df = feature_df[feature_df['code'] == region]
  251. log_.info(f'region_df count = {len(region_df)}')
  252. score_df = cal_score(df=region_df)
  253. video_rank(df=score_df, now_date=now_date, now_h=now_h, rule_key=key, param=value, region=region)
  254. # to-csv
  255. # score_filename = f"score_{region}_{key}_{datetime.datetime.strftime(now_date, '%Y%m%d%H')}.csv"
  256. # score_df.to_csv(f'./data/{score_filename}')
  257. # to-logs
  258. # log_.info({"date": datetime.datetime.strftime(now_date, '%Y%m%d%H'),
  259. # "region_code": region,
  260. # "redis_key_prefix": config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H,
  261. # "rule_key": key,
  262. # "score_df": score_df[['videoid', 'score']]})
  263. def h_rank_bottom(now_date, now_h, rule_key, region_code_list):
  264. """未按时更新数据,用上一小时结果作为当前小时的数据"""
  265. log_.info(f"rule_key = {rule_key}")
  266. # 获取rov模型结果
  267. redis_helper = RedisHelper()
  268. if now_h == 0:
  269. redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
  270. redis_h = 23
  271. else:
  272. redis_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
  273. redis_h = now_h - 1
  274. key_prefix_list = [
  275. config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H,
  276. config_.RECALL_KEY_NAME_PREFIX_DUP1_REGION_DAY_H,
  277. config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_DAY_H,
  278. config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H
  279. ]
  280. # fea_df = get_feature_data(project=project, table=table, now_date=now_date - datetime.timedelta(hours=1))
  281. # region_list = list(set(fea_df[''].to_list()))
  282. for region in region_code_list:
  283. log_.info(f"region = {region}")
  284. for key_prefix in key_prefix_list:
  285. key_name = f"{key_prefix}{region}.{rule_key}.{redis_dt}.{redis_h}"
  286. initial_data = redis_helper.get_data_zset_with_index(key_name=key_name, start=0, end=-1, with_scores=True)
  287. final_data = dict()
  288. for video_id, score in initial_data:
  289. final_data[video_id] = score
  290. # 存入对应的redis
  291. final_key_name = \
  292. f"{key_prefix}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  293. if len(final_data) > 0:
  294. redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=23 * 3600)
  295. # 清空线上过滤应用列表
  296. redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{rule_key}")
  297. def h_timer_check():
  298. rule_params = config_.RULE_PARAMS_REGION
  299. project = config_.PROJECT_REGION
  300. table = config_.TABLE_REGION
  301. region_code_list = [code for region, code in region_code.items()]
  302. now_date = datetime.datetime.today()
  303. log_.info(f"now_date: {datetime.datetime.strftime(now_date, '%Y%m%d%H')}")
  304. now_h = datetime.datetime.now().hour
  305. now_min = datetime.datetime.now().minute
  306. if now_h == 0:
  307. for key, _ in rule_params.items():
  308. h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key, region_code_list=region_code_list)
  309. return
  310. # 查看当前小时更新的数据是否已准备好
  311. h_data_count = h_data_check(project=project, table=table, now_date=now_date)
  312. if h_data_count > 0:
  313. log_.info(f'h_data_count = {h_data_count}')
  314. # 数据准备好,进行更新
  315. rank_by_h(now_date=now_date, now_h=now_h, rule_params=rule_params,
  316. project=project, table=table, region_code_list=region_code_list)
  317. elif now_min > 50:
  318. log_.info('h_recall data is None, use bottom data!')
  319. for key, _ in rule_params.items():
  320. h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key, region_code_list=region_code_list)
  321. else:
  322. # 数据没准备好,1分钟后重新检查
  323. Timer(60, h_timer_check).start()
  324. if __name__ == '__main__':
  325. h_timer_check()