region_rule_rank_h.py 11 KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282
  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. }
  52. features = [
  53. 'code',
  54. 'videoid',
  55. 'lastonehour_preview', # 过去1小时预曝光人数
  56. 'lastonehour_view', # 过去1小时曝光人数
  57. 'lastonehour_play', # 过去1小时播放人数
  58. 'lastonehour_share', # 过去1小时分享人数
  59. 'lastonehour_return', # 过去1小时分享,过去1小时回流人数
  60. 'lastonehour_preview_total', # 过去1小时预曝光次数
  61. 'lastonehour_view_total', # 过去1小时曝光次数
  62. 'lastonehour_play_total', # 过去1小时播放次数
  63. 'lastonehour_share_total', # 过去1小时分享次数
  64. ]
  65. def get_region_code(region):
  66. """获取省份对应的code"""
  67. mysql_helper = MysqlHelper(mysql_info=config_.MYSQL_INFO)
  68. sql = f"SELECT ad_code FROM region_adcode WHERE parent_id = 0 AND region LIKE '{region}%';"
  69. ad_code = mysql_helper.get_data(sql=sql)
  70. return ad_code[0][0]
  71. def h_data_check(project, table, now_date):
  72. """检查数据是否准备好"""
  73. odps = ODPS(
  74. access_id=config_.ODPS_CONFIG['ACCESSID'],
  75. secret_access_key=config_.ODPS_CONFIG['ACCESSKEY'],
  76. project=project,
  77. endpoint=config_.ODPS_CONFIG['ENDPOINT'],
  78. connect_timeout=3000,
  79. read_timeout=500000,
  80. pool_maxsize=1000,
  81. pool_connections=1000
  82. )
  83. try:
  84. dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
  85. sql = f'select * from {project}.{table} where dt = {dt}'
  86. with odps.execute_sql(sql=sql).open_reader() as reader:
  87. data_count = reader.count
  88. except Exception as e:
  89. data_count = 0
  90. return data_count
  91. def get_rov_redis_key(now_date):
  92. """获取rov模型结果存放key"""
  93. redis_helper = RedisHelper()
  94. now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
  95. key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{now_dt}'
  96. if not redis_helper.key_exists(key_name=key_name):
  97. pre_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
  98. key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{pre_dt}'
  99. return key_name
  100. def get_feature_data(project, table, now_date):
  101. """获取特征数据"""
  102. dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
  103. # dt = '2022041310'
  104. records = get_data_from_odps(date=dt, project=project, table=table)
  105. feature_data = []
  106. for record in records:
  107. item = {}
  108. for feature_name in features:
  109. item[feature_name] = record[feature_name]
  110. feature_data.append(item)
  111. feature_df = pd.DataFrame(feature_data)
  112. return feature_df
  113. def cal_score(df):
  114. """
  115. 计算score
  116. :param df: 特征数据
  117. :return:
  118. """
  119. # score计算公式: sharerate*backrate*logback*ctr
  120. # sharerate = lastonehour_share/(lastonehour_play+1000)
  121. # backrate = lastonehour_return/(lastonehour_share+10)
  122. # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值:K2 = 0.6 if ctr > 0.6 else ctr
  123. # score = sharerate * backrate * LOG(lastonehour_return+1) * K2
  124. df = df.fillna(0)
  125. df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
  126. df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10)
  127. df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
  128. df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
  129. df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
  130. df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']
  131. df = df.sort_values(by=['score'], ascending=False)
  132. return df
  133. def video_rank(df, now_date, now_h, rule_key, param, region):
  134. """
  135. 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
  136. :param df:
  137. :param now_date:
  138. :param now_h:
  139. :param rule_key: 小时级数据进入条件
  140. :param param: 小时级数据进入条件参数
  141. :param region: 所属地域
  142. :return:
  143. """
  144. # 获取rov模型结果
  145. redis_helper = RedisHelper()
  146. key_name = get_rov_redis_key(now_date=now_date)
  147. initial_data = redis_helper.get_data_zset_with_index(key_name=key_name, start=0, end=-1, with_scores=True)
  148. log_.info(f'initial data count = {len(initial_data)}')
  149. # 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
  150. return_count = param.get('return_count')
  151. score_value = param.get('score_rule')
  152. h_recall_df = df[(df['lastonehour_return'] >= return_count) & (df['score'] >= score_value)]
  153. h_recall_videos = h_recall_df['videoid'].to_list()
  154. log_.info(f'h_recall videos count = {len(h_recall_videos)}')
  155. # 写入对应的redis
  156. h_video_ids =[]
  157. h_recall_result = {}
  158. for video_id in h_recall_videos:
  159. score = h_recall_df[h_recall_df['videoid'] == video_id]['score']
  160. h_recall_result[int(video_id)] = float(score)
  161. h_video_ids.append(int(video_id))
  162. h_recall_key_name = \
  163. f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  164. if len(h_recall_result) > 0:
  165. redis_helper.add_data_with_zset(key_name=h_recall_key_name, data=h_recall_result, expire_time=23 * 3600)
  166. # 清空线上过滤应用列表
  167. redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{rule_key}")
  168. # 去重更新rov模型结果,并另存为redis中
  169. initial_data_dup = {}
  170. for video_id, score in initial_data:
  171. if int(video_id) not in h_video_ids:
  172. initial_data_dup[int(video_id)] = score
  173. log_.info(f"initial data dup count = {len(initial_data_dup)}")
  174. initial_key_name = \
  175. f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  176. if len(initial_data_dup) > 0:
  177. redis_helper.add_data_with_zset(key_name=initial_key_name, data=initial_data_dup, expire_time=23 * 3600)
  178. def rank_by_h(project, table, now_date, now_h, rule_params, region_code_list):
  179. # 获取特征数据
  180. feature_df = get_feature_data(project=project, table=table, now_date=now_date)
  181. # 获取所有的region
  182. # region_code_list = list(set(feature_df[''].to_list()))
  183. # rank
  184. for key, value in rule_params.items():
  185. log_.info(f"rule = {key}, param = {value}")
  186. for region in region_code_list:
  187. log_.info(f"region = {region}")
  188. # 计算score
  189. region_df = feature_df[feature_df['code'] == region]
  190. score_df = cal_score(df=region_df)
  191. video_rank(df=score_df, now_date=now_date, now_h=now_h, rule_key=key, param=value, region=region)
  192. # to-csv
  193. score_filename = f"score_{region}_{key}_{datetime.datetime.strftime(now_date, '%Y%m%d%H')}.csv"
  194. score_df.to_csv(f'./data/{score_filename}')
  195. def h_rank_bottom(now_date, now_h, rule_key, region_code_list):
  196. """未按时更新数据,用上一小时结果作为当前小时的数据"""
  197. log_.info(f"rule_key = {rule_key}")
  198. # 获取rov模型结果
  199. redis_helper = RedisHelper()
  200. if now_h == 0:
  201. redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
  202. redis_h = 23
  203. else:
  204. redis_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
  205. redis_h = now_h - 1
  206. key_prefix_list = [config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H, config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H]
  207. # fea_df = get_feature_data(project=project, table=table, now_date=now_date - datetime.timedelta(hours=1))
  208. # region_list = list(set(fea_df[''].to_list()))
  209. for region in region_code_list:
  210. log_.info(f"region = {region}")
  211. for key_prefix in key_prefix_list:
  212. key_name = f"{key_prefix}{region}.{rule_key}.{redis_dt}.{redis_h}"
  213. initial_data = redis_helper.get_data_zset_with_index(key_name=key_name, start=0, end=-1, with_scores=True)
  214. final_data = dict()
  215. for video_id, score in initial_data:
  216. final_data[video_id] = score
  217. # 存入对应的redis
  218. final_key_name = \
  219. f"{key_prefix}{region}.{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
  220. if len(final_data) > 0:
  221. redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=23 * 3600)
  222. # 清空线上过滤应用列表
  223. redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{rule_key}")
  224. def h_timer_check():
  225. rule_params = config_.RULE_PARAMS_REGION
  226. project = config_.PROJECT_REGION
  227. table = config_.TABLE_REGION
  228. region_code_list = [code for region, code in region_code.items()]
  229. now_date = datetime.datetime.today()
  230. log_.info(f"now_date: {datetime.datetime.strftime(now_date, '%Y%m%d%H')}")
  231. now_h = datetime.datetime.now().hour
  232. now_min = datetime.datetime.now().minute
  233. if now_h == 0:
  234. for key, _ in rule_params.items():
  235. h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key, region_code_list=region_code_list)
  236. return
  237. # 查看当前小时更新的数据是否已准备好
  238. h_data_count = h_data_check(project=project, table=table, now_date=now_date)
  239. if h_data_count > 0:
  240. log_.info(f'h_data_count = {h_data_count}')
  241. # 数据准备好,进行更新
  242. rank_by_h(now_date=now_date, now_h=now_h, rule_params=rule_params,
  243. project=project, table=table, region_code_list=region_code_list)
  244. elif now_min > 50:
  245. log_.info('h_recall data is None, use bottom data!')
  246. for key, _ in rule_params.items():
  247. h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key, region_code_list=region_code_list)
  248. else:
  249. # 数据没准备好,1分钟后重新检查
  250. Timer(60, h_timer_check).start()
  251. if __name__ == '__main__':
  252. h_timer_check()