ad_users_data_update.py 4.6 KB

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  1. import datetime
  2. import traceback
  3. from threading import Timer
  4. from utils import RedisHelper, data_check, get_feature_data, send_msg_to_feishu
  5. from config import set_config
  6. from log import Log
  7. config_, _ = set_config()
  8. log_ = Log()
  9. redis_helper = RedisHelper()
  10. features = [
  11. 'apptype',
  12. 'group',
  13. 'sharerate_all',
  14. 'sharerate_ad'
  15. ]
  16. def predict_user_group_share_rate(user_group_initial_df, dt, data_key, data_param):
  17. """预估用户组对应的有广告时分享率"""
  18. # 获取对应的用户组特征
  19. user_group_df = user_group_initial_df.copy()
  20. user_group_df['apptype'] = user_group_df['apptype'].astype(int)
  21. user_group_df = user_group_df[user_group_df['apptype'] == data_param]
  22. if data_key == 'data1:1':
  23. user_group_df = user_group_df[~user_group_df['group'].isin(config_.NO_AD_MID_GROUP_LIST)]
  24. user_group_df['sharerate_all'].fillna(0, inplace=True)
  25. user_group_df['sharerate_ad'].fillna(0, inplace=True)
  26. user_group_df['sharerate_all'] = user_group_df['sharerate_all'].astype(float)
  27. user_group_df['sharerate_ad'] = user_group_df['sharerate_ad'].astype(float)
  28. # 获取有广告时所有用户组近30天的分享率
  29. ad_all_group_share_rate = user_group_df[user_group_df['group'] == 'allmids']['sharerate_ad'].values[0]
  30. user_group_df = user_group_df[user_group_df['group'] != 'allmids']
  31. # 计算用户组有广告时分享率
  32. user_group_df['group_ad_share_rate'] = \
  33. user_group_df['sharerate_ad'] * float(ad_all_group_share_rate) / user_group_df['sharerate_all']
  34. user_group_df['group_ad_share_rate'].fillna(0, inplace=True)
  35. # 结果写入redis
  36. key_name = f"{config_.KEY_NAME_PREFIX_AD_GROUP}{data_key}:{dt}"
  37. redis_data = {}
  38. for index, item in user_group_df.iterrows():
  39. redis_data[item['group']] = item['group_ad_share_rate']
  40. group_ad_share_rate_mean = user_group_df['group_ad_share_rate'].mean()
  41. redis_data['mean_group'] = group_ad_share_rate_mean
  42. if len(redis_data) > 0:
  43. redis_helper = RedisHelper()
  44. redis_helper.add_data_with_zset(key_name=key_name, data=redis_data, expire_time=2 * 24 * 3600)
  45. return user_group_df
  46. def update_users_data(project, table, dt, update_params):
  47. """预估用户组有广告时分享率"""
  48. # 获取用户组特征
  49. user_group_initial_df = get_feature_data(project=project, table=table, features=features, dt=dt)
  50. for data_key, data_param in update_params.items():
  51. log_.info(f"data_key = {data_key} update start...")
  52. predict_user_group_share_rate(user_group_initial_df=user_group_initial_df, dt=dt, data_key=data_key, data_param=data_param)
  53. log_.info(f"data_key = {data_key} update end!")
  54. def timer_check():
  55. try:
  56. update_params = config_.AD_USER_DATA_PARAMS
  57. project = config_.ad_model_data['users_share_rate'].get('project')
  58. table = config_.ad_model_data['users_share_rate'].get('table')
  59. now_date = datetime.datetime.today()
  60. dt = datetime.datetime.strftime(now_date, '%Y%m%d')
  61. log_.info(f"now_date: {dt}")
  62. now_min = datetime.datetime.now().minute
  63. # 查看当前更新的数据是否已准备好
  64. data_count = data_check(project=project, table=table, dt=dt)
  65. if data_count > 0:
  66. log_.info(f"ad user group data count = {data_count}")
  67. # 数据准备好,进行更新
  68. update_users_data(project=project, table=table, dt=dt, update_params=update_params)
  69. log_.info(f"ad user group data update end!")
  70. # elif now_min > 45:
  71. # log_.info('ad user group data is None!')
  72. # send_msg_to_feishu(
  73. # webhook=config_.FEISHU_ROBOT['server_robot'].get('webhook'),
  74. # key_word=config_.FEISHU_ROBOT['server_robot'].get('key_word'),
  75. # msg_text=f"rov-offline{config_.ENV_TEXT} - 用户组分享率数据未准备好!\n"
  76. # f"traceback: {traceback.format_exc()}"
  77. # )
  78. else:
  79. # 数据没准备好,1分钟后重新检查
  80. Timer(60, timer_check).start()
  81. except Exception as e:
  82. log_.error(f"用户组分享率预测数据更新失败, exception: {e}, traceback: {traceback.format_exc()}")
  83. send_msg_to_feishu(
  84. webhook=config_.FEISHU_ROBOT['server_robot'].get('webhook'),
  85. key_word=config_.FEISHU_ROBOT['server_robot'].get('key_word'),
  86. msg_text=f"rov-offline{config_.ENV_TEXT} - 用户组分享率预测数据更新失败\n"
  87. f"exception: {e}\n"
  88. f"traceback: {traceback.format_exc()}"
  89. )
  90. if __name__ == '__main__':
  91. timer_check()