update

ad_users_data_update.py

@@ -35,6 +35,8 @@ def predict_user_group_share_rate(project, table, dt, app_type):
     redis_data = {}
     for item in user_group_df:
         redis_data[item['group']] = item['group_ad_share_rate']
+    group_ad_share_rate_mean = user_group_df['group_ad_share_rate'].mean()
+    redis_data['mean_group'] = group_ad_share_rate_mean
     if len(redis_data) > 0:
         redis_helper = RedisHelper()
         redis_helper.add_data_with_zset(key_name=key_name, data=redis_data, expire_time=2 * 24 * 3600)

ad_video_data_upate.py

@@ -22,6 +22,7 @@ def predict_video_share_rate(project, table, dt, app_type):
     video_df = get_feature_data(project=project, table=table, features=features, dt=dt)
     video_df['apptype'] = video_df['apptype'].astype(int)
     video_df = video_df[video_df['apptype'] == app_type]
+    video_df['videoid'] = video_df['videoid'].astype(int)
     video_df['sharerate_all'] = video_df['sharerate_all'].astype(float)
     video_df['sharerate_ad'] = video_df['sharerate_ad'].astype(float)
     # 获取有广告时所有视频近30天的分享率
@@ -34,7 +35,9 @@ def predict_video_share_rate(project, table, dt, app_type):
     key_name = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{dt}"
     redis_data = {}
     for item in video_df:
-        redis_data[item['videoid']] = item['video_ad_share_rate']
+        redis_data[int(item['videoid'])] = item['video_ad_share_rate']
+    group_ad_share_rate_mean = video_df['video_ad_share_rate'].mean()
+    redis_data[-1] = group_ad_share_rate_mean
     if len(redis_data) > 0:
         redis_helper = RedisHelper()
         redis_helper.add_data_with_zset(key_name=key_name, data=redis_data, expire_time=2 * 24 * 3600)