update ad threshold: mean/24*13 -> mean/48*27

ad_user_video_predict.py

@@ -87,9 +87,9 @@ def predict_ad_group_video():
     for index, item in group_df.iterrows():
         predict_df[item['group']] = predict_df['video_ad_share_rate'] * item['group_ad_share_rate']
         # 获取分组对应的均值作为阈值
-        threshold_data[item['group']] = predict_df[item['group']].mean() / 24 * 13
+        threshold_data[item['group']] = predict_df[item['group']].mean() / 48 * 27
         all_group_data.extend(predict_df[item['group']].tolist())
-    threshold_data['mean_group'] = np.mean(all_group_data) / 24 * 13
+    threshold_data['mean_group'] = np.mean(all_group_data) / 48 * 27
     log_.info(f"threshold_data = {threshold_data}")
     # 将阈值写入redis
     for key, val in threshold_data.items():