fix none

ad_recommend.py

@@ -312,23 +312,21 @@ def predict_mid_video_res_with_model(now_date, mid, video_id, abtest_param, abte
     user_score = redis_helper.get_data_from_redis(key_name=user_key_name)
     item_score = redis_helper.get_data_from_redis(key_name=item_key_name)
 
-    if user_score is None:
-        if use_mean is not None and use_mean > 0:
-            user_score = redis_helper.get_data_from_redis(key_name=user_key_name_mean)
-        else:
-            user_score = 0.0
+    if user_score is None and use_mean == 'true':
+        user_score = redis_helper.get_data_from_redis(key_name=user_key_name_mean)
 
-    if item_score is None:
-        if use_mean is not None and use_mean > 0:
-            item_score = redis_helper.get_data_from_redis(key_name=item_key_name_mean)
-        else:
-            item_score = 0.0
+    if item_score is None and use_mean == 'true':
+        item_score = redis_helper.get_data_from_redis(key_name=item_key_name_mean)
 
     if user_score is None:
         user_score = 0.0
+    else:
+        user_score = float(user_score)
 
     if item_score is None:
         item_score = 0.0
+    else:
+        item_score = float(item_score)
 
     offline_score = user_score + item_score
 
@@ -341,7 +339,7 @@ def predict_mid_video_res_with_model(now_date, mid, video_id, abtest_param, abte
     final_score, online_score = get_final_score(online_features, offline_score)
 
     # 获取对应的阈值
-    threshold = redis_helper.get_data_from_redis(key_name=threshold_key)
+    threshold = float(redis_helper.get_data_from_redis(key_name=threshold_key))
 
     # 阈值判断
     if final_score > threshold: