udpate

recommend.py

@@ -329,7 +329,8 @@ def video_recommend(request_id, mid, uid, size, top_K, flow_pool_P, app_type, al
             test_config = pool_recall.get_simrecall_config()
             flow_config = pool_recall.get_simrecall_flow_config()
         #print("test config:",test_config)
-        rank_result = video_sanke_rank(data=data, size=size, top_K=top_K, flow_pool_P=float(flow_pool_P), ab_Code=ab_code, exp_config=test_config, flowConfig=flow_config)
+        rank_result, flow_result = video_sanke_rank(data=data, size=size, top_K=top_K, flow_pool_P=float(flow_pool_P), ab_Code=ab_code, exp_config=test_config, flowConfig=flow_config)
+        result['flow_num'] = flow_result
     else:
         rank_result = video_rank(data=data, size=size, top_K=top_K, flow_pool_P=float(flow_pool_P))
 

video_rank.py

@@ -664,6 +664,7 @@ def video_new_rank2(data, size, top_K, flow_pool_P, ab_code, flowConfig=None):
                 rank_result.append(rov_recall_rank[0])
             elif len(flow_recall_rank) > 0:
                 rank_result.append(flow_recall_rank[0])
+        print("flow_result:", flow_result)
     else:
         # 按概率 p 及score排序获取 size - k 个视频
         i = 0
@@ -801,6 +802,7 @@ def video_sanke_rank(data, size, top_K, flow_pool_P, ab_Code='', exp_config=None
                 rank_result.append(rov_recall_rank[0])
             elif len(flow_recall_rank)>0:
                 rank_result.append(flow_recall_rank[0])
+        print("flow_result:", flow_result)
     else:
         # 按概率 p 及score排序获取 size - k 个视频
         i = 0