change sql for test

diaryQueueUpdate.py

@@ -204,16 +204,17 @@ def update_sql_dairy_queue(queue_name, diary_id):
 
 
 def multi_update(key, name_dict,native_queue_list):
-    predict(key, name_dict[key])
-    score_df = save_result(key, name_dict[key])
-    score_df = merge_score(name_dict[key], score_df)
-    diary_id = update_dairy_queue(score_df)
-
-    if get_native_queue(device_id) == native_queue_list:
-        update_sql_dairy_queue(key, diary_id)
-        print("更新结束")
-    else:
-        print("不需要更新日记队列")
+    if name_dict[key] != []:
+        predict(key, name_dict[key])
+        score_df = save_result(key, name_dict[key])
+        score_df = merge_score(name_dict[key], score_df)
+        diary_id = update_dairy_queue(score_df)
+
+        if get_native_queue(device_id) == native_queue_list:
+            update_sql_dairy_queue(key, diary_id)
+            print("更新结束")
+        else:
+            print("不需要更新日记队列")
 
 
 def user_update(device_id):
@@ -227,27 +228,32 @@ def user_update(device_id):
     pool.join()
 
 if __name__ == "__main__":
-    while True:
-        empty,device_id_list = get_active_users()
-        if empty:
-            for eachFile in os.listdir("/tmp"):
-                if "xlearn" in eachFile:
-                    os.remove("/tmp" + "/" + eachFile)
-            time.sleep(58)
-        else:
-            old_device_id_list = pd.read_csv(DIRECTORY_PATH + "data_set_device_id.csv")["device_id"].values.tolist()
-            # 求活跃用户和老用户的交集，也就是只预测老用户
-            predict_list = list(set(device_id_list) & set(old_device_id_list))
-            # 只预测尾号是6的ID，这块也可以在数据库取数据时过滤一下
-            # predict_list = list(filter(lambda x:str(x)[-1] == "6", predict_list))
-            start = time.time()
-            warnings.filterwarnings("ignore")
-            data_set_cid = pd.read_csv(DIRECTORY_PATH + "data_set_cid.csv")["cid"].values.tolist()
-            for device_id in predict_list:
-                user_update(device_id)
-
-            end = time.time()
-            print(end - start)
+    # while True:
+    empty,device_id_list = get_active_users()
+    if empty:
+        for eachFile in os.listdir("/tmp"):
+            if "xlearn" in eachFile:
+                os.remove("/tmp" + "/" + eachFile)
+        time.sleep(58)
+    else:
+        old_device_id_list = pd.read_csv(DIRECTORY_PATH + "data_set_device_id.csv")["device_id"].values.tolist()
+        # 求活跃用户和老用户的交集，也就是只预测老用户
+        predict_list = list(set(device_id_list) & set(old_device_id_list))
+        # 只预测尾号是6的ID，这块也可以在数据库取数据时过滤一下
+        # predict_list = list(filter(lambda x:str(x)[-1] == "6", predict_list))
+        start = time.time()
+        warnings.filterwarnings("ignore")
+        data_set_cid = pd.read_csv(DIRECTORY_PATH + "data_set_cid.csv")["cid"].values.tolist()
+        for device_id in predict_list:
+            user_update(device_id)
+
+        end = time.time()
+        print(end - start)
+
+
+
+
+
 
     # # TODO 上线后把预测用户改成多进程预测
     # data_set_cid = pd.read_csv(DIRECTORY_PATH + "data_set_cid.csv")["cid"].values.tolist()