测试

ad/pai_flow_operator2.py

@@ -53,7 +53,7 @@ def get_data_from_odps(project, table, num):
             if reader.count < num:
                 return None
             # 获取字段名称
-            column_names = reader.schema.names
+            column_names = [col.name for col in reader.get_schema().columns]
             # 获取查询结果数据
             data = []
             for record in reader:
@@ -564,6 +564,7 @@ def validate_model_data_accuracy(start_time):
         # 对指定列取绝对值再求和
         old_abs_avg = df['old_error'].abs().sum() / num
         new_abs_avg = df['new_error'].abs().sum() / num
+        print(old_abs_avg, new_abs_avg)
         new_auc = get_dict_from_odps('pai_algo', table_dict['二分类评估-1'])['AUC']
         old_auc = get_dict_from_odps('pai_algo', table_dict['二分类评估-2'])['AUC']
         bizdate = get_previous_days_date(1)
@@ -598,17 +599,19 @@ def validate_model_data_accuracy(start_time):
         msg += f"\n\t - 新模型AUC: {new_auc}"
         msg += f"\n\t - 老模型Top10差异平均值: {old_abs_avg}"
         msg += f"\n\t - 新模型Top10差异平均值: {new_abs_avg}"
-        _monitor(level, msg, start_time, elapsed, top10_msg)
+        print(level, msg, start_time, elapsed, top10_msg)
 
 
 if __name__ == '__main__':
     start_time = int(time.time())
-    # 1.更新工作流
-    update_online_flow()
-    # 2.训练模型
-    train_res = train_model()
-    if train_res:
-        # 3. 验证模型数据 & 更新模型到线上
-        validate_model_data_accuracy(start_time)
-    else:
-        print('train_model_error')
+    validate_model_data_accuracy(start_time)
+    # start_time = int(time.time())
+    # # 1.更新工作流
+    # update_online_flow()
+    # # 2.训练模型
+    # train_res = train_model()
+    # if train_res:
+    #     # 3. 验证模型数据 & 更新模型到线上
+    #     validate_model_data_accuracy(start_time)
+    # else:
+    #     print('train_model_error')