modify: demo

metric_test.py

@@ -30,7 +30,7 @@ class A(MetricBase):
 if __name__ == "__main__":
     # f_register_metric_func(A)
 
-    data_loader = DataLoaderHive(DbConfigEntity.from_config("./config/hive_config.json"))
+    data_loader = DataLoaderHive(DbConfigEntity.from_config("config/demo/hive_config.json"))
     df = data_loader.get_data("select * from pokes")
     print(df.head())
 

metric_test3.py

@@ -26,7 +26,7 @@ class BMetric(MetricBase):
 
 if __name__ == "__main__":
     f_register_metric_func(BMetric)
-    data_loader = DataLoaderMysql(DbConfigEntity.from_config("./config/mysql_config.json"))
-    monitor_metric = MonitorMetric("./config/monitor_config_template_excel.json")
+    data_loader = DataLoaderMysql(DbConfigEntity.from_config("config/demo/mysql_config.json"))
+    monitor_metric = MonitorMetric("config/demo/monitor_config_template_excel.json")
     monitor_metric.calculate_metric(data_loader=data_loader)
     monitor_metric.generate_report()

train_test.py

@@ -27,7 +27,7 @@ if __name__ == "__main__":
     data = DataSplitEntity(train_data=dat[:709], test_data=dat[709:])
 
     # 训练并生成报告
-    train_pipeline = Pipeline(MlConfigEntity.from_config('./config/ml_config_template.json'), data)
+    train_pipeline = Pipeline(MlConfigEntity.from_config('config/demo/ml_config_template.json'), data)
     # 特征处理
     cfg = {
         "project_name": "demo",