fix: fix some bugs (ensemble output, HPO, model tuning) (#648)

rdagent/components/coder/data_science/ensemble/eval_tests/ensemble_test.txt

@@ -68,6 +68,8 @@ for key in val_preds_dict.keys():
     else:
         print(f"Model {key} test predictions (test_preds_dict[key]) shape: {test_preds_dict[key].shape}")
 
+print(f"val_y.shape: {val_y.shape}" if not isinstance(val_y, list) else f"val_y(list)'s length: {len(val_y)}")
+
 # Run ensemble
 final_pred = ensemble_workflow(test_preds_dict, val_preds_dict, val_y)
 

rdagent/components/coder/data_science/model/prompts.yaml

@@ -39,8 +39,7 @@ model_coder:
     --------- Feature Engineering Code ---------
     {{ feature_code }}
     2. You should avoid using logging module to output information in your generated code, and instead use the print() function.
-    3. You can decide whether to use AutoML based on the characteristics of the task.
-    4. If the model can both be implemented by PyTorch and Tensorflow, please use pytorch for broader compatibility.
+    3. If the model can both be implemented by PyTorch and Tensorflow, please use pytorch for broader compatibility.
 
     ## Output Format
     {% if out_spec %}