feat: variable printing tool of data_science coder testing (#658)

* update debug_info printing

* refine infomation printing

* update ensemble evaluator prompt

* substitute repr with reprlib.repr

* strip comments

rdagent/components/coder/data_science/model/eval_tests/model_test.txt

@@ -25,10 +25,32 @@ print(f"train_y.shape: {train_y.shape}" if not isinstance(train_y, list) else f"
 print(f"val_X.shape: {val_X.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)}")
 
+import sys
+import reprlib
+def debug_info_print(func):
+    aRepr = reprlib.Repr()
+    aRepr.maxother=300
+    def wrapper(*args, **kwargs):
+        def local_trace(frame, event, arg):
+            if event == "return" and frame.f_code == func.__code__:
+                print("\n" + "="*20 + "Running model training code, local variable values:" + "="*20)
+                for k, v in frame.f_locals.items():
+                    printed = aRepr.repr(v)
+                    print(f"{k}:\n {printed}")
+                print("="*20 + "Local variable values end" + "="*20)
+            return local_trace
+        
+        sys.settrace(local_trace)
+        try:
+            return func(*args, **kwargs)
+        finally:
+            sys.settrace(None)
+    return wrapper
+
 # First execution
 print("The first execution begins.\n")
 start_time = time.time()
-val_pred, test_pred, hypers = model_workflow(
+val_pred, test_pred, hypers = debug_info_print(model_workflow)(
     X=train_X,
     y=train_y,
     val_X=val_X,
@@ -40,7 +62,7 @@ log_execution_results(start_time, val_pred, test_pred, hypers, "The first execut
 # Second execution
 print("The second execution begins.\n")
 start_time = time.time()
-val_pred, test_pred, final_hypers = model_workflow(
+val_pred, test_pred, final_hypers = debug_info_print(model_workflow)(
     X=train_X,
     y=train_y,
     val_X=None,