feat(kaggle): several update in kaggle scenarios (#476)

* udpate plot

* log and reduce token

* trace tag

* add simple_background parameter to get_scenario_all_desc

* update trace

* update first version code

* chat model map

* add annotation for stack index

* add annotation

* reformatted by black

* several update on kaggle scenarios

* update some new change

* fix CI

* fix CI

* fix a bug

* fix bugs in graph RAG

---------

Co-authored-by: Tim <illking@foxmail.com>

rdagent/components/coder/model_coder/CoSTEER/evaluators.py

@@ -83,7 +83,7 @@ class ModelCodeEvaluator(Evaluator):
             .from_string(evaluate_prompts["evaluator_code_feedback"]["system"])
             .render(
                 scenario=(
-                    self.scen.get_scenario_all_desc(target_task)
+                    self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
                     if self.scen is not None
                     else "No scenario description."
                 )
@@ -145,7 +145,7 @@ class ModelFinalEvaluator(Evaluator):
             .from_string(evaluate_prompts["evaluator_final_feedback"]["system"])
             .render(
                 scenario=(
-                    self.scen.get_scenario_all_desc(target_task)
+                    self.scen.get_scenario_all_desc(target_task, filtered_tag=target_task.model_type)
                     if self.scen is not None
                     else "No scenario description."
                 )

rdagent/components/coder/model_coder/CoSTEER/evolving_strategy.py

@@ -76,7 +76,7 @@ class ModelCoderEvolvingStrategy(EvolvingStrategy):
                     coder_prompts["evolving_strategy_model_coder"]["system"],
                 )
                 .render(
-                    scenario=self.scen.get_scenario_all_desc(),
+                    scenario=self.scen.get_scenario_all_desc(filtered_tag=target_task.model_type),
                     queried_former_failed_knowledge=queried_former_failed_knowledge_to_render,
                     current_code=current_code,
                 )

rdagent/components/coder/model_coder/model_execute_template_v2.txt

@@ -13,9 +13,11 @@ valid_y = pd.Series(np.random.randint(0, 2, 8))
 
 model = fit(train_X, train_y, valid_X, valid_y)
 execution_model_output = predict(model, valid_X)
+
 if isinstance(execution_model_output, torch.Tensor):
     execution_model_output = execution_model_output.cpu().detach().numpy()
 
+
 execution_feedback_str = f"Execution successful, output numpy ndarray shape: {execution_model_output.shape}"
 
 pickle.dump(execution_model_output, open("execution_model_output.pkl", "wb"))