Repository: microsoft/RD-Agent
File: rdagent/scenarios/data_science/dev/runner/eval.py (Python)

Code before:

            except Exception as e:
                logger.error(f"Error in checking the scores.csv file: {e}")
                score_check_text += f"\n[Error] in checking the scores.csv file: {e}\nscores.csv's content:\n-----\n{score_fp.read_text()}\n-----"
                score_ret_code = 1

        # DockerEnv for MLEBench submission validation
        submission_check_out = ""
        submission_ret_code = 0
        test_eval = get_test_eval()

        if test_eval.enabled(self.scen.competition):
            submission_check_out, submission_ret_code = test_eval.valid(self.scen.competition, implementation)
            stdout += f"\n### Submission check:\n{submission_check_out}\nIf Submission check returns a 'Submission is valid' or similar message, despite some warning messages, you should still consider the submission as valid and give a positive final decision. "

        # Whether to enable hyperparameter tuning check
        # 1. This is the first loop of evaluation.
        c1 = len(queried_knowledge.task_to_former_failed_traces[target_task.get_task_information()][0]) == 0

        # 2. The current time spent on runner is less than the time limit ratio for runner timeout.
        time_spent_ratio = implementation.running_info.running_time / env.conf.running_timeout_period
        c2 = time_spent_ratio < DS_RD_SETTING.time_ratio_limit_to_enable_hyperparameter_tuning

        # 3. Only enable hyperparameter tuning during the merge stage if configured.
        timer = RD_Agent_TIMER_wrapper.timer
        res_time = timer.remain_time()
        if DS_RD_SETTING.only_enable_tuning_in_merge:
            c3 = res_time <= timedelta(hours=DS_RD_SETTING.merge_hours)
        else:
            c3 = True

        # 4. The current time spent on global is less than the time limit ratio for whole timeout.
        res_ratio = res_time / timer.all_duration
        c4 = res_ratio <= DS_RD_SETTING.res_time_ratio_limit_to_enable_hyperparameter_tuning

        # Only enable hyperparameter tuning check if all conditions are met
        enable_hyperparameter_tuning_check = c1 and c2 and c3 and c4

        system_prompt = T(".prompts:DSCoSTEER_eval.system").r(
            scenario=self.scen.get_scenario_all_desc(eda_output=implementation.file_dict.get("EDA.md", None)),
            task_desc=target_task.get_task_information(),
            enable_hyperparameter_tuning_check=enable_hyperparameter_tuning_check,
        )
        user_prompt = T(".prompts:DSCoSTEER_eval.user").r(
            code=implementation.all_codes,
            change_summary=implementation.change_summary,
            stdout=shrink_text(stdout),
            time_spent=f"{implementation.running_info.running_time:.2f} seconds",
            timeout=f"{env.conf.running_timeout_period} seconds",
            percent_of_timeout_used=f"{time_spent_ratio * 100:.2f}%",
            queried_former_failed_knowledge=queried_former_failed_knowledge,

Review comment (suggestion):
```suggestion
        # TODO: it is not restricted in merge stage now for fast implementation.
        timer = RD_Agent_TIMER_wrapper.timer
```

Code after:

            except Exception as e:
                logger.error(f"Error in checking the scores.csv file: {e}")
                score_check_text += f"\n[Error] in checking the scores.csv file: {e}\nscores.csv's content:\n-----\n{score_fp.read_text()}\n-----"
                score_ret_code = 1

        # DockerEnv for MLEBench submission validation
        submission_check_out = ""
        submission_ret_code = 0
        test_eval = get_test_eval()

        if test_eval.enabled(self.scen.competition):
            submission_check_out, submission_ret_code = test_eval.valid(self.scen.competition, implementation)
            stdout += f"\n### Submission check:\n{submission_check_out}\nIf Submission check returns a 'Submission is valid' or similar message, despite some warning messages, you should still consider the submission as valid and give a positive final decision. "

        # Whether to enable hyperparameter tuning check
        # 1. This is the first loop of evaluation.
        c1 = len(queried_knowledge.task_to_former_failed_traces[target_task.get_task_information()][0]) == 0

        # 2. The current time spent on runner is less than the time limit ratio for runner timeout.
        time_spent_ratio = implementation.running_info.running_time / env.conf.running_timeout_period
        c2 = time_spent_ratio < DS_RD_SETTING.time_ratio_limit_to_enable_hyperparameter_tuning

        # 3. Only enable hyperparameter tuning during the merge stage if configured.
        # TODO: it is not restricted in merge stage now for fast implementation.
        timer = RD_Agent_TIMER_wrapper.timer
        res_time = timer.remain_time()
        if DS_RD_SETTING.only_enable_tuning_in_merge:
            c3 = res_time <= timedelta(hours=DS_RD_SETTING.merge_hours)
        else:
            c3 = True

        # 4. The current time spent on global is less than the time limit ratio for whole timeout.
        res_ratio = res_time / timer.all_duration
        c4 = res_ratio <= DS_RD_SETTING.res_time_ratio_limit_to_enable_hyperparameter_tuning

        # Only enable hyperparameter tuning check if all conditions are met
        enable_hyperparameter_tuning_check = c1 and c2 and c3 and c4

        system_prompt = T(".prompts:DSCoSTEER_eval.system").r(
            scenario=self.scen.get_scenario_all_desc(eda_output=implementation.file_dict.get("EDA.md", None)),
            task_desc=target_task.get_task_information(),
            enable_hyperparameter_tuning_check=enable_hyperparameter_tuning_check,
        )
        user_prompt = T(".prompts:DSCoSTEER_eval.user").r(
            code=implementation.all_codes,
            change_summary=implementation.change_summary,
            stdout=shrink_text(stdout),
            time_spent=f"{implementation.running_info.running_time:.2f} seconds",
            timeout=f"{env.conf.running_timeout_period} seconds",
            percent_of_timeout_used=f"{time_spent_ratio * 100:.2f}%",