Repository: microsoft/RD-Agent
File: rdagent/components/coder/data_science/pipeline/eval.py (Python)

Code before:
            implementation.execute(env=env, entry=get_clear_ws_cmd())
            result = implementation.run(env=env, entry=f"python -m coverage run main.py --debug")
            if result.exit_code != 0:
                stdout += "Debug mode failed to run. Please check the stdout:\n"
                stdout += result.stdout
                run_full_data = False
            else:
                stdout += "Debug mode ran successfully.\n"
                debug_time, full_estimated_time = None, None
                if match := re.search(r"debug_time:\s*(\d+(?:.\d+)?)", result.stdout, re.DOTALL):
                    debug_time = float(match.group(1))
                if match := re.search(r"estimated_time:\s*(\d+(?:.\d+)?)", result.stdout, re.DOTALL):
                    full_estimated_time = float(match.group(1))
                if debug_time is not None and full_estimated_time is not None:
                    stdout += f"Debug mode ran in {debug_time:.2f} seconds, estimated full run time is {full_estimated_time:.2f} seconds.\n"
                    if full_estimated_time < env.conf.running_timeout_period * 3:
                        stdout += "The estimated full run time is less than three times the timeout period, proceeding with full data run.\n"
                    else:
                        stdout += f"The estimated full run time is more than three times the timeout period, skipping full data run.\nFollowing the stdout of the debug mode run:\n{result.stdout.strip()}\n"
                        run_full_data = False
                else:
                    stdout += (
                        "Debug mode did not provide debug_time or estimated_time, proceeding with full data run.\n"
                    )

        if run_full_data:
            # Clean the scores.csv & submission.csv.
            implementation.execute(env=env, entry=get_clear_ws_cmd())
            result = implementation.run(env=env, entry=f"python -m coverage run main.py")
            if not DS_RD_SETTING.sample_data:
                match = re.search(
                    r"(.*?)=== Start of EDA part ===(.*)=== End of EDA part ===", result.stdout, re.DOTALL
                )
                eda_output = match.groups()[1] if match else None
                if eda_output is None:
                    eda_output = "No EDA output."
                implementation.inject_files(**{"EDA.md": eda_output})
                implementation.running_info.running_time = result.running_time
            result.stdout = remove_eda_part(result.stdout)
            stdout += f"The code executed {'successfully' if result.exit_code == 0 else 'failed'} with following output:\n{result.stdout.strip()}\n"

        score_fp = implementation.workspace_path / "scores.csv"
        score_ret_code = 0
        score_check_text = ""
        if not score_fp.exists():
            score_check_text = "[Error] Metrics file (scores.csv) is not generated!"
            score_ret_code = 1
        else:
            try:
                score_df = pd.read_csv(score_fp, index_col=0)
                model_set_in_scores = set(score_df.index)

Review comment (suggestion):
I feel running and coding are tighly coupled now (we copy the running code from runner, and then add a branch to skip the running the the runner).
If we have to couple them,  we should share the code at least.

Code after:
            result = implementation.run(env=env, entry=f"python -m coverage run main.py --debug")
        else:
            result = implementation.run(env=env, entry=f"python -m coverage run main.py")
        execute_ret_code = result.exit_code
        result.stdout = remove_eda_part(result.stdout)
        if result.exit_code != 0:
            stdout += f"Code failed to run. Please check the stdout:\n Following the stdout of the debug mode run:\n{result.stdout.strip()}\n"
        else:
            stdout += f"Code ran successfully.\n Following the stdout of the debug mode run:\n{result.stdout.strip()}\n"
        if DS_RD_SETTING.sample_data_by_LLM:
            debug_time, full_estimated_time = None, None
            if match := re.search(r"debug_time:\s*(\d+(?:.\d+)?)", result.stdout, re.DOTALL):
                debug_time = float(match.group(1))
            if match := re.search(r"estimated_time:\s*(\d+(?:.\d+)?)", result.stdout, re.DOTALL):
                full_estimated_time = float(match.group(1))
            if debug_time is not None and full_estimated_time is not None:
                stdout += f"Debug mode ran in {debug_time:.2f} seconds, estimated full run time is {full_estimated_time:.2f} seconds.\n"
                if full_estimated_time < env.conf.running_timeout_period * 3:
                    stdout += "The estimated full run time is less than three times the timeout period.\n"
                else:
                    stdout += f"The estimated full run time is more than three times the timeout period.\n"
            else:
                stdout += "Debug mode did not provide debug_time or estimated_time, it's a buggy implementation.\n"

        score_fp = implementation.workspace_path / "scores.csv"
        score_ret_code = 0
        score_check_text = ""
        if not score_fp.exists():
            score_check_text = "[Error] Metrics file (scores.csv) is not generated!"
            score_ret_code = 1
        else:
            try:
                score_df = pd.read_csv(score_fp, index_col=0)
                model_set_in_scores = set(score_df.index)

                # Check model names (index)
                if not score_df.index.is_unique:
                    score_check_text += "\n[Error] The file 'scores.csv' contains duplicate model names."
                    score_ret_code = 1
                if "ensemble" not in model_set_in_scores:
                    score_check_text += "\n[Error] The file 'scores.csv' doesn't contain the ensemble model."
                    score_ret_code = 1
                if score_ret_code != 0:
                    score_check_text += f"The dataframe in file 'scores.csv' is:\n{score_df}"

                # Check metric name (columns)
                if score_df.columns.tolist() != [self.scen.metric_name]:
                    score_check_text += f"\n[Error] The scores dataframe does not contain the correct column names.\nCorrect columns is: ['{self.scen.metric_name}']\nBut got: {score_df.columns.tolist()}"
                    score_ret_code = 1

                # Check if scores contain NaN (values)