Repository: microsoft/RD-Agent
File: rdagent/app/data_science/conf.py (Python)

Code before:
    ## Workflow Related
    consecutive_errors: int = 5

    ## Coding Related
    coding_fail_reanalyze_threshold: int = 3

    debug_timeout: int = 600
    """The timeout limit for running on debugging data"""
    full_timeout: int = 3600
    """The timeout limit for running on full data"""

    ### specific feature

    #### enable specification
    spec_enabled: bool = True

    #### proposal related
    proposal_version: str = "v1"
    coder_on_whole_pipeline: bool = False
    max_trace_hist: int = 3

    coder_max_loop: int = 10
    runner_max_loop: int = 1

    rule_base_eval: bool = False
    sample_data_by_LLM: bool = True
    use_raw_description: bool = False
    show_nan_columns: bool = False

    #### model dump
    enable_model_dump: bool = False
    enable_doc_dev: bool = False
    model_dump_check_level: Literal["medium", "high"] = "medium"

    ### knowledge base
    enable_knowledge_base: bool = False
    knowledge_base_version: str = "v1"
    knowledge_base_path: str | None = None
    idea_pool_json_path: str | None = None

    ### archive log folder after each loop
    enable_log_archive: bool = True
    log_archive_path: str | None = None
    log_archive_temp_path: str | None = (
        None  # This is to store the mid tar file since writing the tar file is preferred in local storage then copy to target storage
    )

    #### Evaluation on Test related
    eval_sub_dir: str = "eval"  # TODO: fixme, this is not a good name
    """We'll use f"{DS_RD_SETTING.local_data_path}/{DS_RD_SETTING.eval_sub_dir}/{competition}"
    to find the scriipt to evaluate the submission on test"""

Review comment (suggestion):
```suggestion
    sample_data_by_LLM: bool = True
    """In coding phase, the system will sample data for better efficency(always enabled).
    if sample_data_by_LLM is True, the coder will leverage LLM's intelligence,
    Otherwise, rule-based data-sampling strategy will be used.
    """
```

Code after:
    ## Workflow Related
    consecutive_errors: int = 5

    ## Coding Related
    coding_fail_reanalyze_threshold: int = 3

    debug_timeout: int = 600
    """The timeout limit for running on debugging data"""
    full_timeout: int = 3600
    """The timeout limit for running on full data"""

    ### specific feature

    #### enable specification
    spec_enabled: bool = True

    #### proposal related
    proposal_version: str = "v1"
    coder_on_whole_pipeline: bool = False
    max_trace_hist: int = 3

    coder_max_loop: int = 10
    runner_max_loop: int = 1

    rule_base_eval: bool = False
    sample_data_by_LLM: bool = False
    use_raw_description: bool = False
    show_nan_columns: bool = False

    #### model dump
    enable_model_dump: bool = False
    enable_doc_dev: bool = False
    model_dump_check_level: Literal["medium", "high"] = "medium"

    ### knowledge base
    enable_knowledge_base: bool = False
    knowledge_base_version: str = "v1"
    knowledge_base_path: str | None = None
    idea_pool_json_path: str | None = None

    ### archive log folder after each loop
    enable_log_archive: bool = True
    log_archive_path: str | None = None
    log_archive_temp_path: str | None = (
        None  # This is to store the mid tar file since writing the tar file is preferred in local storage then copy to target storage
    )

    #### Evaluation on Test related
    eval_sub_dir: str = "eval"  # TODO: fixme, this is not a good name
    """We'll use f"{DS_RD_SETTING.local_data_path}/{DS_RD_SETTING.eval_sub_dir}/{competition}"
    to find the scriipt to evaluate the submission on test"""