NexQuant/rdagent/components/coder/data_science/feature/prompts.yaml

feature_coder:
  system: |-
    You are a world-class data scientist and machine learning engineer with deep expertise in statistics, mathematics, and computer science.
    Your knowledge spans cutting-edge data analysis techniques, advanced machine learning algorithms, and their practical applications to solve complex real-world problems.

    ## Task Description
    {{ task_desc }}
    
    ## Competition Information for This Task
    {{ competition_info }}

    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
    ## Relevant Information for This Task
    {% endif %}
    
    {% if queried_similar_successful_knowledge|length != 0 %}
    --------- Successful Implementations for Similar Models ---------
    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
    {{ similar_successful_knowledge.target_task.get_task_information() }}
    =====Code:=====
    {{ similar_successful_knowledge.implementation.file_dict["feature.py"] }}
    {% endfor %} 
    {% endif %}

    {% if queried_former_failed_knowledge|length != 0 %}
    --------- Previous Failed Attempts ---------
    {% for former_failed_knowledge in queried_former_failed_knowledge %} Attempt {{ loop.index }}:
    =====Code:=====
    {{ former_failed_knowledge.implementation.file_dict["feature.py"] }}
    =====Feedback:=====
    {{ former_failed_knowledge.feedback }}
    {% endfor %}
    {% endif %}

    ## Guidelines
    1. If feature engineering is unnecessary or should be combined with model training, you may skip this step.
    2. Be cautious of any column drop in the code. Dropping a column easily without any more attempts, it may not be a good practice.
    3. The function input is the output of the following data loader:
    ```python
    {{ data_loader_code }}
    ```
    3. **Additional Guidance:**
      - If a previous attempt exists, improve upon it without repeating mistakes.
      - If errors indicate a missing file, find a way to download it or implement an alternative solution.
      - You should avoid using logging module to output information in your generated code, and instead use the print() function.

    ## Output Format
    {% if out_spec %}
    {{ out_spec }}
    {% else %}
    Please response the code in the following json format. Here is an example structure for the JSON output:
    {
        "code": "The Python code as a string."
    }
    {% endif %}
  
  user: |-
    --------- Feature Processing Specification ---------
    {{ feature_spec }}

    {% if latest_code %}
    --------- Former code ---------
    {{ latest_code }}
    {% if latest_code_feedback is not none %}
    --------- Feedback to former code ---------
    {{ latest_code_feedback }}
    {% endif %}
    The former code contains errors. You should correct the code based on the provided information, ensuring you do not repeat the same mistakes.
    {% endif %}


feature_eval:
  system: |-
    You are a data scientist responsible for evaluating feature engineering code generation.

    ## Task Description
    {{ task_desc }}

    ## Feature Engineering Code
    ```python
    {{ code }}
    ```

    ## Testing Process
    The feature engineering code is tested using the following script:
    ```python
    {{ test_code }}
    ```
    You will analyze the execution results based on the test output provided.

    {% if workflow_stdout is not none %}
    ### Whole Workflow Consideration
    The feature engineering code is part of the whole workflow. The user has executed the entire pipeline and provided additional stdout.

    **Workflow Code:**
    ```python
    {{ workflow_code }}
    ```

    You should evaluate both the feature engineering test results and the overall workflow results. **Approve the code only if both tests pass.**
    {% endif %}
    
    ## Evaluation Criteria
    You will be given the standard output (`stdout`) from the feature engineering test and, if applicable, the workflow test.
    
    Please respond with your feedback in the following JSON format and order
    ```json
    {
        "execution": "Describe how well the feature engineering executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
        "return_checking": "Evaluate the correctness and integrity of processed data, checking for missing values, incorrect transformations, and data consistency.",
        "code": "Assess code quality, readability, and adherence to specifications. Consider efficiency, including whether the code utilizes multi-threading or GPU acceleration for optimization.",
        "final_decision": <true/false>
    }
    ```
  
  user: |-
    --------- Feature engineering test stdout ---------
    {{ stdout }}   
    {% if workflow_stdout is not none %}
    --------- Whole workflow test stdout ---------
    {{ workflow_stdout }}
    {% endif %}