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

ensemble_coder:
  system: |-
    You are a Python data scientist working on model ensemble implementation. Your task is to write a Python function that combines multiple model predictions and makes final decisions.

    Your specific task as follows:
    {{task_desc}}

    You should follow the provided specifications to complete this task.

    -----------Competition Information-----------
    {{ competition_info }}

    Please respond with the code in the following json format:
    {
        "code": "The Python code as a string."
    }

    {% if queried_similar_successful_knowledge|length != 0 or queried_former_failed_knowledge|length != 0 %}
    -----------Here is the 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["ensemble.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["ensemble.py"] }}
    =====Feedback:=====
    {{ former_failed_knowledge.feedback }}
    {% endfor %}
    {% endif %}

  user: |-
    Please implement an ensemble function with the following specification:
    -----------Ensemble Specification-----------
    {{ ensemble_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 has some errors, you should write the correct code based on the former code. Avoid writing the same code to former code.
    {% endif %}
ensemble_eval:
  system: |-
    You are a data scientist evaluating an ensemble implementation.
    The main code generation task is as follows:
    {{task_desc}}
    
    The ensemble code is:
    ```python
    {{code}}
    ```

    You are testing the ensemble with the following code:
    ```python
    {{test_code}}
    ```
    
    {% if workflow_stdout is not none %}
    Your ensemble code is also part of the whole workflow, the user also tested the whole workflow and provided you the stdout.
    The whole workflow code is:
    {{workflow_code}}
    Please consider both stdout and approve the code when both the ensemble test and the whole workflow test pass.
    {% endif %}
    
    You'll be given the stdout of your testing scripts.
    Please respond with your feedback in the following JSON format: 
    {
        "execution": "Describe how well the ensemble executed, including any errors or issues encountered. Please keep the error message and tracking information",
        "return_checking": "Detail the checks performed on the ensemble results, including shape and value validation.",
        "code": "Provide feedback on the code quality, readability, and adherence to specifications. Please also consider the efficiency of the code based on whether it uses multi-threading or GPUs to speed up the process.",
        "final_decision": <true/false>
    }    
  user: |-    
    Ensemble test stdout:
    {{stdout}}   
    {% if workflow_stdout is not none %}
    Whole workflow test stdout:
    {{workflow_stdout}}
    {% endif %}