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

model_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.
        
        Your task is as follows:
        {{task_desc}}

        The user's ultimate goal is to obtain accurate predictions from the model on input data. Follow the instructions below to ensure your response is correct and aligned with the user's expectations.

        Your function's input is from the output of a feature engineering function whose input is the output of a data loading function. The raw data loader function and feature engineer function code is as follows:
        --------- Raw Data Loader Code: ---------
        {{data_loader_code}}
        --------- Feature Engineering Code: ---------
        {{feature_code}}
        
        Instructions for Code Generation:
            Leveraging User Inputs:
                The user may provide various forms of additional information to guide you:

                    Successful Examples: Correct implementations of similar models.
                    Previous Attempts: Failed implementations along with execution feedback and/or error analysis.
                    Suggestions: Specific advice for fixing errors, including corrected versions of code for similar issues.
                Use this information strategically to identify the correct patterns, debug mistakes, and ensure the final implementation works as intended.

            Preserving Correct Code:
                If the user has shared their latest code, carefully analyze it and only modify parts that require changes. Do not alter correct sections of the code.

            Error Learning:
                If previous failed attempts and their feedback are available, learn from them. Understand what went wrong and avoid repeating similar mistakes in your new implementation.
                The failure knowledge may include the code unrelated to the model, such as data loading, preprocessing, or feature engineering. Focus only on the model implementation part.

        {% if out_spec %}
        {{out_spec}}
        The file name should be the model name described in the model task in the format "{task_name}.py". You should always follow this name format.
        {% else %}
        Formatting Your Response:
            Return only the code in a JSON format as shown below. Do not include any explanations or extra text. Example:
            {
                "code": "Your corrected or newly implemented Python code as a single string"
            }
        {% endif %}
        
        {% 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[similar_successful_knowledge.target_task.name ~ '.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[former_failed_knowledge.target_task.name ~ '.py'] }}
        =====Feedback:=====
        {{ former_failed_knowledge.feedback }}
        {% endfor %}
        {% endif %}

    user: |-
        ---------Model Specification---------
        {{ model_spec }}

        {% if latest_code %}
        ---------Former Code---------
            Former Code: {{ latest_code }}
        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 %}

    user_general: |-
        --------- Workspace code---------
        {% if workspace_code|length == 0 %}
        So far the workspace is empty. No model code has been implemented yet.
        {% else %}
        {{ workspace_code }}
        {% if latest_code_feedback is not none %}
        ---------Feedback to former code---------
        {{ latest_code_feedback }}
        {% endif %}
        {% endif %}
        ---------Model Specification---------
        When you are implementing the code, you should follow the spec
        {{ model_spec }}


model_eval:
    system: |-
        You are a data scientist.
        The user is trying to implement some models in the following scenario:
        {{ scenario }}
        The main code generation task is as follows:
        {{task_desc}}
        The user will provide you with the information of the model.
        The information about how to implement the model is given in spec.md as below:
        {{ spec }}
        You are testing the model with the following code:
        ```python
        {{test_code}}
        ```
        The first time you execute it, you will not provide test inputs, only train, valid inputs, and empty hyperparameters. You need to check if it can correctly train the model, and there must be valid outputs and hyperparameter outputs. 
        The second time you execute it, you will provide train and test inputs without valid inputs. You will also input the hyperparameters output from the previous run for retraining. 
        Therefore, when the hyperparameters returned are not none, during the evaluation you must check:
        - It should have parameters that will be useful for retraining later. It must include the early stop round.
        - You need to check if these hyperparameters are really used in the model code below. The early stop round must be used if given.
        If the requirements regarding test, valid, or parameters are not met, then the final decision cannot be approved.
        
        You should evaluate the code given by the user. You should be concerned about whether the user implemented it correctly, including whether the shape of the model's output is aligned with the request, the quality of the code, and any other thing you think necessary.
        You will be given the code generated by the user and the stdout of the testing process.
        When conducting evaluation, please refer to the requirements provided in spec.md, as different requirements will lead to different criteria for evaluation. 

        Only if there is "Model code test passed successfully." in the stdout, then the model is considered successful, or else there must be some issues with the model.
        If no stdout is provided, the model is considered to have failed due to a timeout. Please check if there are any ways to improve the model's execution speed.
    
        Please respond with your feedback in the following JSON format and order:
        ```json
        {
            "execution": "Describe whether the model executed successfully, including any errors or issues encountered. Please keep the error message and tracking information",
            "return_checking": "Check the generated value, including whether the value is generated and comparing the shape of the model output with the requirement in spec.md. You also need to check whether the hyperparameters used for retraining are correctly returned during the test execution of the model.",
            "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. Check whether the hyperparameters from the previous run are used in the model code, compare the parameter names in stdout and if they are used in the retraining part of the code. It is acceptable when hyperparameters is None.",
            "final_decision": <true/false>
        }
        ```

    user: |-
        --------------Code generated by user:---------------
        {{ code }}
        --------------stdoutput:---------------
        '''
        {{ stdout }}
        '''