mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-29 08:27:43 +00:00
Suhan Cui
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
from pathlib import Path
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from rdagent.components.coder.model_coder.model import (
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ModelExperiment,
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ModelFBWorkspace,
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ModelTask,
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)
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from rdagent.core.prompts import Prompts
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from rdagent.core.scenario import Scenario
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from rdagent.scenarios.data_mining.experiment.workspace import DMFBWorkspace
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prompt_dict = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class DMModelExperiment(ModelExperiment[ModelTask, DMFBWorkspace, ModelFBWorkspace]):
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def __init__(self, *args, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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self.experiment_workspace = DMFBWorkspace(template_folder_path=Path(__file__).parent / "model_template")
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class DMModelScenario(Scenario):
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@property
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def background(self) -> str:
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return prompt_dict["dm_model_background"]
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@property
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def source_data(self) -> str:
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raise NotImplementedError("source_data is not implemented")
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@property
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def output_format(self) -> str:
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return prompt_dict["dm_model_output_format"]
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@property
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def interface(self) -> str:
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return prompt_dict["dm_model_interface"]
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@property
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def simulator(self) -> str:
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return prompt_dict["dm_model_simulator"]
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@property
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def rich_style_description(self) -> str:
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return """
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### MIMIC-III Model Evolving Automatic R&D Demo
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#### [Overview](#_summary)
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The demo showcases the iterative process of hypothesis generation, knowledge construction, and decision-making in model construction in a clinical prediction task. The model should predict whether a patient would suffer from Acute Respiratory Failure (ARF) based on first 12 hours ICU monitoring data.
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#### [Automated R&D](#_rdloops)
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- **[R (Research)](#_research)**
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- Iteration of ideas and hypotheses.
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- Continuous learning and knowledge construction.
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- **[D (Development)](#_development)**
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- Evolving code generation and model refinement.
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- Automated implementation and testing of models.
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#### [Objective](#_summary)
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To demonstrate the dynamic evolution of models through the R&D loop, emphasizing how each iteration enhances the model performance and reliability. The performane is measured by the AUROC score (Area Under the Receiver Operating Characteristic), which is a commonly used metric for binary classification. """
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def get_scenario_all_desc(self) -> str:
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return f"""Background of the scenario:
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{self.background}
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The interface you should follow to write the runnable code:
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{self.interface}
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The output of your code should be in the format:
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{self.output_format}
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The simulator user can use to test your model:
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{self.simulator}
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"""
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