From 9ed352b2cedf74a44202c1711dadbe05a14e86ae Mon Sep 17 00:00:00 2001 From: lidanyang Date: Wed, 7 Feb 2024 14:56:19 +0800 Subject: [PATCH] add example for ml_engineer_with_tools --- examples/ml_engineer_with_tools.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 examples/ml_engineer_with_tools.py diff --git a/examples/ml_engineer_with_tools.py b/examples/ml_engineer_with_tools.py new file mode 100644 index 000000000..1c90f2946 --- /dev/null +++ b/examples/ml_engineer_with_tools.py @@ -0,0 +1,16 @@ +import asyncio + +from metagpt.roles.ci.ml_engineer import MLEngineer + + +async def main(requirement: str, auto_run: bool = True, use_tools: bool = True): + role = MLEngineer(goal=requirement, auto_run=auto_run, use_tools=use_tools) + await role.run(requirement) + + +if __name__ == "__main__": + data_path = "your_path_to_icr/icr-identify-age-related-conditions" # 替换 'your_path_to_icr' 为实际数据存放的路径 + train_path = f"{data_path}/your_train_data.csv" # 替换 'your_train_data.csv' 为你的训练数据文件名 + eval_path = f"{data_path}/your_eval_data.csv" # 替换 'your_eval_data.csv' 为你的评估数据文件名 + requirement = f"This is a medical dataset with over fifty anonymized health characteristics linked to three age-related conditions. Your goal is to predict whether a subject has or has not been diagnosed with one of these conditions.The target column is Class. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report f1 score on the eval data. Train data path: {train_path}, eval data path:{eval_path}." + asyncio.run(main(requirement))