From 48ef61c6e42c65aa38a5c4466c24191912198c4e Mon Sep 17 00:00:00 2001 From: stellahsr Date: Wed, 20 Dec 2023 14:46:29 +0800 Subject: [PATCH] change format --- metagpt/roles/ml_engineer.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/metagpt/roles/ml_engineer.py b/metagpt/roles/ml_engineer.py index 16ffe69db..33b570d1a 100644 --- a/metagpt/roles/ml_engineer.py +++ b/metagpt/roles/ml_engineer.py @@ -4,7 +4,6 @@ from datetime import datetime import fire - from metagpt.actions import Action from metagpt.actions.debug_code import DebugCode from metagpt.actions.execute_code import ExecutePyCode @@ -28,6 +27,7 @@ from metagpt.utils.common import remove_comments, create_func_config from metagpt.utils.save_code import save_code_file from metagpt.utils.recovery_util import save_history, load_history + class UpdateDataColumns(Action): async def run(self, plan: Plan = None) -> dict: finished_tasks = plan.get_finished_tasks() @@ -41,7 +41,7 @@ class UpdateDataColumns(Action): class MLEngineer(Role): def __init__( - self, name="ABC", profile="MLEngineer", goal="", auto_run: bool = False + self, name="ABC", profile="MLEngineer", goal="", auto_run: bool = False ): super().__init__(name=name, profile=profile, goal=goal) self._set_react_mode(react_mode="plan_and_act") @@ -104,8 +104,7 @@ class MLEngineer(Role): task.code = task.code + "\n\n" + new_code confirmed_and_more = (ReviewConst.CONTINUE_WORD[0] in review.lower() - and review.lower() not in ReviewConst.CONTINUE_WORD[ - 0]) # "confirm, ... (more content, such as changing downstream tasks)" + and review.lower() not in ReviewConst.CONTINUE_WORD[0]) # "confirm, ... (more content, such as changing downstream tasks)" if confirmed_and_more: self.working_memory.add(Message(content=review, role="user", cause_by=AskReview)) await self._update_plan(review) @@ -294,11 +293,10 @@ if __name__ == "__main__": requirement = f"This is a house price dataset, your goal is to predict the sale price of a property based on its features. The target column is SalePrice. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report RMSE between the logarithm of the predicted value and the logarithm of the observed sales price on the eval data. Train data path: '{data_path}/split_train.csv', eval data path: '{data_path}/split_eval.csv'." save_dir = "" + + # save_dir = DATA_PATH / "output" / "2023-12-14_20-40-34" - - - async def main(requirement: str = requirement, auto_run: bool = True, save_dir: str = save_dir): """ The main function to run the MLEngineer with optional history loading.