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This commit is contained in:
stellahsr 2023-12-14 17:18:35 +08:00
parent 5ba3fe9be8
commit 82ccdde687
1 changed files with 11 additions and 13 deletions
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24
metagpt/roles/ml_engineer.py
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@ -49,8 +49,8 @@ class MLEngineer(Role):
self._watch([DownloadData, SubmitResult])
self.plan = Plan(goal=goal)
self.use_tools = False
self.use_code_steps = False
self.use_tools = True
self.use_code_steps = True
self.execute_code = ExecutePyCode()
self.auto_run = auto_run
self.data_desc = {}
@ -101,8 +101,8 @@ class MLEngineer(Role):
if self.use_tools:
success, new_code = await self._update_data_columns()
if success:
task.code = task.code + "\n\n" + new_code
if success:
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[
@ -245,9 +245,7 @@ class MLEngineer(Role):
async def _reflect(self):
context = self.get_memories()
context = "\n".join([str(msg) for msg in context])
# print("*" * 10)
# print(context)
# print("*" * 10)
reflection = await Reflect().run(context=context)
self.working_memory.add(Message(content=reflection, role="assistant"))
self.working_memory.add(Message(content=Reflect.REWRITE_PLAN_INSTRUCTION, role="user"))
@ -296,7 +294,7 @@ if __name__ == "__main__":
# requirement = f"This is a customers financial dataset. Your goal is to predict which customers will make a specific transaction in the future. The target column is target. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report F1 Score 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 / "save" / "2023-12-14_15-11-40"
# save_dir = DATA_PATH / "save" / "2023-12-14_16-58-03"
def load_history(save_dir: str = save_dir):
@ -328,13 +326,14 @@ if __name__ == "__main__":
Returns:
Path: The path to the saved history directory.
"""
save_path = Path(save_dir) if save_dir else DATA_PATH / "save" / datetime.now().strftime(
# save_path = Path(save_dir) if save_dir else DATA_PATH / "save" / datetime.now().strftime(
# '%Y-%m-%d_%H-%M-%S')
save_path = DATA_PATH / "save" / datetime.now().strftime(
'%Y-%m-%d_%H-%M-%S')
# overwrite
# overwrite exist trajectory
save_path.mkdir(parents=True, exist_ok=True)
plan = role.plan.dict()
logger.info(f"Plan is {plan}")
with open(save_path / "plan.json", "w", encoding="utf-8") as plan_file:
json.dump(plan, plan_file, indent=4, ensure_ascii=False)
@ -361,8 +360,7 @@ if __name__ == "__main__":
role = MLEngineer(goal=requirement, auto_run=auto_run)
role.plan = Plan(**plan)
role.execute_code = ExecutePyCode(nb)
import pdb;
pdb.set_trace()
else:
logger.info("Run from scratch")
role = MLEngineer(goal=requirement, auto_run=auto_run)