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Merge branch 'kaggle_team' into 'dev'

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task update support more forms of generated tasks

See merge request agents/data_agents_opt!20
This commit is contained in:
林义章 2023-12-13 02:28:56 +00:00
commit ce9bd1cd02
2 changed files with 24 additions and 7 deletions
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10
metagpt/actions/write_plan.py
View file

@ -13,6 +13,7 @@ from metagpt.actions import Action
from metagpt.prompts.ml_engineer import ASSIGN_TASK_TYPE_PROMPT, ASSIGN_TASK_TYPE
from metagpt.schema import Message, Task, Plan
from metagpt.utils.common import CodeParser, create_func_config
from metagpt.logs import logger
class WritePlan(Action):
@ -22,6 +23,7 @@ class WritePlan(Action):
# Task:
Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to __max_tasks__ tasks.
If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.
If you encounter errors on the current task, revise and output the current single task only.
Output a list of jsons following the format:
```json
[
@ -76,7 +78,13 @@ def rsp_to_tasks(rsp: str) -> List[Task]:
def update_plan_from_rsp(rsp: str, current_plan: Plan):
tasks = rsp_to_tasks(rsp)
if len(tasks) == 1:
if len(tasks) == 1 or tasks[0].dependent_task_ids:
if tasks[0].dependent_task_ids and len(tasks) > 1:
# tasks[0].dependent_task_ids means the generated tasks are not a complete plan
# for they depend on tasks in the current plan, in this case, we only support updating one task each time
logger.warning(
"Current plan will take only the first generated task if the generated tasks are not a complete plan"
)
# handle a single task
if current_plan.has_task_id(tasks[0].task_id):
# replace an existing task

21
metagpt/roles/ml_engineer.py
View file

@ -28,7 +28,7 @@ class MLEngineer(Role):
self.plan = Plan(goal=goal)
self.use_tools = False
self.use_code_steps = True
self.use_code_steps = False
self.execute_code = ExecutePyCode()
self.auto_run = auto_run
@ -64,6 +64,11 @@ class MLEngineer(Role):
# ask for acceptance, users can other refuse and change tasks in the plan
review, task_result_confirmed = await self._ask_review(trigger=ReviewConst.TASK_REVIEW_TRIGGER)
if self.auto_run:
# if human confirms the task result, then we deem the task completed, regardless of whether the code run succeeds;
# if auto mode, then the code run has to succeed for the task to be considered completed
task_result_confirmed = success
if task_result_confirmed:
# tick off this task and record progress
task.code = code
@ -143,7 +148,7 @@ class MLEngineer(Role):
if not success and counter >= max_retry:
logger.info("coding failed!")
review, _ = await self._ask_review(auto_run=False, trigger=ReviewConst.CODE_REVIEW_TRIGGER)
if ReviewConst.CHANGE_WORD in review:
if ReviewConst.CHANGE_WORD[0] in review:
counter = 0 # redo the task again with help of human suggestions
return code, result, success, code_steps
@ -199,9 +204,12 @@ class MLEngineer(Role):
# TODO dataset description , code steps
user_requirement = self.plan.goal
data_desc = self.plan.context
tasks = json.dumps(
[task.dict() for task in self.plan.tasks], indent=4, ensure_ascii=False
)
tasks = [task.dict() for task in self.plan.tasks]
for task in tasks:
# Shorten the context as we don't need code steps after we get the codes.
# This doesn't affect current_task below, which should hold the code steps
task.pop("code_steps")
tasks = json.dumps(tasks, indent=4, ensure_ascii=False)
current_task = self.plan.current_task.json() if self.plan.current_task else {}
context = STRUCTURAL_CONTEXT.format(
user_requirement=user_requirement, data_desc=data_desc, tasks=tasks, current_task=current_task
@ -219,7 +227,8 @@ if __name__ == "__main__":
# requirement = "Run data analysis on sklearn Diabetes dataset, include a plot"
# requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy"
# requirement = "Run data analysis on sklearn Wisconsin Breast Cancer dataset, include a plot, train a model to predict targets (20% as validation), and show validation accuracy"
requirement = "Run EDA and visualization on this dataset, train a model to predict survival, report metrics on validation set (20%), dataset: workspace/titanic/train.csv"
# requirement = "Run EDA and visualization on this dataset, train a model to predict survival, report metrics on validation set (20%), dataset: workspace/titanic/train.csv"
requirement = "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: 'workspace/house-prices-advanced-regression-techniques/split_train.csv', eval data path: 'workspace/house-prices-advanced-regression-techniques/split_eval.csv'."
async def main(requirement: str = requirement, auto_run: bool = False):
role = MLEngineer(goal=requirement, auto_run=auto_run)