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Merge remote-tracking branch 'origin/dev' into dev_tool_selection
# Conflicts: # metagpt/roles/ml_engineer.py
This commit is contained in:
lidanyang
commit
3c8ef3e848
15 changed files with 806 additions and 160 deletions
153
metagpt/roles/kaggle_manager.py
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153
metagpt/roles/kaggle_manager.py
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@ -0,0 +1,153 @@
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from typing import Dict, List, Union, Tuple
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import json
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import subprocess
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import os
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import fire
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import pandas as pd
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from metagpt.config import CONFIG
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from metagpt.const import WORKSPACE_ROOT
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from metagpt.roles import Role
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from metagpt.actions import Action, BossRequirement
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from metagpt.actions.ml_da_action import AskReview, SummarizeAnalysis
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from metagpt.schema import Message, Task, Plan
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from metagpt.logs import logger
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from metagpt.utils.common import CodeParser
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os.environ["KAGGLE_USERNAME"] = CONFIG.kaggle_username
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os.environ["KAGGLE_KEY"] = CONFIG.kaggle_key
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def run_command(cmd):
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print(cmd)
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output = subprocess.run(cmd, shell=True, capture_output=True, text=True)
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if output.returncode != 0:
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print("Error output:", output.stderr)
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exit()
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else:
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print(output.stdout)
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return output.stdout
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class DownloadData(Action):
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async def run(self, competition, data_desc="") -> str:
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data_path = WORKSPACE_ROOT / competition
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output = run_command(f"kaggle competitions list --search {competition}")
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assert output != "No competitions found", "You must provide the correct competition name"
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run_command(f"kaggle competitions download {competition} --path {WORKSPACE_ROOT}")
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if not os.path.exists(data_path):
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# if True:
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# run_command(f"rm -r {data_path / '*'}")
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run_command(f"unzip -o {WORKSPACE_ROOT / '*.zip'} -d {data_path}") # FIXME: not safe
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file_list = run_command(f"ls {data_path}")
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rsp = f"""
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Location:
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Data downloaded at {data_path} folder, including {file_list}
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Data Description:
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{data_desc}
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"""
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return rsp
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class SubmitResult(Action):
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PROMPT_TEMPLATE = """
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# Summary
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__summary__
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# Your task
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Extract the file path for test set prediction from the summary above, output a json following the format:
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```json
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{"file_path": str = "the file path, for example, /path/to/the/prediction/file/xxx.csv, /path/to/the/prediction/file/xxx.xlsx"}
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```
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"""
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def __init__(self, name: str = "", context=None, llm=None) -> str:
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super().__init__(name, context, llm)
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async def _parse_submit_file_path(self, context) -> str:
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prompt = self.PROMPT_TEMPLATE.replace("__summary__", context)
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rsp = await self._aask(prompt)
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rsp = CodeParser.parse_code(block=None, text=rsp)
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file_path = json.loads(rsp)["file_path"]
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return file_path
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async def run(self, competition, submit_message="") -> str:
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submit_file_path = await self._parse_submit_file_path(submit_message)
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data_path = WORKSPACE_ROOT / competition
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submit_message = submit_message.replace("'", "")
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run_command(f"kaggle competitions submit {competition} -f {submit_file_path} -m '{submit_message}'")
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run_command(f"kaggle competitions leaderboard --show --csv {competition} > {data_path / 'leaderboard.csv'}")
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run_command(f"kaggle competitions submissions --csv {competition} > {data_path / 'submission.csv'}")
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leaderboard = pd.read_csv(data_path / 'leaderboard.csv')
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submission = pd.read_csv(data_path / 'submission.csv')
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print(submission) # submission.to_json(orient="records")
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submission_score = submission.loc[0, "publicScore"]
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best_score = max(submission["publicScore"]) # might be min
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rank = leaderboard.loc[leaderboard["score"] == best_score].index[0]
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rank_pct = round(rank / len(leaderboard), 4) * 100
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submission_summary = f"""
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# All histories:
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{submission.head(5).to_string()}
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# Current
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Current submission score: {submission_score}, best score: {best_score}, best rank: {rank} (top {rank_pct}%)
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"""
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logger.info(submission_summary)
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return submission_summary
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class KaggleManager(Role):
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def __init__(
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self, name="ABC", profile="KaggleManager", goal="", competition="titanic", data_desc=""
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):
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super().__init__(name=name, profile=profile, goal=goal)
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self._init_actions([DownloadData, SubmitResult])
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self._watch([BossRequirement, SummarizeAnalysis])
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self.competition = competition
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self.data_desc = data_desc # currently passed in, later can be scrapped down from web by another Role
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async def _think(self):
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observed = self.get_memories()[-1].cause_by
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if observed == BossRequirement:
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self._set_state(0) # DownloadData, get competition of interest from human, download datasets
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elif observed == SummarizeAnalysis:
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self._set_state(1) # SubmitResult, get prediction from MLEngineer and submit it to Kaggle
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async def _act(self):
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todo = self._rc.todo
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logger.info(f"{self._setting}: ready to {self._rc.todo}")
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if isinstance(todo, DownloadData):
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rsp = await todo.run(self.competition, self.data_desc)
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elif isinstance(todo, SubmitResult):
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submit_message = self.get_memories()[-1].content # use analysis summary from MLEngineer as submission message
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rsp = await todo.run(competition=self.competition, submit_message=submit_message)
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msg = Message(content=rsp, role="user", cause_by=type(todo))
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return msg
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if __name__ == "__main__":
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competition, data_desc, requirement = (
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"titanic",
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"Training set is train.csv.\nTest set is test.csv. We also include gender_submission.csv, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.",
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"Run EDA on the train dataset, train a model to predict survival (20% as validation) and save it, predict the test set using saved model, save the test result according to format",
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)
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summary = "I used Python with pandas for data preprocessing, sklearn's RandomForestClassifier for modeling, and achieved 82.12% accuracy on validation. Predictions saved at '/Users/gary/Desktop/data_agents_opt/workspace/titanic/gender_submission.csv'."
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async def main(requirement: str = requirement):
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role = KaggleManager(competition=competition, data_desc=data_desc)
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# await role.run(Message(content="", cause_by=BossRequirement))
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await role.run(Message(content=summary, cause_by=SummarizeAnalysis))
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fire.Fire(main)
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@ -1,14 +1,20 @@
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from typing import List
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import json
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import re
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from datetime import datetime
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from typing import List
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import fire
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import pandas as pd
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from metagpt.roles import Role
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from metagpt.schema import Message, Plan
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from metagpt.memory import Memory
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from metagpt.logs import logger
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from metagpt.actions import Action
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from metagpt.actions.execute_code import ExecutePyCode
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from metagpt.actions.write_plan import WritePlan, update_plan_from_rsp, precheck_update_plan_from_rsp
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from metagpt.actions.write_analysis_code import WriteCodeByGenerate, WriteCodeWithTools
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from metagpt.actions.ml_da_action import AskReview, SummarizeAnalysis, Reflect, ReviewConst
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from metagpt.actions.execute_code import ExecutePyCode
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from metagpt.roles.kaggle_manager import DownloadData, SubmitResult
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from metagpt.prompts.ml_engineer import STRUCTURAL_CONTEXT
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from metagpt.actions.write_code_steps import WriteCodeSteps
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from metagpt.actions.write_plan import WritePlan
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from metagpt.const import DATA_PATH, PROJECT_ROOT
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@ -22,60 +28,7 @@ from metagpt.roles import Role
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from metagpt.schema import Message, Plan
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from metagpt.utils.common import CodeParser, remove_comments, create_func_config
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from metagpt.actions.debug_code import DebugCode
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STRUCTURAL_CONTEXT = """
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## User Requirement
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{user_requirement}
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## Dataset Description
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{data_desc}
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## Current Plan
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{tasks}
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## Current Task
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{current_task}
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"""
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def truncate(result: str, keep_len: int = 1000) -> str:
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desc = "Truncated to show only the last 1000 characters\n"
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if result.startswith(desc):
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result = result[-len(desc):]
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if len(result) > keep_len:
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result = result[-keep_len:]
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if not result.startswith(desc):
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return desc + result
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return desc
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def remove_escape_and_color_codes(input_str):
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# 使用正则表达式去除转义字符和颜色代码
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pattern = re.compile(r'\x1b\[[0-9;]*[mK]')
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result = pattern.sub('', input_str)
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return result
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class AskReview(Action):
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async def run(self, context: List[Message], plan: Plan = None):
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logger.info("Current overall plan:")
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logger.info(
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"\n".join([f"{task.task_id}: {task.instruction}, is_finished: {task.is_finished}" for task in plan.tasks])
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)
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logger.info("most recent context:")
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latest_action = context[-1].cause_by.__name__ if context[-1].cause_by else ""
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prompt = f"\nPlease review output from {latest_action}:\n" \
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"If you want to change a task in the plan, say 'change task task_id, ... (things to change)'\n" \
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"If you confirm the output and wish to continue with the current process, type CONFIRM\n" \
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"If you want to terminate the process, type exit:\n"
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rsp = input(prompt)
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if rsp.lower() in ("exit"):
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exit()
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confirmed = rsp.lower() in ("confirm", "yes", "y")
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return rsp, confirmed
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from metagpt.utils.save_code import save_code_file
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class UpdateDataColumns(Action):
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@ -91,32 +44,58 @@ class UpdateDataColumns(Action):
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class MLEngineer(Role):
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def __init__(
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self, name="ABC", profile="MLEngineer", goal="", auto_run: bool = False,
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self, name="ABC", profile="MLEngineer", goal="", auto_run: bool = False
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):
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super().__init__(name=name, profile=profile, goal=goal)
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self._set_react_mode(react_mode="plan_and_act")
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self._watch([DownloadData, SubmitResult])
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self.plan = Plan(goal=goal)
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self.use_tools = True
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self.use_code_steps = True
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self.use_tools = False
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self.use_code_steps = False
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self.execute_code = ExecutePyCode()
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self.auto_run = auto_run
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self.data_desc = {}
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# memory for working on each task, discarded each time a task is done
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self.working_memory = Memory()
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async def _plan_and_act(self):
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### Actions in a multi-agent multi-turn setting ###
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memories = self.get_memories()
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if memories:
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latest_event = memories[-1].cause_by
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if latest_event == DownloadData:
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self.plan.context = memories[-1].content
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elif latest_event == SubmitResult:
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# self reflect on previous plan outcomes and think about how to improve the plan, add to working memory
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await self._reflect()
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# get feedback for improvement from human, add to working memory
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await self._ask_review(trigger=ReviewConst.TASK_REVIEW_TRIGGER)
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### Common Procedure in both single- and multi-agent setting ###
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# create initial plan and update until confirmation
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await self._update_plan()
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while self.plan.current_task:
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task = self.plan.current_task
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logger.info(f"ready to take on task {task}")
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# take on current task
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code, result, success, code_steps = await self._write_and_exec_code()
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# ask for acceptance, users can other refuse and change tasks in the plan
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task_result_confirmed = await self._ask_review()
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if success and task_result_confirmed:
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review, task_result_confirmed = await self._ask_review(trigger=ReviewConst.TASK_REVIEW_TRIGGER)
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if self.auto_run:
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# if human confirms the task result, then we deem the task completed, regardless of whether the code run succeeds;
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# if auto mode, then the code run has to succeed for the task to be considered completed
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task_result_confirmed = success
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if task_result_confirmed:
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# tick off this task and record progress
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task.code = code
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task.result = result
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@ -127,9 +106,33 @@ class MLEngineer(Role):
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success, new_code = await self._update_data_columns()
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if success:
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task.code = task.code + "\n\n" + new_code
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confirmed_and_more = (ReviewConst.CONTINUE_WORD[0] in review.lower()
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and review.lower() not in ReviewConst.CONTINUE_WORD[0]) # "confirm, ... (more content, such as changing downstream tasks)"
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if confirmed_and_more:
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self.working_memory.add(Message(content=review, role="user", cause_by=AskReview))
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await self._update_plan(review)
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elif "redo" in review:
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# Ask the Role to redo this task with help of review feedback,
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# useful when the code run is successful but the procedure or result is not what we want
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continue
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else:
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# update plan according to user's feedback and to take on changed tasks
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await self._update_plan()
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await self._update_plan(review)
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completed_plan_memory = self.get_useful_memories() # completed plan as a outcome
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self._rc.memory.add(completed_plan_memory[0]) # add to persistent memory
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summary = await SummarizeAnalysis().run(self.plan)
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rsp = Message(content=summary, cause_by=SummarizeAnalysis)
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self._rc.memory.add(rsp)
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# save code using datetime.now or keywords related to the goal of your project (plan.goal).
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project_record = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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save_code_file(name=project_record, code_context=self.execute_code.nb, file_format="ipynb")
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return rsp
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time = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
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self.execute_code.save_notebook(f"{DATA_PATH}/notebooks/ml_{time}.ipynb")
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|
|
@ -150,21 +153,25 @@ class MLEngineer(Role):
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if self.use_code_steps
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else ""
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)
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counter = 0
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improve_code = ""
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success = False
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debug_context = []
|
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|
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|
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finished_tasks = self.plan.get_finished_tasks()
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code_context = [task.code for task in finished_tasks]
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code_result = [task.result for task in finished_tasks]
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code_context = "\n\n".join(code_context)
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code_result = "\n\n".join(code_result)
|
||||
|
||||
|
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while not success and counter < max_retry:
|
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context = self.get_useful_memories()
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|
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# print("*" * 10)
|
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# print(context)
|
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# print("*" * 10)
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# breakpoint()
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if counter > 0:
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improve_code = await DebugCode().run(plan=self.plan.current_task.instruction,
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# finished_code=code_context,
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|
|
@ -195,42 +202,42 @@ class MLEngineer(Role):
|
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)
|
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debug_context = tool_context
|
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cause_by = WriteCodeWithTools
|
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|
||||
|
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self.working_memory.add(
|
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Message(content=code, role="assistant", cause_by=cause_by)
|
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)
|
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|
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# debug on code, run on runcode with finished code and new_df
|
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# runcode = code_context + "\n\n" + code
|
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result, success = await self.execute_code.run(code)
|
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# truncated the result
|
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print(truncate(result))
|
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|
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result, success = await self.execute_code.run(code)
|
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print(result)
|
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self.working_memory.add(
|
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Message(content=truncate(remove_escape_and_color_codes(result)), role="user", cause_by=ExecutePyCode)
|
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Message(content=result, role="user", cause_by=ExecutePyCode)
|
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)
|
||||
|
||||
|
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if "!pip" in code:
|
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success = False
|
||||
# if not success:
|
||||
# await self._ask_review()
|
||||
|
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success = False
|
||||
|
||||
counter += 1
|
||||
|
||||
|
||||
if not success and counter >= max_retry:
|
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logger.info("coding failed!")
|
||||
review, _ = await self._ask_review(auto_run=False, trigger=ReviewConst.CODE_REVIEW_TRIGGER)
|
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if ReviewConst.CHANGE_WORD[0] in review:
|
||||
counter = 0 # redo the task again with help of human suggestions
|
||||
|
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return code, result, success, code_steps
|
||||
|
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async def _ask_review(self):
|
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if not self.auto_run:
|
||||
|
||||
async def _ask_review(self, auto_run: bool = None, trigger: str = ReviewConst.TASK_REVIEW_TRIGGER):
|
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auto_run = auto_run or self.auto_run
|
||||
if not auto_run:
|
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context = self.get_useful_memories()
|
||||
review, confirmed = await AskReview().run(context=context[-5:], plan=self.plan)
|
||||
review, confirmed = await AskReview().run(context=context[-5:], plan=self.plan, trigger=trigger)
|
||||
if not confirmed:
|
||||
self.working_memory.add(Message(content=review, role="user", cause_by=AskReview))
|
||||
return confirmed
|
||||
return True
|
||||
|
||||
async def _update_plan(self, max_tasks: int = 3):
|
||||
return review, confirmed
|
||||
return "", True
|
||||
|
||||
async def _update_plan(self, review: str = "", max_tasks: int = 3, max_retries: int = 3):
|
||||
plan_confirmed = False
|
||||
|
||||
while not plan_confirmed:
|
||||
context = self.get_useful_memories()
|
||||
rsp = await WritePlan().run(
|
||||
|
|
@ -239,58 +246,64 @@ class MLEngineer(Role):
|
|||
self.working_memory.add(
|
||||
Message(content=rsp, role="assistant", cause_by=WritePlan)
|
||||
)
|
||||
plan_confirmed = await self._ask_review()
|
||||
|
||||
new_tasks = WritePlan.rsp_to_tasks(rsp)
|
||||
logger.debug(len(self.plan.tasks))
|
||||
logger.debug(len(new_tasks))
|
||||
## fixme: 能重复执行多轮重新plan，但应该有更优处理逻辑
|
||||
## fixme: do not overwrite original tasks
|
||||
tasks = self.plan.tasks + new_tasks
|
||||
|
||||
self.plan.add_tasks(tasks)
|
||||
|
||||
# precheck plan before asking reviews
|
||||
is_plan_valid, error = precheck_update_plan_from_rsp(rsp, self.plan)
|
||||
if not is_plan_valid and max_retries > 0:
|
||||
error_msg = f"The generated plan is not valid with error: {error}, try regenerating, remember to generate either the whole plan or the single changed task only"
|
||||
logger.warning(error_msg)
|
||||
self.working_memory.add(Message(content=error_msg, role="assistant", cause_by=WritePlan))
|
||||
max_retries -= 1
|
||||
continue
|
||||
|
||||
_, plan_confirmed = await self._ask_review(trigger=ReviewConst.TASK_REVIEW_TRIGGER)
|
||||
|
||||
update_plan_from_rsp(rsp, self.plan)
|
||||
|
||||
self.working_memory.clear()
|
||||
|
||||
|
||||
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"))
|
||||
|
||||
def get_useful_memories(self, task_exclude_field: set = None) -> List[Message]:
|
||||
"""find useful memories only to reduce context length and improve performance"""
|
||||
# TODO dataset description , code steps
|
||||
user_requirement = self.plan.goal
|
||||
tasks = json.dumps(
|
||||
[task.dict(exclude=task_exclude_field) for task in self.plan.tasks], indent=4, ensure_ascii=False
|
||||
)
|
||||
data_desc = self.plan.context
|
||||
tasks = [task.dict(exclude=task_exclude_field) 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=self.data_desc,
|
||||
tasks=tasks,
|
||||
current_task=current_task
|
||||
user_requirement=user_requirement, data_desc=data_desc, tasks=tasks, current_task=current_task
|
||||
)
|
||||
context_msg = [Message(content=context, role="user")]
|
||||
|
||||
return context_msg + self.working_memory.get()
|
||||
|
||||
@property
|
||||
def working_memory(self):
|
||||
return self._rc.memory
|
||||
|
||||
return context_msg + self.get_working_memories()
|
||||
|
||||
def get_working_memories(self) -> List[Message]:
|
||||
return self.working_memory.get()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# requirement = "Run data analysis on sklearn Iris dataset, include a plot"
|
||||
requirement = "Run data analysis on sklearn Iris dataset, include a plot"
|
||||
# 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 = "Perform data analysis on the provided data. Train a model to predict the target variable Survived. Include data preprocessing, feature engineering, and modeling in your pipeline. The metric is accuracy."
|
||||
|
||||
data_path = f"{DATA_PATH}/titanic"
|
||||
requirement = f"This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: '{data_path}/split_train.csv', eval data path: '{data_path}/split_eval.csv'."
|
||||
# requirement = f"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"
|
||||
# data_path = f"{DATA_PATH}/icr-identify-age-related-conditions"
|
||||
# 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: {data_path}/split_train.csv, eval data path: {data_path}/split_eval.csv."
|
||||
|
||||
async def main(requirement: str = requirement, auto_run: bool = True):
|
||||
role = MLEngineer(goal=requirement, auto_run=auto_run)
|
||||
await role.run(requirement)
|
||||
|
||||
|
||||
|
||||
fire.Fire(main)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue