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add GenerateDataDesc action
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lidanyang
parent
20a918bf39
commit
962632cd15
1 changed files with 112 additions and 19 deletions
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@ -1,25 +1,38 @@
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from typing import Dict, List, Union
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import glob
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import json
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import subprocess
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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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import re
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from metagpt.roles import Role
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from metagpt.actions import Action
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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.actions.write_plan import WritePlan
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from metagpt.actions.write_analysis_code import WriteCodeByGenerate, WriteCodeWithTools
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from metagpt.actions.execute_code import ExecutePyCode
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from metagpt.actions.write_analysis_code import WriteCodeByGenerate, WriteCodeWithTools
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from metagpt.actions.write_plan import WritePlan
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from metagpt.actions.write_task_guide import WriteTaskGuide
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from metagpt.logs import logger
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from metagpt.prompts.ml_engineer import GEN_DATA_DESC_PROMPT
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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.utils.common import CodeParser
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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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## Packages Installed
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scikit-learn
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pandas
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numpy
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lightgbm
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xgboost
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catboost
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"""
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@ -43,6 +56,50 @@ def remove_escape_and_color_codes(input_str):
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return result
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def read_data(file: str) -> pd.DataFrame:
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if file.endswith(".csv"):
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df = pd.read_csv(file, sep=",")
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sep_list = [";", "\t", ":", " ", "|"]
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for sep in sep_list:
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if df.shape[1] == 1:
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df = pd.read_csv(file, sep=sep)
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else:
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break
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else:
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raise ValueError(f"Unsupported file type: {file}")
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return df
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def get_samples(df: pd.DataFrame) -> str:
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data = []
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if len(df) > 5:
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df_ = df.sample(5, random_state=0)
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else:
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df_ = df
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for i in list(df_):
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nan_freq = float("%.2g" % (df[i].isna().mean() * 100))
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n_unique = df[i].nunique()
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s = df_[i].tolist()
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if str(df[i].dtype) == "float64":
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s = [round(sample, 2) if not pd.isna(sample) else None for sample in s]
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data.append([df_[i].name, df[i].dtype, nan_freq, n_unique, s])
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samples = pd.DataFrame(
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data,
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columns=[
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"Column_name",
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"Data_type",
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"NaN_Frequency(%)",
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"N_unique",
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"Samples",
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],
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)
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return samples.to_string(index=False)
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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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@ -66,24 +123,47 @@ class AskReview(Action):
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return rsp, confirmed
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class WriteTaskGuide(Action):
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async def run(self, task_instruction: str, data_desc: str = "") -> str:
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return ""
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# class WriteTaskGuide(Action):
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# async def run(self, task_instruction: str, data_desc: dict = None) -> str:
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# return ""
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class GenerateDataDesc(Action):
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async def run(self, files: list) -> dict:
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data_desc = {}
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for file in files:
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df = read_data(file)
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file_name = file.split("/")[-1]
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data_head = df.head().to_dict(orient="list")
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data_head = json.dumps(data_head, indent=4, ensure_ascii=False)
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prompt = GEN_DATA_DESC_PROMPT.replace("{data_head}", data_head)
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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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data_desc[file_name] = {}
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data_desc[file_name]["path"] = file
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data_desc[file_name]["description"] = rsp
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data_desc[file_name]["column_info"] = get_samples(df)
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return data_desc
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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, data_path: str = None
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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.plan = Plan(goal=goal)
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self.use_tools = False
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self.use_task_guide = False
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self.use_tools = True
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self.use_task_guide = True
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self.execute_code = ExecutePyCode()
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self.auto_run = auto_run
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self.data_path = data_path
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self.data_desc = {}
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async def _plan_and_act(self):
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if self.data_path:
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self.data_desc = await self._generate_data_desc()
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# create initial plan and update until confirmation
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await self._update_plan()
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@ -108,9 +188,14 @@ class MLEngineer(Role):
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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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async def _generate_data_desc(self):
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files = glob.glob(self.data_path + "/*.csv")
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data_desc = await GenerateDataDesc().run(files=files)
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return data_desc
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async def _write_and_exec_code(self, max_retry: int = 3):
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task_guide = (
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await WriteTaskGuide().run(self.plan.current_task.instruction)
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await WriteTaskGuide().run(self.plan)
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if self.use_task_guide
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else ""
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)
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@ -126,14 +211,16 @@ class MLEngineer(Role):
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# breakpoint()
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if not self.use_tools or self.plan.current_task.task_type == "other":
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logger.info("Write code with pure generation")
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# code = "print('abc')"
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code = await WriteCodeByGenerate().run(
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context=context, plan=self.plan, task_guide=task_guide, temperature=0.0
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)
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cause_by = WriteCodeByGenerate
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else:
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logger.info("Write code with tools")
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code = await WriteCodeWithTools().run(
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context=context, plan=self.plan, task_guide=task_guide, data_desc=""
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context=context, plan=self.plan, task_guide=task_guide
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)
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cause_by = WriteCodeWithTools
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@ -192,7 +279,10 @@ class MLEngineer(Role):
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)
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current_task = self.plan.current_task.json() if self.plan.current_task else {}
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context = STRUCTURAL_CONTEXT.format(
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user_requirement=user_requirement, tasks=tasks, current_task=current_task
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user_requirement=user_requirement,
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data_desc=self.data_desc,
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tasks=tasks,
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current_task=current_task
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)
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context_msg = [Message(content=context, role="user")]
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@ -204,14 +294,17 @@ class MLEngineer(Role):
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if __name__ == "__main__":
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requirement = "Run data analysis on sklearn Iris dataset, include a plot"
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# requirement = "Run data analysis on sklearn Iris dataset, include a plot.."
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# requirement = "Run data analysis on sklearn Diabetes dataset, include a plot"
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# 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"
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# 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"
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# 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"
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async def main(requirement: str = requirement, auto_run: bool = False):
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role = MLEngineer(goal=requirement, auto_run=auto_run)
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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."
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data_path = "/data/lidanyang/tabular_data/titanic"
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async def main(requirement: str = requirement, auto_run: bool = True, data_path: str = data_path):
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role = MLEngineer(goal=requirement, auto_run=auto_run, data_path=data_path)
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await role.run(requirement)
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fire.Fire(main)
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