add end2end

metagpt/ext/sela/end2end_demo.py

@@ -0,0 +1,227 @@
+import os
+import fire
+import asyncio
+import json
+import argparse
+import shutil
+from typing import Optional
+from metagpt.llm import LLM
+
+from metagpt.ext.sela.data.custom_task import get_mle_is_lower_better, get_mle_task_id
+from metagpt.ext.sela.runner.autogluon import GluonRunner
+from metagpt.ext.sela.runner.autosklearn import AutoSklearnRunner
+from metagpt.ext.sela.runner.custom import CustomRunner
+from metagpt.ext.sela.runner.mcts import MCTSRunner
+from metagpt.ext.sela.runner.random_search import RandomSearchRunner
+from metagpt.ext.sela.runner.runner import Runner
+
+from metagpt.ext.sela.evaluation.evaluation import (
+    node_evaluate_score_mlebench,
+    node_evaluate_score_sela,
+)
+from metagpt.ext.sela.evaluation.visualize_mcts import get_tree_text
+from metagpt.ext.sela.runner.runner import Runner
+from metagpt.ext.sela.search.search_algorithm import MCTS, Greedy, Random
+
+
+class Sela:
+    DEFAULT_CONFIG = {
+        "name": "",
+        "reflection": True,
+        "no_reflection": False,
+        "exp_mode": "mcts",
+        "rollouts": 10,
+        "load_tree": False,
+        "role_timeout": 1000,
+        "use_fixed_insights": False,
+        "low_is_better": False,
+        "start_task_id": 2,
+        "from_scratch": False,
+        "eval_func": "sela",
+        "custom_dataset_dir": None,
+        "max_depth": 4,
+        "rs_mode": "single",
+        "is_multimodal": True,
+        "num_experiments": 1,
+        "external_eval": True,
+        "no_external_eval": False,
+        "special_instruction": None,
+    }
+
+    def __init__(self, use_llm: bool = True):
+        """
+        初始化 Sela 类。
+
+        Args:
+            use_llm: 是否使用 LLM 来解析 requirement。
+        """
+        self.llm = LLM() if use_llm else None
+
+    async def _parse_requirement(self, requirement: str) -> dict:
+        """
+        使用 LLM 分析实验需求，提取实验配置和实验数据信息。
+
+        Args:
+            requirement: 用户输入的实验需求描述。
+
+        Returns:
+            dict: 包含实验配置和实验数据信息的字典。
+        """
+        if not self.llm:
+            raise ValueError("LLM is not initialized. Cannot parse the requirement.")
+
+        # 确保 `requirement` 是安全的字符串
+        sanitized_requirement = json.dumps(requirement)  # 将字符串转为 JSON 安全字符串
+
+        prompt = f"""
+        You are an assistant that helps configure machine learning experiments.
+
+        Given the following requirement:
+        {sanitized_requirement}
+
+        Your task:
+        1. Extract **experiment configurations** from the requirement if they are explicitly mentioned.
+           For example, "rollouts: 10", "exp_mode: mcts", or "max_depth: 4". These should override default values.
+        2. Extract **experiment data information** from the requirement. This includes:
+           - **dataset**: The name of the dataset being used (e.g., "04_titanic").
+           - **metric**: The evaluation metric or scoring method mentioned (e.g., "f1", "rmse", "f1 weighted").
+           - **target_col**: Predict the target column `Survived` (e.g., "Survived").
+           - **user_requirement**: Any specific instructions or requirements for handling the dataset (e.g.,"Your goal is to predict the target column `Survived`."
+                   "Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. "
+                   "Report f1 on the eval data. Do not plot or make any visualizations.")
+
+        Output a JSON object containing two parts:
+        - "config": This is a dictionary containing the experiment configuration. Include only explicitly mentioned configurations. Use keys like:
+          - "task": str (e.g., "titanic")
+          - "exp_mode": str (e.g., "mcts", "rs", "base", "custom", "greedy", "autogluon", "random", "autosklearn")
+          - "rollouts": int
+          - "max_depth": int
+          - "rs_mode": str (e.g., "single", "set")
+          - "special_instruction": str (e.g., "text", "image")
+        - "data_info": This is a dictionary containing the experiment data information with keys:
+          - "dataset": str (e.g., "04_titanic")
+          - "metric": str (e.g., "f1", "rmse", "f1 weighted")
+          - "target_col": str (e.g., "Survived")
+          - "user_requirement": str (e.g., Your goal is to predict the target column `Survived`."
+                   "Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. "
+                   "Report f1 on the eval data. Do not plot or make any visualizations.")
+
+        Example output:
+        {{
+          "config": {{
+            "task": "titanic",
+            "exp_mode": "mcts",
+            "rollouts": 10
+          }},
+          "data_info": {{
+            "dataset": "04_titanic",
+            "metric": "f1",
+            "target_col": "Predict the target column Survived",
+            "user_requirement": Your goal is to predict the target column `Survived`. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. "
+                   "Report f1 on the eval data. Do not plot or make any visualizations."
+          }}
+        }}
+
+        Return only the JSON object. Do not include any comments or extra text.
+        """
+        response = await self.llm.aask(prompt)
+        print(f"LLM Response: {response}")
+
+        parsed_response = self._parse_json(response)
+        config_from_user = parsed_response.get("config", {})
+        data_info = parsed_response.get("data_info", {})
+
+        # 合并默认配置和用户提供的配置
+        config = {**self.DEFAULT_CONFIG, **config_from_user}
+        return {"config": config, "data_info": data_info}
+
+    @staticmethod
+    def _parse_json(json_string: str) -> dict:
+        """
+        解析 JSON 字符串，去除可能的 Markdown 标记。
+        """
+        json_string = json_string.strip()
+        if json_string.startswith("```json"):
+            json_string = json_string[7:].strip()
+        if json_string.endswith("```"):
+            json_string = json_string[:-3].strip()
+
+        try:
+            return json.loads(json_string)
+        except json.JSONDecodeError:
+            raise ValueError(f"Invalid JSON format: {json_string}")
+
+    def _select_runner(self, config: argparse.Namespace, data_config: dict):
+        """
+        根据配置选择适当的实验执行器。
+
+        Args:
+            config: 从 LLM 解析出的实验配置。
+
+        Returns:
+            实验执行器实例。
+        """
+        exp_mode = config.exp_mode
+        if exp_mode == "mcts":
+            return MCTSRunner(config, data_config)
+        elif exp_mode == "greedy":
+            return MCTSRunner(tree_mode="greedy")
+        elif exp_mode == "random":
+            return MCTSRunner(tree_mode="random")
+        elif exp_mode == "rs":
+            return RandomSearchRunner(config)
+        elif exp_mode == "base":
+            return Runner(config)
+        elif exp_mode == "custom":
+            return CustomRunner(config)
+        else:
+            raise ValueError(f"Invalid exp_mode: {exp_mode}")
+
+    async def run(self, requirement: str, data_dir: Optional[str] = None):
+        """
+        Args:
+            requirement: 实验需求，描述目标任务。
+            data_dir: 数据目录。
+        """
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(f"Dataset directory not found: {data_dir}")
+
+        # 使用 LLM 解析需求
+        config_all = await self._parse_requirement(requirement)
+        config_exp = config_all["config"]
+        data_info = config_all["data_info"]
+
+        # 构造默认的 data_config 文件
+        data_config = {
+            "datasets_dir": data_dir,  # 用户输入的数据目录路径
+            "work_dir": "../../workspace",  # 默认的工作目录
+            "role_dir": "storage/SELA",  # 存储角色路径
+            "datasets": {config_exp.get("task"): data_info},  # 数据集信息
+        }
+
+        # 根据需求选择适当的实验执行器
+        runner = self._select_runner(argparse.Namespace(**config_exp), data_config)
+
+        # 运行实验
+        await runner.run_experiment()
+
+
+async def main():
+    """
+    Main 函数作为入口，支持直接运行。
+    """
+    # 示例需求和数据路径
+    requirement = ("Optimize 04_titanic dataset using MCTS with 10 rollouts. "
+                   "Your goal is to predict the target column `Survived`."
+                   "Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. "
+                   "Report f1 on the eval data. Do not plot or make any visualizations.")
+
+    data_dir = "/home/coder/project/chenxin/MetaGPT/metagpt/ext/sela/data/SELA-datasets"
+
+    # 初始化 Sela 并运行
+    sela = Sela()
+    await sela.run(requirement, data_dir)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

metagpt/ext/sela/runner/mcts.py

@@ -12,7 +12,7 @@
 class MCTSRunner(Runner):
     result_path: str = "results/mcts"
 
-    def __init__(self, args, tree_mode=None, **kwargs):
+    def __init__(self, args, data_config=None, tree_mode=None, **kwargs):
         if args.special_instruction == "image":
             self.start_task_id = 1  # start from datapreprocessing if it is image task
         else:
@@ -23,7 +23,7 @@ def __init__(self, args, tree_mode=None, **kwargs):
         elif args.eval_func == "mlebench":
             self.eval_func = node_evaluate_score_mlebench
 
-        super().__init__(args, **kwargs)
+        super().__init__(args, data_config=None, **kwargs)
         self.tree_mode = tree_mode
 
     async def run_experiment(self):

metagpt/ext/sela/runner/runner.py

@@ -16,14 +16,14 @@ class Runner:
     data_config = DATA_CONFIG
     start_task_id = 1
 
-    def __init__(self, args, **kwargs):
+    def __init__(self, args, data_config=None,**kwargs):
         self.args = args
         self.start_time_raw = datetime.datetime.now()
         self.start_time = self.start_time_raw.strftime("%Y%m%d%H%M")
         self.state = create_initial_state(
             self.args.task,
             start_task_id=self.start_task_id,
-            data_config=self.data_config,
+            data_config=data_config if data_config is not None else self.data_config,
             args=self.args,
         )