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rename research assistant to experimenter
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Cyzus Chi
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14 changed files with 33 additions and 33 deletions
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@ -213,7 +213,7 @@ #### Run
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The `log` folder will contain the experimental configuration and the generated scheme, and the `workspace` folder will save the final results generated by aide
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```
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python experimenter/aide.py
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python runner/aide.py
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```
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### Autogluon
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@ -1,7 +1,7 @@
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import os
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from metagpt.ext.sela.data.dataset import SPECIAL_INSTRUCTIONS
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from metagpt.ext.sela.experimenter.mle_bench.instructions import (
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from metagpt.ext.sela.runner.mle_bench.instructions import (
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ADDITIONAL_NOTES,
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INSTRUCTIONS,
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INSTRUCTIONS_OBFUSCATED,
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@ -2,12 +2,12 @@ import argparse
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import asyncio
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from metagpt.ext.sela.data.custom_task import get_mle_is_lower_better, get_mle_task_id
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from metagpt.ext.sela.experimenter.autogluon import GluonExperimenter
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from metagpt.ext.sela.experimenter.autosklearn import AutoSklearnExperimenter
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from metagpt.ext.sela.experimenter.custom import CustomExperimenter
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from metagpt.ext.sela.experimenter.experimenter import Experimenter
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from metagpt.ext.sela.experimenter.mcts import MCTSExperimenter
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from metagpt.ext.sela.experimenter.random_search import RandomSearchExperimenter
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from metagpt.ext.sela.runner.autogluon import GluonRunner
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from metagpt.ext.sela.runner.autosklearn import AutoSklearnRunner
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from metagpt.ext.sela.runner.custom import CustomRunner
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from metagpt.ext.sela.runner.mcts import MCTSRunner
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from metagpt.ext.sela.runner.random_search import RandomSearchRunner
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from metagpt.ext.sela.runner.runner import Runner
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def get_args(cmd=True):
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@ -74,24 +74,24 @@ def get_di_args(parser):
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async def main(args):
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if args.exp_mode == "mcts":
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experimenter = MCTSExperimenter(args)
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runner = MCTSRunner(args)
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elif args.exp_mode == "greedy":
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experimenter = MCTSExperimenter(args, tree_mode="greedy")
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runner = MCTSRunner(args, tree_mode="greedy")
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elif args.exp_mode == "random":
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experimenter = MCTSExperimenter(args, tree_mode="random")
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runner = MCTSRunner(args, tree_mode="random")
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elif args.exp_mode == "rs":
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experimenter = RandomSearchExperimenter(args)
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runner = RandomSearchRunner(args)
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elif args.exp_mode == "base":
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experimenter = Experimenter(args)
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runner = Runner(args)
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elif args.exp_mode == "autogluon":
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experimenter = GluonExperimenter(args)
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runner = GluonRunner(args)
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elif args.exp_mode == "custom":
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experimenter = CustomExperimenter(args)
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runner = CustomRunner(args)
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elif args.exp_mode == "autosklearn":
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experimenter = AutoSklearnExperimenter(args)
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runner = AutoSklearnRunner(args)
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else:
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raise ValueError(f"Invalid exp_mode: {args.exp_mode}")
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await experimenter.run_experiment()
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await runner.run_experiment()
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if __name__ == "__main__":
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@ -3,7 +3,7 @@ from datetime import datetime
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import pandas as pd
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from metagpt.ext.sela.experimenter.custom import CustomExperimenter
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from metagpt.ext.sela.runner.custom import CustomRunner
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class AGRunner:
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@ -102,7 +102,7 @@ class AGRunner:
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return train_data, dev_data, dev_wo_target_data, test_data
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class GluonExperimenter(CustomExperimenter):
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class GluonRunner(CustomRunner):
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result_path: str = "results/autogluon"
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def __init__(self, args, **kwargs):
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@ -4,7 +4,7 @@ from functools import partial
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import pandas as pd
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from metagpt.ext.sela.evaluation.evaluation import evaluate_score
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from metagpt.ext.sela.experimenter.custom import CustomExperimenter
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from metagpt.ext.sela.runner.custom import CustomRunner
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def custom_scorer(y_true, y_pred, metric_name):
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@ -69,7 +69,7 @@ class ASRunner:
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return {"test_preds": test_preds, "dev_preds": dev_preds}
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class AutoSklearnExperimenter(CustomExperimenter):
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class AutoSklearnRunner(CustomRunner):
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result_path: str = "results/autosklearn"
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def __init__(self, args, **kwargs):
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@ -3,11 +3,11 @@ import os
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import pandas as pd
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from metagpt.ext.sela.evaluation.evaluation import evaluate_score
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from metagpt.ext.sela.experimenter.experimenter import Experimenter
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from metagpt.ext.sela.runner.runner import Runner
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from metagpt.ext.sela.search.tree_search import create_initial_state
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class CustomExperimenter(Experimenter):
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class CustomRunner(Runner):
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result_path: str = "results/custom"
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def __init__(self, args, **kwargs):
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@ -5,11 +5,11 @@ from metagpt.ext.sela.evaluation.evaluation import (
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node_evaluate_score_sela,
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)
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from metagpt.ext.sela.evaluation.visualize_mcts import get_tree_text
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from metagpt.ext.sela.experimenter.experimenter import Experimenter
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from metagpt.ext.sela.runner.runner import Runner
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from metagpt.ext.sela.search.search_algorithm import MCTS, Greedy, Random
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class MCTSExperimenter(Experimenter):
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class MCTSRunner(Runner):
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result_path: str = "results/mcts"
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def __init__(self, args, tree_mode=None, **kwargs):
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@ -8,7 +8,7 @@ You will be given a machine learning task. You must solve the task by training a
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BENCHMARK INSTRUCTIONS
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------
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First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `experimenter/mle_bench/instructions.py`.
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First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `runner/mle_bench/instructions.py`.
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- **Competition-specific instructions**: The instructions for the competition are available in `{dataset_dir}/description.md`. Read this file to understand the problem setup for the competition you are currently solving.
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- **Dataset**: The dataset files for the competition are available in `{dataset_dir}`.
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@ -28,7 +28,7 @@ You will be given a machine learning task. You must solve the task by training a
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BENCHMARK INSTRUCTIONS
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------
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First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `experimenter/mle_bench/instructions.py`.
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First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `runner/mle_bench/instructions.py`.
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- **Task-specific instructions**: The instructions for the task are available in `{dataset_dir}/description.md`. Read this file to understand the problem setup for the task you are currently solving.
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- **Dataset**: The dataset files for the task are available in `{dataset_dir}/`.
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@ -1,6 +1,6 @@
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from metagpt.ext.sela.experimenter.experimenter import Experimenter
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from metagpt.ext.sela.experimenter import ResearchAssistant
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from metagpt.ext.sela.insights.instruction_generator import InstructionGenerator
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from metagpt.ext.sela.research_assistant import ResearchAssistant
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from metagpt.ext.sela.runner.runner import Runner
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from metagpt.ext.sela.utils import get_exp_pool_path
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EXPS_PROMPT = """
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@ -10,7 +10,7 @@ When doing the tasks, you can refer to the insights below:
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"""
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class RandomSearchExperimenter(Experimenter):
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class RandomSearchRunner(Runner):
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result_path: str = "results/random_search"
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async def run_experiment(self):
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@ -6,12 +6,12 @@ import numpy as np
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import pandas as pd
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from metagpt.ext.sela.evaluation.evaluation import evaluate_score
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from metagpt.ext.sela.research_assistant import ResearchAssistant
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from metagpt.ext.sela.experimenter import ResearchAssistant
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from metagpt.ext.sela.search.tree_search import create_initial_state
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from metagpt.ext.sela.utils import DATA_CONFIG, save_notebook
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class Experimenter:
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class Runner:
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result_path: str = "results/base"
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data_config = DATA_CONFIG
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start_task_id = 1
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@ -15,8 +15,8 @@ from metagpt.ext.sela.data.dataset import (
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get_split_dataset_path,
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)
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from metagpt.ext.sela.evaluation.evaluation import evaluate_score
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from metagpt.ext.sela.experimenter import ResearchAssistant, TimeoutException
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from metagpt.ext.sela.insights.instruction_generator import InstructionGenerator
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from metagpt.ext.sela.research_assistant import ResearchAssistant, TimeoutException
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from metagpt.ext.sela.utils import get_exp_pool_path, load_execute_notebook, mcts_logger
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from metagpt.tools.tool_recommend import ToolRecommender
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from metagpt.utils.common import read_json_file
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