From 95d7160c0e64af3a23da8711afb67553a0e72eaa Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 30 Aug 2024 16:26:05 +0800 Subject: [PATCH 001/135] add expo --- expo/MCTS.py | 347 +++++++++++++++++++++++++ expo/data.yaml | 157 +++++++++++ expo/dataset.py | 262 +++++++++++++++++++ expo/datasets.yaml | 134 ++++++++++ expo/evaluation/evaluation.py | 23 ++ expo/evaluation/visualize_mcts.py | 54 ++++ expo/experimenter/aug_experimenter.py | 0 expo/experimenter/experimenter.py | 18 ++ expo/experimenter/mcts_experimenter.py | 0 expo/insights/InsightGenerate.py | 114 ++++++++ expo/insights/solution_designer.py | 127 +++++++++ expo/research_assistant.py | 141 ++++++++++ expo/results/PLACEHOLDER | 0 expo/results/tree/TREE | 0 expo/run_exp_augmentation.py | 96 +++++++ expo/run_experiment.py | 44 ++++ expo/run_mcts.py | 48 ++++ expo/utils.py | 150 +++++++++++ 18 files changed, 1715 insertions(+) create mode 100644 expo/MCTS.py create mode 100644 expo/data.yaml create mode 100644 expo/dataset.py create mode 100644 expo/datasets.yaml create mode 100644 expo/evaluation/evaluation.py create mode 100644 expo/evaluation/visualize_mcts.py create mode 100644 expo/experimenter/aug_experimenter.py create mode 100644 expo/experimenter/experimenter.py create mode 100644 expo/experimenter/mcts_experimenter.py create mode 100644 expo/insights/InsightGenerate.py create mode 100644 expo/insights/solution_designer.py create mode 100644 expo/research_assistant.py create mode 100644 expo/results/PLACEHOLDER create mode 100644 expo/results/tree/TREE create mode 100644 expo/run_exp_augmentation.py create mode 100644 expo/run_experiment.py create mode 100644 expo/run_mcts.py create mode 100644 expo/utils.py diff --git a/expo/MCTS.py b/expo/MCTS.py new file mode 100644 index 000000000..9026e09b4 --- /dev/null +++ b/expo/MCTS.py @@ -0,0 +1,347 @@ +import random +import math +import os +import pandas as pd +from expo.research_assistant import ResearchAssistant +from expo.insights.InsightGenerate import InsightGenerator +from expo.dataset import get_split_dataset_path +from expo.evaluation.evaluation import evaluate_score +from expo.utils import mcts_logger, load_execute_notebook, generate_task_requirement, get_exp_pool_path + +from metagpt.tools.tool_recommend import BM25ToolRecommender, ToolRecommender +from metagpt.utils.common import write_json_file, read_json_file, format_trackback_info +import numpy as np +import pickle + +def initialize_di_root_node(task, data_config, low_is_better=False, reflection=True, name=""): + start_task_id = 2 + state = create_initial_state(task, start_task_id=start_task_id, data_config=data_config, low_is_better=low_is_better, name=name) + role = ResearchAssistant(node_id="0", start_task_id=start_task_id, use_reflection=reflection, role_dir=state["node_dir"]) + return role, Node(parent=None, state=state, action=None, value=0) + + +def create_initial_state(task, start_task_id, data_config, low_is_better, name): + initial_state = { + "task": task, + "work_dir": data_config["work_dir"], + "node_dir": os.path.join(data_config["work_dir"], data_config["role_dir"], f"{task}{name}"), + "dataset_config": data_config["datasets"][task], + "datasets_dir": get_split_dataset_path(task, data_config), + "exp_pool_path": get_exp_pool_path(task, data_config, pool_name="ds_analysis_pool"), + "requirement": generate_task_requirement(task, data_config), + "has_run": False, + "start_task_id": start_task_id, + "low_is_better": low_is_better, + } + return initial_state + + +class Node(): + state : dict = {} + action : str = None + value : float = 0 + visited : int = 0 + children : list = [] + parent = None + + def __init__(self, parent=None, state = None, action=None, value = 0, max_depth=4, **kwargs): + self.state = state + self.action = action + self.value = value + self.raw_value = 0 + self.raw_reward = dict() + self.parent = parent + self.children = [] + self.max_depth = max_depth + self.depth = self.generate_depth() + self.id = self.generate_id() + if self.parent is not None: + self.save_node() + + def avg_value(self): + if self.visited == 0: + return 0 + return self.value / self.visited + + def __hash__(self): + return hash(self.id) + + def save_node(self): + os.makedirs(self.state["node_dir"], exist_ok=True) + with open(os.path.join(self.state["node_dir"], f"Node-{self.id}.pkl"), 'wb') as f: + pickle.dump(self, f) + + def load_node(self): + with open(os.path.join(self.state["node_dir"], f"Node-{self.id}.pkl"), 'rb') as f: + return pickle.load(f) + + def get_depth(self): + return self.depth + + def generate_depth(self): + if self.parent is None: + return 0 + else: + return self.parent.depth + 1 + + def generate_id(self): + if self.parent is None: + return "0" + else: + num_sibling = len(self.parent.children) + return f"{self.parent.id}-{num_sibling}" + + def is_terminal(self): + return int(self.state["start_task_id"]) == self.max_depth + 1 + + def is_fully_expanded(self): + return len(self.children) > 0 + + def add_child(self, child_node): + self.children.append(child_node) + + def update(self, reward:dict, child_node=None): + if child_node is not None: + child_role = child_node.load_role() + role = self.load_role() + role.update_til_start_task(child_role) + role.save_state() + else: + self.raw_value = reward["test_score"] + self.value += reward["score"] + self.visited += 1 + self.save_node() + + def get_role_path(self): + fname = f"Node-{self.id}.json" + role_path = os.path.join(self.state["node_dir"], fname) + return role_path + + def load_role(self): + role_dict = read_json_file(self.get_role_path()) + if role_dict.get('tool_recommender') is None: + role_dict['tool_recommender'] = ToolRecommender() + elif isinstance(role_dict.get('tool_recommender', {}).get('tools'), dict): + role_dict['tool_recommender']['tools'] = list(role_dict['tool_recommender']['tools'].keys()) + role = ResearchAssistant(**role_dict) + if self.parent is not None: # TODO: Check this + parent_role = self.parent.load_role() + role.update_til_start_task(parent_role, backward=False) + role.remap_tasks() + return role + + def save_new_role(self, role: ResearchAssistant): + role.node_id = self.id + role.start_task_id = self.state['start_task_id'] + role.state_saved = False + role.change_next_instruction(self.action) + mcts_logger.log("MCTS", f"保存新的role: {role.node_id}") + role.save_state(static_save=True) + + async def expand(self, max_children): + if self.is_fully_expanded(): + return + insight_geneartor = InsightGenerator() + role = self.load_role() + original_instruction = role.get_next_instruction() + insights = await insight_geneartor.generate_new_instructions(task_id=role.start_task_id + 1, + original_instruction=original_instruction, + max_num=max_children, + file_path=self.state["exp_pool_path"]) + new_state = self.state.copy() + new_state['start_task_id'] += 1 + for insight in insights: + new_role = role.model_copy() + node = Node(parent=self, state=new_state, action=insight, value=0) + node.save_new_role(new_role) + self.add_child(node) + + # def evaluate_test(self): + # prediction_fpath = os.path.join(self.state["work_dir"], self.state["task"], "predictions.csv") + # predictions = pd.read_csv(prediction_fpath)["target"] + # # copy predictions.csv to the node_dir + # predictions_node_fpath = os.path.join(self.state["node_dir"], "Node-{self.id}-predictions.csv") + # predictions.to_csv(predictions_node_fpath, index=False) + # # load test_target.csv + # split_datasets_dir = self.state["datasets_dir"] + # gt = pd.read_csv(os.path.join(split_datasets_dir["test_target"]))["target"] + # metric = self.state["dataset_config"]["metric"] + # return evaluate_score(predictions, gt, metric) + + def evaluate_prediction(self, split): + pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}-predictions.csv") + pred_node_path = os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}-predictions.csv") + gt_path = os.path.join(self.state["datasets_dir"][f"{split}_target"]) + preds = pd.read_csv(pred_path)["target"] + preds.to_csv(pred_node_path, index=False) + gt = pd.read_csv(gt_path)["target"] + metric = self.state["dataset_config"]["metric"] + return evaluate_score(preds, gt, metric) + + def evaluate_simulation(self, score_dict): + scores = { + "dev_score": self.evaluate_prediction("dev"), + "test_score": self.evaluate_prediction("test") + } + score_dict.update(scores) + return score_dict + + + async def run_node(self, role=None): + if self.is_terminal() and role is not None: + if role.state_saved: + return self.raw_reward + + if not role: + role = self.load_role() + await load_execute_notebook(role) # execute previous notebook's code + await role.run(with_message='continue') + else: + await role.run(with_message=self.state['requirement']) + score_dict = await role.get_score() + score_dict = self.evaluate_simulation(score_dict) + self.raw_reward = score_dict + + if self.state["low_is_better"]: + # normalized the score to be between 0 and 1, and higher is better + def normalize_score(score): + return 1 / (1 + score) + score_dict = {k: normalize_score(v) for k, v in score_dict.items()} + return score_dict + + +class MCTS(): + #data_path + root_node : Node = None + children : dict = {} + max_depth : int = 5 + c_explore : float = 1.4 + c_unvisited : float = 0.8 + + def __init__(self, root_node, max_depth): + self.root_node = root_node + self.max_depth = max_depth + + def select(self, node: Node): + node = self.best_child() + mcts_logger.log("MCTS", f"选择的叶子节点id: {node.id}") + return node + + def best_child(self): + def uct(node: Node): + n_visits = node.visited if node.visited else self.c_unvisited + avg_value = node.avg_value() if node.visited else node.value/self.c_unvisited + return avg_value + self.c_explore * math.sqrt(math.log(node.parent.visited) / n_visits) + if len(self.children) == 0: + return self.root_node + all_children = [child for children in self.children.values() for child in children] + return max(all_children, key=uct) + + async def expand(self, node : Node, max_children=4): + await node.expand(max_children) + if node not in self.children or not self.children[node]: + self.children[node] = node.children + return node.children + + async def simulate(self, node : Node, role=None): + "Returns the reward for a random simulation (to completion) of `node`" + while node.children: + node = random.choice(node.children) + reward = await node.run_node(role) + return reward + + + def backpropagate(self, node : Node, reward): + child_node = node + node.update(reward) + node = node.parent + while node is not None: + node.update(reward, child_node) + node, child_node = node.parent, node + + def best_path(self, root : Node): + best_child = root + best_score = 0 + def bfs(node : Node, best_score, best_child : Node): + if node not in self.children: + return best_score, best_child + for child in self.children[node]: + print(child.id, child.raw_value) + if child.raw_value > best_score: + best_score = child.raw_value + best_child = child + best_score, best_child = bfs(child, best_score, best_child) + return best_score, best_child + best_score, best_child = bfs(root, best_score, best_child) + mcts_logger.log("MCTS", f"Best Score: {best_score}, Best Node ID: {best_child.id}") + return best_child + + def get_num_simulations(self): + return self.root_node.visited + + async def search(self, task, data_config, name, + rollout=3, load_tree=False, low_is_better=False, reflection=False): + + role, root = initialize_di_root_node(task, data_config, low_is_better=low_is_better, reflection=reflection, name=name) + self.root_node = root + tree_loaded = False + if load_tree: + tree_loaded = self.load_tree() + mcts_logger.log("MCTS", f"Number of simulations: {self.get_num_simulations()}") + if not tree_loaded: + self.children[root] = [] + reward = await self.simulate(root, role) + self.backpropagate(root, reward) + mcts_logger.log("MCTS", f"Root node's value: {reward}") + children = await self.expand(root) + #目前是随机选择1个，后续可以改成多个 + first_leaf = random.choice(children) + mcts_logger.log("MCTS", f"随机选择的叶子节点id: {first_leaf.id}") + reward = await self.simulate(first_leaf) + mcts_logger.log("MCTS", f"模拟完毕的叶子节点的Normalized score: {reward}") + self.backpropagate(first_leaf, reward) + else: + root = self.root_node + # 后续迭代：使用UCT进行选择，expand并模拟和反向传播 + for _ in range(rollout): # 迭代次数 + mcts_logger.log("MCTS", f"开始第{_+1}次迭代") + leaf = self.select(root) + if leaf.is_terminal(): + if leaf.raw_value == 0: + reward = await self.simulate(leaf) + else: + reward = {"test_score": leaf.raw_value, "score": leaf.value} + mcts_logger.log("MCTS", f"终止节点的得分为: {reward}") + self.backpropagate(leaf, reward) + else: + if leaf.visited > 0: + children = await self.expand(leaf) + leaf = random.choice(children) + mcts_logger.log("MCTS", f"随机选择的叶子节点id: {leaf.id}") + reward = await self.simulate(leaf) + mcts_logger.log("MCTS", f"模拟完毕的叶子节点{leaf.id}的Normalized score: {reward}") + self.backpropagate(leaf, reward) + return self.best_path(root) + + + def load_tree(self): + def load_children_node(node): + mcts_logger.log("MCTS", f"加载节点{node.id}的子节点：{node.children}") + if node.is_terminal() or not node.children: + return + for child in node.children: + child.load_node() + self.children[child] = child.children + load_children_node(child) + # Load all pkl files in the node_dir + all_pkl_files = os.listdir(self.root_node.state["node_dir"]) + all_pkl_files = [f for f in all_pkl_files if f.endswith(".pkl")] + if os.path.exists(os.path.join(self.root_node.state["node_dir"], "Node-0.pkl")): + with open(os.path.join(self.root_node.state["node_dir"], "Node-0.pkl"), 'rb') as f: + self.root_node = pickle.load(f) + self.children[self.root_node] = self.root_node.children + load_children_node(self.root_node) + if self.children: + mcts_logger.log("MCTS", "成功加载树") + return True + return False \ No newline at end of file diff --git a/expo/data.yaml b/expo/data.yaml new file mode 100644 index 000000000..d921e1ebf --- /dev/null +++ b/expo/data.yaml @@ -0,0 +1,157 @@ +datasets_dir: "D:/work/automl/datasets" # path to the datasets directory + +datasets: + titanic: + dataset: "04_titanic" + user_requirement: "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. Don't plot." + metric: "accuracy" + + house_prices: + dataset: "05_house-prices-advanced-regression-techniques" + user_requirement: "This is a house price dataset, your goal is to predict the sale price of a property based on its features. Make sure to generate at least 5 tasks each time, including eda, data preprocessing, feature engineering, model training to predict the target, and model evaluation. Report RMSE between the logarithm of the predicted value and the logarithm of the observed sale prices on the eval data. The target column is 'SalePrice'. Please do not include any processing of the target column in the data preprocessing and feature engineering stages. Don't plot." + metric: "log rmse" + + santander_customers: + dataset: "06_santander-customer-transaction-prediction" + user_requirement: "This is a customers financial dataset. Your goal is to predict which customers will make a specific transaction in the future. The target column is target. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report AUC on the eval data. Don't plot." + metric: "auc" + + icr: + dataset: "07_icr-identify-age-related-conditions" + user_requirement: "ICR dataset 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. Make sure to generate at least 5 tasks each time, including eda, data preprocessing, feature engineering, model training to predict the target, and model evaluation. The target column is Class. Report F1 Score on the eval data. Don't plot." + metric: "f1" + + santander_value: + dataset: "08_santander-value-prediction-challenge" + user_requirement: "This is a regression problem. Your goal is to predict the value of transactions for potential customers. The target column is target. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report RMSE on the eval data. Don't plot." + metric: "rmse" + + load_wine: + dataset: None + user_requirement: "Analyze the 'load_wine' dataset from sklearn to predict wine quality. Visualize relationships between features, use machine learning for classification, and report model accuracy. Include analysis and prediction visualizations. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Don't plot!" + metric: "accuracy" + + lick_prediction_small: + dataset: Click_prediction_small + metric: f1 + user_requirement: "This is a Click_prediction_small dataset. Your goal is to predict\ + \ the target column `click`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + \ Do not plot or make any visualizations.\n" + + GesturePhaseSegmentationProcessed: + dataset: GesturePhaseSegmentationProcessed + metric: f1 weighted + user_requirement: "This is a GesturePhaseSegmentationProcessed dataset. Your goal\ + \ is to predict the target column `Phase`.\nPerform data analysis, data preprocessing,\ + \ feature engineering, and modeling to predict the target. \nReport f1 weighted\ + \ on the eval data. Do not plot or make any visualizations.\n" + + Moneyball: + dataset: Moneyball + metric: rmse + user_requirement: "This is a Moneyball dataset. Your goal is to predict the target\ + \ column `RS`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ + \ plot or make any visualizations.\n" + + SAT11-HAND-runtime-regression: + dataset: SAT11-HAND-runtime-regression + metric: rmse + user_requirement: "This is a SAT11-HAND-runtime-regression dataset. Your goal\ + \ is to predict the target column `runtime`.\nPerform data analysis, data preprocessing,\ + \ feature engineering, and modeling to predict the target. \nReport rmse on\ + \ the eval data. Do not plot or make any visualizations.\n" + + boston: + dataset: boston + metric: rmse + user_requirement: "This is a boston dataset. Your goal is to predict the target\ + \ column `MEDV`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ + \ plot or make any visualizations.\n" + + colleges: + dataset: colleges + metric: rmse + user_requirement: "This is a colleges dataset. Your goal is to predict the target\ + \ column `percent_pell_grant`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport rmse on the eval\ + \ data. Do not plot or make any visualizations.\n" + + credit-g: + dataset: credit-g + metric: f1 + user_requirement: "This is a credit-g dataset. Your goal is to predict the target\ + \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + + diamonds: + dataset: diamonds + metric: rmse + user_requirement: "This is a diamonds dataset. Your goal is to predict the target\ + \ column `price`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ + \ plot or make any visualizations.\n" + + jasmine: + dataset: jasmine + metric: f1 + user_requirement: "This is a jasmine dataset. Your goal is to predict the target\ + \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + + kc1: + dataset: kc1 + metric: f1 + user_requirement: "This is a kc1 dataset. Your goal is to predict the target column\ + \ `defects`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + + kick: + dataset: kick + metric: f1 + user_requirement: "This is a kick dataset. Your goal is to predict the target\ + \ column `IsBadBuy`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + + mfeat-factors: + dataset: mfeat-factors + metric: f1 weighted + user_requirement: "This is a mfeat-factors dataset. Your goal is to predict the\ + \ target column `class`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" + + segment: + dataset: segment + metric: f1 weighted + user_requirement: "This is a segment dataset. Your goal is to predict the target\ + \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ + \ Do not plot or make any visualizations.\n" + + steel-plates-fault: + dataset: steel-plates-fault + metric: f1 weighted + user_requirement: "This is a steel-plates-fault dataset. Your goal is to predict\ + \ the target column `target`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" + + wine-quality-white: + dataset: wine-quality-white + metric: f1 weighted + user_requirement: "This is a wine-quality-white dataset. Your goal is to predict\ + \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" + + +work_dir: D:/work/MG-open/MetaGPT/workspace # path to the workspace directory +role_dir: storage/team/environment/roles/ResearchAssistant_David +# analysis_pool_dir: D:/work/MG-open/MetaGPT/examples/MCTS_test/analysis_pool_sample.json \ No newline at end of file diff --git a/expo/dataset.py b/expo/dataset.py new file mode 100644 index 000000000..4bce6e9fe --- /dev/null +++ b/expo/dataset.py @@ -0,0 +1,262 @@ +import openml +from pathlib import Path +from sklearn.model_selection import train_test_split +import os +import json +import yaml +import pandas as pd +from examples.MCTS_test.insights.solution_designer import SolutionDesigner +import asyncio + +BASE_USER_REQUIREMENT = """\ +This is a {datasetname} dataset. Your goal is to predict the target column `{target_col}`. +Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. +Report {metric} on the eval data. Do not plot or make any visualizations. +""" + +SEED = 100 +TRAIN_TEST_SPLIT = 0.8 +TRAIN_DEV_SPLIT = 0.75 + +OPENML_DATASET_IDS = [ + # reg + 41021, + 42727, + 41980, + 42225, + 531, + + # cls + 41143, + 31, + 42733, + 41162, + 1067, + + # multi cls + 40498, + 40982, + 12, + 40984, + 4538, +] + +CUSTOM_DATASETS = [ + ("04_titanic", "Survived"), + ("05_house-prices-advanced-regression-techniques", "SalePrice"), + ("06_santander-customer-transaction-prediction", "target"), + ("07_icr-identify-age-related-conditions", "Class") +] + +def get_split_dataset_path(dataset_name, config): + datasets_dir = config['datasets_dir'] + if dataset_name in config['datasets']: + dataset = config['datasets'][dataset_name] + data_path = os.path.join(datasets_dir, dataset['dataset']) + split_datasets = { + "train": os.path.join(data_path, "split_train.csv"), + "dev": os.path.join(data_path, "split_dev.csv"), + "dev_wo_target": os.path.join(data_path, "split_dev_wo_target.csv"), + "dev_target": os.path.join(data_path, "split_dev_target.csv"), + "test": os.path.join(data_path, "split_test.csv"), + "test_wo_target": os.path.join(data_path, "split_test_wo_target.csv"), + "test_target": os.path.join(data_path, "split_test_target.csv"), + } + return split_datasets + else: + raise ValueError(f"Dataset {dataset_name} not found in config file. Available datasets: {config['datasets'].keys()}") + +def get_user_requirement(task_name, config): + datasets_dir = config['datasets_dir'] + if task_name in config['datasets']: + dataset = config['datasets'][task_name] + data_path = os.path.join(datasets_dir, dataset['dataset']) + user_requirement = dataset['user_requirement'] + return data_path, user_requirement + else: + raise ValueError(f"Dataset {task_name} not found in config file. Available datasets: {config['datasets'].keys()}") + + +def save_datasets_dict_to_yaml(datasets_dict): + with open("datasets.yaml", "w") as file: + yaml.dump(datasets_dict, file) + +def create_dataset_dict(dataset): + dataset_dict = { + "dataset": dataset.name, + "user_requirement": dataset.create_base_requirement(), + "metric": dataset.get_metric() + } + return dataset_dict + +class ExpDataset: + description : str = None + metadata : dict = None + dataset_dir : str = None + target_col : str = None + name : str = None + + def __init__(self, name, dataset_dir, **kwargs): + self.name = name + self.dataset_dir = dataset_dir + self.target_col = kwargs.get("target_col", None) + self.force_update = kwargs.get("force_update", False) + self.save_dataset(target_col=self.target_col) + + def check_dataset_exists(self): + fnames = ["split_train.csv", "split_dev.csv", "split_test.csv", + "split_dev_wo_target.csv", "split_dev_target.csv", + "split_test_wo_target.csv", "split_test_target.csv"] + for fname in fnames: + if not os.path.exists(Path(self.dataset_dir, self.name, fname)): + return False + return True + + def check_datasetinfo_exists(self): + return os.path.exists(Path(self.dataset_dir, self.name, "dataset_info.json")) + + + def get_raw_dataset(self): + raw_dir = Path(self.dataset_dir, self.name, "raw") + if not os.path.exists(Path(raw_dir, "train.csv")): + raise FileNotFoundError(f"Raw dataset `train.csv` not found in {raw_dir}") + else: + df = pd.read_csv(Path(raw_dir, "train.csv")) + return df + + def get_dataset_info(self): + raw_df = pd.read_csv(Path(self.dataset_dir, self.name, "raw", "train.csv")) + metadata = { + 'NumberOfClasses': raw_df[self.target_col].nunique(), + 'NumberOfFeatures': raw_df.shape[1], + 'NumberOfInstances': raw_df.shape[0], + 'NumberOfInstancesWithMissingValues': int(raw_df.isnull().any(axis=1).sum()), + 'NumberOfMissingValues': int(raw_df.isnull().sum().sum()), + 'NumberOfNumericFeatures': raw_df.select_dtypes(include=['number']).shape[1], + 'NumberOfSymbolicFeatures': raw_df.select_dtypes(include=['object']).shape[1], + } + + df_head_text = raw_df.head().to_string(index=False) + + dataset_info = { + "name": self.name, + "description": "", + "target_col": self.target_col, + "metadata": metadata, + "df_head": df_head_text + } + return dataset_info + + def get_metric(self): + dataset_info = self.get_dataset_info() + num_classes = dataset_info["metadata"]["NumberOfClasses"] + if num_classes == 2: + metric = "f1" + elif 2 < num_classes <= 200: + metric = "f1 weighted" + elif num_classes > 200 or num_classes == 0: + metric = "rmse" + else: + raise ValueError(f"Number of classes {num_classes} not supported") + return metric + + def create_base_requirement(self): + metric = self.get_metric() + req = BASE_USER_REQUIREMENT.format(datasetname=self.name, target_col=self.target_col, metric=metric) + return req + + def save_dataset(self, target_col): + + df = self.get_raw_dataset() + if not self.check_dataset_exists() or self.force_update: + print(f"Saving Dataset {self.name} in {self.dataset_dir}") + self.split_and_save(df, target_col) + else: + print(f"Dataset {self.name} already exists") + if not self.check_datasetinfo_exists() or self.force_update: + print(f"Saving Dataset info for {self.name}") + dataset_info = self.get_dataset_info() + self.save_datasetinfo(dataset_info) + else: + print(f"Dataset info for {self.name} already exists") + + def save_datasetinfo(self, dataset_info): + with open(Path(self.dataset_dir, self.name, "dataset_info.json"), "w") as file: + json.dump(dataset_info, file, indent=4) + + def save_split_datasets(self, df, split, target_col=None): + path = Path(self.dataset_dir, self.name) + df.to_csv(Path(path, f"split_{split}.csv"), index=False) + if target_col: + df_wo_target = df.drop(columns=[target_col]) + df_wo_target.to_csv(Path(path, f"split_{split}_wo_target.csv"), index=False) + df_target = df[[target_col]].copy() + if target_col != "target": + df_target["target"] = df_target[target_col] + df_target = df_target.drop(columns=[target_col]) + df_target.to_csv(Path(path, f"split_{split}_target.csv"), index=False) + + def split_and_save(self, df, target_col): + if not target_col: + raise ValueError("Target column not provided") + train, test = train_test_split(df, test_size=1-TRAIN_TEST_SPLIT, random_state=SEED) + train, dev = train_test_split(train, test_size=1-TRAIN_DEV_SPLIT, random_state=SEED) + self.save_split_datasets(train, "train") + self.save_split_datasets(dev, "dev", target_col) + self.save_split_datasets(test, "test", target_col) + + + +class OpenMLExpDataset(ExpDataset): + def __init__(self, name, dataset_dir, dataset_id, **kwargs): + self.dataset_id = dataset_id + self.dataset = openml.datasets.get_dataset(self.dataset_id, + download_data=False, + download_qualities=False, + download_features_meta_data=True) + self.name = self.dataset.name + self.target_col = self.dataset.default_target_attribute + super().__init__(self.name, dataset_dir, target_col=self.target_col, **kwargs) + + + def get_raw_dataset(self): + dataset = self.dataset + dataset_df, *_ = dataset.get_data() + raw_dir = Path(self.dataset_dir, self.name, "raw") + os.makedirs(raw_dir, exist_ok=True) + dataset_df.to_csv(Path(raw_dir, "train.csv"), index=False) + return dataset_df + + def get_dataset_info(self): + dataset_info = super().get_dataset_info() + dataset = self.dataset + dataset_info["name"] = dataset.name + dataset_info["description"] = dataset.description + dataset_info["metadata"].update(dataset.qualities) + return dataset_info + + +# class HFExpDataset(ExpDataset): +# def __init__(self, name, dataset_dir, dataset_name, **kwargs): +# super().__init__(name, dataset_dir, **kwargs) + + + +if __name__ == "__main__": + datasets_dir = "D:/work/automl/datasets" + force_update = True + datasets_dict = {"datasets": {}} + solution_designer = SolutionDesigner() + for dataset_id in OPENML_DATASET_IDS: + openml_dataset = OpenMLExpDataset("", datasets_dir, dataset_id, force_update=force_update) + asyncio.run(solution_designer.generate_solutions(openml_dataset.get_dataset_info(), openml_dataset.name)) + dataset_dict = create_dataset_dict(openml_dataset) + datasets_dict["datasets"][openml_dataset.name] = dataset_dict + + for dataset_name, target_col in CUSTOM_DATASETS: + custom_dataset = ExpDataset(dataset_name, datasets_dir, target_col=target_col, force_update=force_update) + asyncio.run(solution_designer.generate_solutions(custom_dataset.get_dataset_info(), custom_dataset.name)) + dataset_dict = create_dataset_dict(custom_dataset) + datasets_dict["datasets"][custom_dataset.name] = dataset_dict + + save_datasets_dict_to_yaml(datasets_dict) diff --git a/expo/datasets.yaml b/expo/datasets.yaml new file mode 100644 index 000000000..ec00e3d1f --- /dev/null +++ b/expo/datasets.yaml @@ -0,0 +1,134 @@ +datasets: + 04_titanic: + dataset: 04_titanic + metric: f1 + user_requirement: "This is a 04_titanic dataset. Your goal is to predict the target\ + \ column `Survived`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + 05_house-prices-advanced-regression-techniques: + dataset: 05_house-prices-advanced-regression-techniques + metric: rmse + user_requirement: "This is a 05_house-prices-advanced-regression-techniques dataset.\ + \ Your goal is to predict the target column `SalePrice`.\nPerform data analysis,\ + \ data preprocessing, feature engineering, and modeling to predict the target.\ + \ \nReport rmse on the eval data. Do not plot or make any visualizations.\n" + 06_santander-customer-transaction-prediction: + dataset: 06_santander-customer-transaction-prediction + metric: f1 + user_requirement: "This is a 06_santander-customer-transaction-prediction dataset.\ + \ Your goal is to predict the target column `target`.\nPerform data analysis,\ + \ data preprocessing, feature engineering, and modeling to predict the target.\ + \ \nReport f1 on the eval data. Do not plot or make any visualizations.\n" + 07_icr-identify-age-related-conditions: + dataset: 07_icr-identify-age-related-conditions + metric: f1 + user_requirement: "This is a 07_icr-identify-age-related-conditions dataset. Your\ + \ goal is to predict the target column `Class`.\nPerform data analysis, data\ + \ preprocessing, feature engineering, and modeling to predict the target. \n\ + Report f1 on the eval data. Do not plot or make any visualizations.\n" + Click_prediction_small: + dataset: Click_prediction_small + metric: f1 + user_requirement: "This is a Click_prediction_small dataset. Your goal is to predict\ + \ the target column `click`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + \ Do not plot or make any visualizations.\n" + GesturePhaseSegmentationProcessed: + dataset: GesturePhaseSegmentationProcessed + metric: f1 weighted + user_requirement: "This is a GesturePhaseSegmentationProcessed dataset. Your goal\ + \ is to predict the target column `Phase`.\nPerform data analysis, data preprocessing,\ + \ feature engineering, and modeling to predict the target. \nReport f1 weighted\ + \ on the eval data. Do not plot or make any visualizations.\n" + Moneyball: + dataset: Moneyball + metric: rmse + user_requirement: "This is a Moneyball dataset. Your goal is to predict the target\ + \ column `RS`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ + \ plot or make any visualizations.\n" + SAT11-HAND-runtime-regression: + dataset: SAT11-HAND-runtime-regression + metric: rmse + user_requirement: "This is a SAT11-HAND-runtime-regression dataset. Your goal\ + \ is to predict the target column `runtime`.\nPerform data analysis, data preprocessing,\ + \ feature engineering, and modeling to predict the target. \nReport rmse on\ + \ the eval data. Do not plot or make any visualizations.\n" + boston: + dataset: boston + metric: rmse + user_requirement: "This is a boston dataset. Your goal is to predict the target\ + \ column `MEDV`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ + \ plot or make any visualizations.\n" + colleges: + dataset: colleges + metric: rmse + user_requirement: "This is a colleges dataset. Your goal is to predict the target\ + \ column `percent_pell_grant`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport rmse on the eval\ + \ data. Do not plot or make any visualizations.\n" + credit-g: + dataset: credit-g + metric: f1 + user_requirement: "This is a credit-g dataset. Your goal is to predict the target\ + \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + diamonds: + dataset: diamonds + metric: rmse + user_requirement: "This is a diamonds dataset. Your goal is to predict the target\ + \ column `price`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ + \ plot or make any visualizations.\n" + jasmine: + dataset: jasmine + metric: f1 + user_requirement: "This is a jasmine dataset. Your goal is to predict the target\ + \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + kc1: + dataset: kc1 + metric: f1 + user_requirement: "This is a kc1 dataset. Your goal is to predict the target column\ + \ `defects`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + kick: + dataset: kick + metric: f1 + user_requirement: "This is a kick dataset. Your goal is to predict the target\ + \ column `IsBadBuy`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" + mfeat-factors: + dataset: mfeat-factors + metric: f1 weighted + user_requirement: "This is a mfeat-factors dataset. Your goal is to predict the\ + \ target column `class`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" + segment: + dataset: segment + metric: f1 weighted + user_requirement: "This is a segment dataset. Your goal is to predict the target\ + \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ + \ Do not plot or make any visualizations.\n" + steel-plates-fault: + dataset: steel-plates-fault + metric: f1 weighted + user_requirement: "This is a steel-plates-fault dataset. Your goal is to predict\ + \ the target column `target`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" + wine-quality-white: + dataset: wine-quality-white + metric: f1 weighted + user_requirement: "This is a wine-quality-white dataset. Your goal is to predict\ + \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" diff --git a/expo/evaluation/evaluation.py b/expo/evaluation/evaluation.py new file mode 100644 index 000000000..20a35aa27 --- /dev/null +++ b/expo/evaluation/evaluation.py @@ -0,0 +1,23 @@ +from sklearn.metrics import f1_score, accuracy_score, roc_auc_score, mean_squared_error +import numpy as np + +def evaluate_score(pred, gt, metric): + if metric == "accuracy": + return accuracy_score(gt, pred) + elif metric == "f1": + unique_classes = np.unique(gt) + if 1 in unique_classes and 0 in unique_classes: + pos_label = 1 + else: + pos_label = unique_classes[0] if len(unique_classes) == 2 else None + return f1_score(gt, pred, pos_label=pos_label) + elif metric == "f1 weighted": + return f1_score(gt, pred, average="weighted") + elif metric == "roc_auc": + return roc_auc_score(gt, pred) + elif metric == "rmse": + return mean_squared_error(gt, pred, squared=False) + elif metric == "log rmse": + return mean_squared_error(np.log1p(gt), np.log1p(pred), squared=False) + else: + raise ValueError(f"Metric {metric} not supported") \ No newline at end of file diff --git a/expo/evaluation/visualize_mcts.py b/expo/evaluation/visualize_mcts.py new file mode 100644 index 000000000..6e38576e2 --- /dev/null +++ b/expo/evaluation/visualize_mcts.py @@ -0,0 +1,54 @@ + +from expo.MCTS import Node, MCTS +import textwrap + +NODE_TEMPLATE = """\ +[Node {id}] +Plans: +{plans} +Simulated: {simulated} +Score: {score}, Visits: {num_visits} + +""" + +def get_role_plans(role): + plans = role.planner.plan.tasks + instruct_plans = [f"{i+1}. {task.instruction}" for i, task in enumerate(plans)] + return instruct_plans + + +def get_tree_text(node : Node): + role_dict = {} + code_set = set() + def load_role(node): + if node.id not in role_dict: + role_dict[node.id] = node.load_role() + return role_dict[node.id] + + def visualize_node(node : Node, previous_plans=None): + role = load_role(node) + node_id = node.id + plans = role.planner.plan.tasks + instruct_plans = [f"{i+1}. {task.instruction}" for i, task in enumerate(plans)] + if previous_plans is not None: + instruct_plans = [plan for plan, prev_plan in zip(instruct_plans, previous_plans) if plan != prev_plan] + instruct_plans_text = "\n".join(instruct_plans) + simulated = role.state_saved + score = f"avg score: {node.avg_value()}, simulated score: {node.raw_reward}" + num_visits = node.visited + return NODE_TEMPLATE.format(id=node_id, plans=instruct_plans_text, simulated=simulated, score=score, num_visits=num_visits) + + def visualize_tree(node, depth=0, previous_plans=None): + text = "" + if node is not None: + text += visualize_node(node, previous_plans) + role = load_role(node) + code_set.update({task.instruction for task in role.planner.plan.tasks}) + previous_plans = get_role_plans(role) + for child in node.children: + text += textwrap.indent(visualize_tree(child, depth+1, previous_plans), "\t") + return text + + return visualize_tree(node), len(code_set) + + diff --git a/expo/experimenter/aug_experimenter.py b/expo/experimenter/aug_experimenter.py new file mode 100644 index 000000000..e69de29bb diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py new file mode 100644 index 000000000..d7ed82070 --- /dev/null +++ b/expo/experimenter/experimenter.py @@ -0,0 +1,18 @@ + +class Experimenter: + result_path : str = "results" + + async def run_experiment(self): + pass + + + def save_scores(self): + pass + + def save_result(self): + results = { + "test_score": self.test_score, + "num_experiments": self.num_experiments, + "insights": self.insights, + "avg_score": self.avg_score, + } \ No newline at end of file diff --git a/expo/experimenter/mcts_experimenter.py b/expo/experimenter/mcts_experimenter.py new file mode 100644 index 000000000..e69de29bb diff --git a/expo/insights/InsightGenerate.py b/expo/insights/InsightGenerate.py new file mode 100644 index 000000000..de58b7e4e --- /dev/null +++ b/expo/insights/InsightGenerate.py @@ -0,0 +1,114 @@ +REFLECTION_SYSTEM_MSG = "As a Kaggle grandmaster participating in a competition, you need to analyze your experience and propose evolutionary points that are more likely to improve the performance of baseline code." + +CHANGE_INSTRUCTION = """ +# Original instruction +{instruction} + +# Insights +{insights} + +Rewrite the original instruction according to the insights + +# Expected Output Hard Format +```json +{{ + "Original Instruction": "original instruction", + "New Instruction": "new instruction" +}} +``` +""" + +import re +import random +import json +from metagpt.llm import LLM +from metagpt.schema import Message +from examples.MCTS_test.utils import load_data_config, mcts_logger +DATA_CONFIG = load_data_config() + + +class InsightGenerator: + data_config = DATA_CONFIG + + @staticmethod + def load_json_data(json_dir): + with open(json_dir, "r") as file: + json_data = json.load(file) + return json_data + + @staticmethod + def _random_sample(analysis, num_samples): + return random.sample(analysis, num_samples) + + @staticmethod + def sample_instruction_set(data): + data_dict = {} + for item in data: + task_id = item["task_id"] + if task_id not in data_dict: + data_dict[task_id] = [] + data_dict[task_id].append(item) + instruction_set = [] + for task_id in sorted(data_dict.keys()): + instruction_set.append(random.choice(data_dict[task_id])) + return instruction_set + + + @staticmethod + def clean_json_from_rsp(text): + pattern = r"```json(.*?)```" + matches = re.findall(pattern, text, re.DOTALL) + if matches: + json_str = "\n".join(matches) + return json_str + else: + return "" + + @staticmethod + def format_output(rsp): + rsp_list = [] + new_data = [] + rsp_list.append(rsp) + for item in rsp_list: + item_dict = json.loads(item) + data = { + "Insights": item_dict, + } + new_data.append(data) + return new_data + + @staticmethod + def load_analysis_pool(file_path, task_id=None): + data = InsightGenerator.load_json_data(file_path) + for item in data: + if "task_id" not in item: + raise ValueError("task_id is not found in the analysis pool") + + if task_id: + data = [item for item in data if int(item["task_id"]) == int(task_id)] + return data + + @staticmethod + async def generate_new_instructions(task_id, original_instruction, max_num, file_path): + data = InsightGenerator.load_analysis_pool(file_path, task_id) + new_instructions = [] + if len(data) == 0: + mcts_logger.log("MCTS", f"No insights available for task {task_id}") + return [original_instruction] # Return the original instruction if no insights are available + for item in data[:max_num]: + insights = item["Analysis"] + new_instruction = await InsightGenerator.generate_new_instruction(original_instruction, insights) + new_instructions.append(new_instruction) + return new_instructions + + @staticmethod + async def generate_new_instruction(original_instruction, insights): + prompt = CHANGE_INSTRUCTION.format(instruction=original_instruction, insights=insights) + llm = LLM() + context = llm.format_msg([Message(content=prompt, role="user")]) + llm_response = await llm.aask( + context, system_msgs=[REFLECTION_SYSTEM_MSG] + ) + rsp = InsightGenerator.clean_json_from_rsp(llm_response) + new_instruction = json.loads(rsp)["New Instruction"] + return new_instruction \ No newline at end of file diff --git a/expo/insights/solution_designer.py b/expo/insights/solution_designer.py new file mode 100644 index 000000000..0986c392a --- /dev/null +++ b/expo/insights/solution_designer.py @@ -0,0 +1,127 @@ +import re +import random +import json +from metagpt.llm import LLM +from metagpt.schema import Message +from examples.MCTS_test.utils import clean_json_from_rsp, load_data_config + + +DATA_CONFIG = load_data_config() + +DATASET_INSIGHT_PROMPT = """ +# Dataset Description +{dataset} + +# Dataset Metadata +{metadata} + +# Dataset Head +{head} + +# Instruction +Propose insights to help improve the performance of the model on this dataset. +The insights should be proposed based on the dataset description with different task types. +Each task type should have at least 5 insights. +Make sure each method is independent and can be implemented separately. + +# Format +```json +[ + {{ + "task_type": "EDA", + "insights": [ + "insight1", + "insight2", + "insight3", + ... + "insightN" + ] + }}, + {{ + "task_type": "Data Preprocessing", + "insights": [ + "insight1", + "insight2", + "insight3", + ... + "insightN" + ] + }}, + {{ + "task_type": "Feature Engineering", + "insights": [ + "insight1", + "insight2", + "insight3", + ... + "insightN" + ] + }}, + {{ + "task_type": "Model Training", + "insights": [ + "insight1", + "insight2", + "insight3", + ... + "insightN" + ] + }} +] +``` +""" + +KEY_DATASET_FEATURES = [ + 'NumberOfClasses', + 'NumberOfFeatures', + 'NumberOfInstances', + 'NumberOfInstancesWithMissingValues', + 'NumberOfMissingValues', + 'NumberOfNumericFeatures', + 'NumberOfSymbolicFeatures' +] + +TASK_TO_ID = { + "EDA": 1, + "Data Preprocessing": 2, + "Feature Engineering": 3, + "Model Training": 4, + "Model Evaluation": 5 +} + +class SolutionDesigner: + data_dir : str= DATA_CONFIG["datasets_dir"] + + async def generate_solutions(self, dataset_info, dataset_name): + llm = LLM() + context = DATASET_INSIGHT_PROMPT.format(dataset=dataset_info["description"], + metadata=self.metadata_builder(dataset_info["metadata"]), + head=dataset_info["df_head"]) + rsp = await llm.aask(context) + rsp = clean_json_from_rsp(rsp) + analysis_pool = self.process_analysis_pool(json.loads(rsp)) + dataset_path = f"{self.data_dir}/{dataset_name}" + self.save_analysis_pool(dataset_path, analysis_pool) + + + def process_analysis_pool(self, insights_rsp): + analysis_pool = [] + for task_type_insights in insights_rsp: + task_type = task_type_insights["task_type"] + for insight in task_type_insights["insights"]: + analysis_pool.append({"Analysis": insight, "Category": task_type, "task_id": TASK_TO_ID[task_type]}) + return analysis_pool + + + def metadata_builder(self, qualities): + metadata = {} + for key in KEY_DATASET_FEATURES: + metadata[key] = qualities.get(key, "N/A") + metadata_text = json.dumps(metadata, indent=4) + return metadata_text + + def save_analysis_pool(self, dataset_path, analysis_pool): + fpath = f"{dataset_path}/ds_analysis_pool.json" + with open(fpath, "w") as file: + json.dump(analysis_pool, file, indent=4) + \ No newline at end of file diff --git a/expo/research_assistant.py b/expo/research_assistant.py new file mode 100644 index 000000000..fbd74f7db --- /dev/null +++ b/expo/research_assistant.py @@ -0,0 +1,141 @@ +from __future__ import annotations + +import json +from metagpt.roles.di.data_interpreter import DataInterpreter +from metagpt.schema import Message, Task, TaskResult +from metagpt.strategy.task_type import TaskType +from metagpt.tools.tool_recommend import BM25ToolRecommender, ToolRecommender +from metagpt.utils.common import CodeParser +from metagpt.utils.common import write_json_file, read_json_file, format_trackback_info +from metagpt.const import MESSAGE_ROUTE_TO_ALL, SERDESER_PATH +from metagpt.utils.recovery_util import save_history +from expo.utils import mcts_logger, save_notebook +import re +import os + +EXTRACT_SCORE_PROMPT = """ +# Code: +{code} + +# Execution Result: +{result} + +# Instruction: +Based on the code and execution result, please extract the scores and return it as a dictionary. +If you cannot find the scores, please still return a dictionary with the keys 'train_score', 'dev_score', and 'test_score', and set the values to -1. + +# Format: +```json +{{ + "train_score": x.x, + "dev_score": x.x, + "test_score": x.x, +}} +``` +""" + +class ResearchAssistant(DataInterpreter): + node_id: str = "0" + start_task_id: int = 1 + state_saved : bool = False + role_dir : str = SERDESER_PATH.joinpath("team", "environment", "roles", f"Experimenter") + + def get_node_name(self): + return f"Node-{self.node_id}" + + def get_next_instruction(self): + return self.planner.plan.tasks[self.start_task_id] + + def change_next_instruction(self, new_instruction): + if new_instruction is not None: + self.planner.plan.task_map[str(self.start_task_id)].instruction = new_instruction + self.remap_tasks() + + + def update_til_start_task(self, role: ResearchAssistant, backward: bool = True): + if backward: + # make sure the previous task instructions are matched + assert self.start_task_id == role.start_task_id - 1, f"start_task_id: {self.start_task_id}, role.start_task_id: {role.start_task_id}" + for i in range(self.start_task_id): + if self.planner.plan.task_map[str(self.start_task_id)].instruction != role.planner.plan.task_map[str(self.start_task_id)].instruction: + mcts_logger.info("Previous task instructions not matched") + self.remap_tasks() + return + # copy new role's task (self.start_task_id) to current role + self.planner.plan.task_map[str(self.start_task_id)] = role.planner.plan.task_map[str(self.start_task_id)].model_copy() + self.remap_tasks() + + else: + assert self.start_task_id == role.start_task_id + 1, f"start_task_id: {self.start_task_id}, role.start_task_id: {role.start_task_id}" + if int(role.planner.plan.current_task_id) > self.start_task_id: + for i in range(role.start_task_id): + self.planner.plan.task_map[str(i)] = role.planner.plan.task_map[str(i)].model_copy() + self.remap_tasks() + + async def get_score(self): + score_dict = await self.llm_extract_score() + score_dict["score"] = score_dict["dev_score"] + return score_dict + + async def llm_extract_score(self): + result_text = self.planner.plan.task_map[str(len(self.planner.plan.task_map))].result + code_text = self.planner.plan.task_map[str(len(self.planner.plan.task_map))].code + rsp = await self.llm.aask(EXTRACT_SCORE_PROMPT.format(code=code_text, result=result_text, role="user")) + json_block = CodeParser.parse_code(block=None, text=rsp) + score_dict = json.loads(json_block) + return score_dict + + async def _act_on_task(self, current_task: Task) -> TaskResult: + """Useful in 'plan_and_act' mode. Wrap the output in a TaskResult for review and confirmation.""" + mcts_logger.info(f"The current_task is: {current_task}") + + # 执行任务的代码 + code, result, is_success = await self._write_and_exec_code() + task_result = TaskResult(code=code, result=result, is_success=is_success) + # 只在任务类型为 'feature engineering' 时保存状态 + if int(current_task.task_id) == self.start_task_id + 1: + # fe_id = current_task.dependent_task_ids + self.save_state() + save_notebook(role=self, save_dir=self.role_dir, name=self.get_node_name()) + return task_result + + def save_state(self, static_save=False): + if self.state_saved and not static_save: + return + if not static_save: + self.state_saved = True + mcts_logger.log("MCTS", f"Saving state at task {self.start_task_id}") + else: + mcts_logger.log("MCTS", f"Static Saving") + stg_path = self.role_dir + name = self.get_node_name() + role_path = os.path.join(stg_path, f"{name}.json") + # 将状态保存为 JSON 文件 + write_json_file(role_path, self.model_dump()) + save_history(role=self, save_dir=stg_path, name=name) + + + def remap_tasks(self): + self.planner.plan.tasks = [self.planner.plan.task_map[task_id] for task_id in sorted(self.planner.plan.task_map.keys())] + + + async def run(self, with_message=None) -> Message | None: + """Observe, and think and act based on the results of the observation""" + if with_message == "continue": + # self.set_todo(None) + # working_memory = self.working_memory + # self.remap_tasks() + mcts_logger.info("Continue to run") + self.rc.working_memory.clear() + self.working_memory.clear() + # self.rc.todo = WriteAnalysisCode() + rsp = await self.react() + # 发送响应消息给 Environment 对象，以便它将消息传递给订阅者 + self.set_todo(None) + self.publish_message(rsp) + return rsp + return await super().run(with_message) + + + + \ No newline at end of file diff --git a/expo/results/PLACEHOLDER b/expo/results/PLACEHOLDER new file mode 100644 index 000000000..e69de29bb diff --git a/expo/results/tree/TREE b/expo/results/tree/TREE new file mode 100644 index 000000000..e69de29bb diff --git a/expo/run_exp_augmentation.py b/expo/run_exp_augmentation.py new file mode 100644 index 000000000..492a424d4 --- /dev/null +++ b/expo/run_exp_augmentation.py @@ -0,0 +1,96 @@ +import os +from metagpt.roles.di.research_assistant import ResearchAssistant +import asyncio +from examples.MCTS_test.utils import DATA_CONFIG, generate_task_requirement, get_exp_pool_path +from examples.MCTS_test.insights.InsightGenerate import InsightGenerator +from examples.MCTS_test.MCTS import create_initial_state +from examples.MCTS_test.evaluation.evaluation import evaluate_score +import json +import argparse +import pandas as pd +import datetime + +EXPS_PROMPT = """ +When doing the tasks, you can refer to the insights below: +{experience} + +""" +data_config = DATA_CONFIG + +def evaluate_test(score, state): + datetime_text = datetime.datetime.now().strftime("%Y%m%d%H%M") + task_name = state["task"] + prediction_fpath = os.path.join(state["work_dir"], task_name, "predictions.csv") + predictions = pd.read_csv(prediction_fpath)["target"] + # copy predictions.csv to the node_dir + + predictions_node_fpath = os.path.join("results", f"{task_name}-{datetime_text}-predictions.csv") + predictions.to_csv(predictions_node_fpath, index=False) + # load test_target.csv + split_datasets_dir = state["datasets_dir"] + gt = pd.read_csv(os.path.join(split_datasets_dir["test_target"]))["target"] + metric = state["dataset_config"]["metric"] + score["test_score"] = evaluate_score(predictions, gt, metric) + return score + + + + +async def main(task_name, use_reflection=True, mode="single", num_experiments=2): + """ + mode: single or set + single: sample one instruction + set: sample a set of instructions + """ + low_is_better = False + state = create_initial_state(task_name, start_task_id=1, data_config=data_config, low_is_better=low_is_better, name="") + + user_requirement = generate_task_requirement(task_name, data_config) + exp_pool_path = get_exp_pool_path(task_name, data_config, pool_name="ds_analysis_pool") + exp_pool = InsightGenerator.load_analysis_pool(exp_pool_path) + if mode == "single": + exps = InsightGenerator._random_sample(exp_pool, num_experiments) + exps = [exp["Analysis"] for exp in exps] + elif mode == "set": + exp_set = InsightGenerator.sample_instruction_set(exp_pool) + exp_set_text = "\n".join([f"{exp['task_id']}: {exp['Analysis']}" for exp in exp_set]) + exps = [exp_set_text] * num_experiments + else: + raise ValueError(f"Invalid mode: {mode}") + + scores = [] + for i in range(num_experiments): + di = ResearchAssistant(node_id=str(i), use_reflection=use_reflection) + di.role_dir = f"{di.role_dir}_{task_name}" + requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) + print(requirement) + await di.run(requirement) + score = await di.get_score(low_is_better=False) + score = evaluate_test(score, state) + + scores.append(score) + + + with open(f"results/{task_name}_scores.json", "w") as f: + # save scores and corresponding insights + results = {"avg_score": sum([score["test_score"] for score in scores if score])/num_experiments, + "max_score": max([score["test_score"] for score in scores]), + "scores": scores, "insights": exps} + json.dump(results, f, indent=4) + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--task", type=str, default="titanic") + parser.add_argument("--use_reflection", dest="use_reflection", action="store_true") + parser.add_argument("--no_use_reflection", dest="use_reflection", action="store_false") + parser.set_defaults(use_reflection=True) + parser.add_argument("--mode", type=str, default="single") + parser.add_argument("--num_experiments", type=int, default=2) + return parser.parse_args() + + + +if __name__ == "__main__": + args = parse_args() + asyncio.run(main(args.task, use_reflection=args.use_reflection, mode=args.mode, num_experiments=args.num_experiments)) diff --git a/expo/run_experiment.py b/expo/run_experiment.py new file mode 100644 index 000000000..e75897f5a --- /dev/null +++ b/expo/run_experiment.py @@ -0,0 +1,44 @@ +from examples.MCTS_test.MCTS import MCTS, Node, initialize_di_root_node +from examples.MCTS_test.utils import load_data_config, generate_task_requirement +from examples.MCTS_test.visualize_mcts import get_tree_text +import asyncio +import argparse + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--name", type=str, default="") + get_di_args(parser) + get_mcts_args(parser) + get_aug_exp_args(parser) + + + return parser.parse_args() + + +def get_mcts_args(parser): + parser.add_argument("--load_tree", dest="load_tree", action="store_true") + parser.add_argument("--no_load_tree", dest="load_tree", action="store_false") + parser.set_defaults(load_tree=True) + parser.add_argument("--rollout", type=int, default=3) + +def get_aug_exp_args(parser): + parser.add_argument("--aug_mode", type=str, default="single", choices=["single", "set"]) + parser.add_argument("--num_experiments", type=int, default=2) + + +def get_di_args(parser): + parser.add_argument("--task", type=str, default="titanic") + parser.add_argument("--low_is_better", dest="low_is_better", action="store_true") + parser.set_defaults(low_is_better=False) + parser.add_argument("--reflection", dest="reflection", action="store_true") + parser.add_argument("--no_reflection", dest="reflection", action="store_false") + parser.set_defaults(reflection=True) + + +async def main(args): + pass + +if __name__ == "__main__": + args = get_args() + asyncio.run(main(args)) \ No newline at end of file diff --git a/expo/run_mcts.py b/expo/run_mcts.py new file mode 100644 index 000000000..0c0c486db --- /dev/null +++ b/expo/run_mcts.py @@ -0,0 +1,48 @@ +from expo.MCTS import MCTS, Node, initialize_di_root_node +from expo.utils import load_data_config, generate_task_requirement +from expo.evaluation.visualize_mcts import get_tree_text +import asyncio +import argparse + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--task", type=str, default="titanic") + parser.add_argument("--low_is_better", dest="low_is_better", action="store_true") + parser.set_defaults(low_is_better=False) + parser.add_argument("--load_tree", dest="load_tree", action="store_true") + parser.add_argument("--no_load_tree", dest="load_tree", action="store_false") + parser.set_defaults(load_tree=True) + parser.add_argument("--reflection", dest="reflection", action="store_true") + parser.add_argument("--no_reflection", dest="reflection", action="store_false") + parser.set_defaults(reflection=True) + parser.add_argument("--rollout", type=int, default=3) + parser.add_argument("--name", type=str, default="") + return parser.parse_args() + + +data_config = load_data_config() + +if __name__ == "__main__": + args = get_args() + requirement = generate_task_requirement(args.task, data_config) + print(requirement) + + # role, root_node = initialize_di_root_node(requirement, data_config) + # asyncio.run(role.run(requirement)) + + # asyncio.run(root_node.run_node()) + mcts = MCTS(root_node=None, max_depth=5) + best_node = asyncio.run(mcts.search(args.task, data_config, + low_is_better=args.low_is_better, load_tree=args.load_tree, + reflection=args.reflection, rollout=args.rollout, name=args.name)) + text, num_generated_codes = get_tree_text(mcts.root_node) + print(text) + print(f"Generated {num_generated_codes} unique codes.") + + with open(f"results/{args.task}_tree{args.name}.txt", "w") as f: + f.write(f"Generated {num_generated_codes} unique codes.\n") + f.write(f"Best node: {best_node}, score: {best_node.raw_reward}\n") + f.write(text) + + diff --git a/expo/utils.py b/expo/utils.py new file mode 100644 index 000000000..ac4a64697 --- /dev/null +++ b/expo/utils.py @@ -0,0 +1,150 @@ +import yaml +from examples.MCTS_test.dataset import get_user_requirement, get_split_dataset_path +from metagpt.roles.role import Role +from metagpt.actions.di.execute_nb_code import ExecuteNbCode +from metagpt.utils.save_code import save_code_file +# from nbclient import NotebookClient +from nbformat.notebooknode import NotebookNode +import nbformat +from pathlib import Path +from loguru import logger as _logger +from datetime import datetime +import sys +import os +import re + +TASK_PROMPT = """\ +# User requirement +{user_requirement} +**Attention** Please do not leak the target label in any form during training. + +## Saving Dev and Test Predictions +Save the prediction results of the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory BEFORE printig out the results. +The file should contain a single `target` column with the predicted values. +Make sure the prediction results are in the same format as the target column in the training set. The labels should be transformed back to the original format if any transformation was applied during training. + +## Output Training Set Performance +Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. +Print the training set performance in the last step. Write in this format: +```python +... +print("Train score:", train_score) +``` + +# Data dir +training: {train_path} +dev: {dev_path} +testing: {test_path} + +# Output dir +{output_dir} + +""" + +def load_data_config(file_path="data.yaml"): + with open(file_path, 'r') as stream: + data_config = yaml.safe_load(stream) + return data_config + +DATA_CONFIG = load_data_config() + +def get_mcts_logger(): + print_level = "INFO" + print_level2 = "MCTS" + logfile_level="MCTS" + name: str = None + current_date = datetime.now() + formatted_date = current_date.strftime("%Y%m%d") + log_name = f"{name}_{formatted_date}" if name else formatted_date # name a log with prefix name + + _logger.remove() + new_level = _logger.level(logfile_level, color="", no=25) + _logger.add(sys.stderr, level=print_level) + _logger.add(sys.stderr, level=print_level2) + _logger.add(Path(DATA_CONFIG["work_dir"]) / DATA_CONFIG["role_dir"] / f"{log_name}.txt", level=logfile_level) + _logger.propagate = False + return _logger + +mcts_logger = get_mcts_logger() + + +def get_exp_pool_path(task_name, data_config, pool_name="analysis_pool"): + datasets_dir = data_config['datasets_dir'] + if task_name in data_config['datasets']: + dataset = data_config['datasets'][task_name] + data_path = os.path.join(datasets_dir, dataset['dataset']) + else: + raise ValueError(f"Dataset {task_name} not found in config file. Available datasets: {data_config['datasets'].keys()}") + exp_pool_path = os.path.join(data_path, f"{pool_name}.json") + return exp_pool_path + +def generate_task_requirement(task_name, data_config): + user_requirement = get_user_requirement(task_name, data_config) + split_dataset_path = get_split_dataset_path(task_name, data_config) + train_path = split_dataset_path["train"] + dev_path = split_dataset_path["dev_wo_target"] + test_path = split_dataset_path["test_wo_target"] + work_dir = data_config["work_dir"] + output_dir = f"{work_dir}/{task_name}" + user_requirement = TASK_PROMPT.format(user_requirement=user_requirement, + train_path=train_path, dev_path=dev_path, test_path=test_path, + output_dir=output_dir) + return user_requirement + +def change_plan(role, plan): + print(f"Change next plan to: {plan}") + tasks = role.planner.plan.tasks + finished = True + for i, task in enumerate(tasks): + if not task.code: + finished = False + break + if not finished: + tasks[i].plan = plan + return finished + + + +def is_cell_to_delete(cell: NotebookNode) -> bool: + if "outputs" in cell: + for output in cell["outputs"]: + if output and "traceback" in output: + return True + return False + + +def process_cells(nb: NotebookNode) -> NotebookNode: + new_cells = [] + i = 1 + for cell in nb["cells"]: + if cell["cell_type"] == "code" and not is_cell_to_delete(cell): + cell["execution_count"] = i + new_cells.append(cell) + i = i + 1 + nb["cells"] = new_cells + return nb + +def save_notebook(role: Role, save_dir: str = "", name: str = ""): + save_dir = Path(save_dir) + nb = process_cells(role.execute_code.nb) + save_code_file(name=name, code_context=nb, file_format="ipynb", save_dir=save_dir) + +async def load_execute_notebook(role): + tasks = role.planner.plan.tasks + codes = [task.code for task in tasks if task.code] + executor = role.execute_code + # await executor.build() + for code in codes: + outputs, success = await executor.run(code) + print(f"Execution success: {success}, Output: {outputs}") + print("Finish executing the loaded notebook") + return executor + +def clean_json_from_rsp(text): + pattern = r"```json(.*?)```" + matches = re.findall(pattern, text, re.DOTALL) + if matches: + json_str = "\n".join(matches) + return json_str + else: + return "" \ No newline at end of file From fab1293cab8fda88c623441fd2158202b7d8d6d1 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 30 Aug 2024 16:26:56 +0800 Subject: [PATCH 002/135] ignore expo/results --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index aa5edd74a..3fc66cecb 100644 --- a/.gitignore +++ b/.gitignore @@ -188,3 +188,4 @@ cov.xml *-structure.json *.dot .python-version +expo/results/* From 211f758b5311278607b020f1dfb335bf054456eb Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 30 Aug 2024 16:58:31 +0800 Subject: [PATCH 003/135] add openml to requirement --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 8bf0ee399..7ea849f5a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -79,3 +79,4 @@ gymnasium==0.29.1 boto3~=1.34.69 spark_ai_python~=0.3.30 agentops +openml==0.14.2 From d14f07f9b193030cbfb90c35c006527e5d951234 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 30 Aug 2024 16:59:38 +0800 Subject: [PATCH 004/135] 1. change data.yaml to more generalized path 2. correct import --- expo/MCTS.py | 4 +-- expo/data.yaml | 2 +- expo/dataset.py | 45 +++++++++++++++++++++++++++++- expo/insights/InsightGenerate.py | 2 +- expo/insights/solution_designer.py | 2 +- expo/research_assistant.py | 14 ++++++++++ expo/run_exp_augmentation.py | 11 ++++---- expo/run_experiment.py | 7 +++-- expo/run_mcts.py | 4 ++- expo/utils.py | 41 --------------------------- 10 files changed, 76 insertions(+), 56 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 9026e09b4..af50ff7a0 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -4,9 +4,9 @@ import pandas as pd from expo.research_assistant import ResearchAssistant from expo.insights.InsightGenerate import InsightGenerator -from expo.dataset import get_split_dataset_path +from expo.dataset import get_split_dataset_path, generate_task_requirement from expo.evaluation.evaluation import evaluate_score -from expo.utils import mcts_logger, load_execute_notebook, generate_task_requirement, get_exp_pool_path +from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path from metagpt.tools.tool_recommend import BM25ToolRecommender, ToolRecommender from metagpt.utils.common import write_json_file, read_json_file, format_trackback_info diff --git a/expo/data.yaml b/expo/data.yaml index d921e1ebf..df26e29e8 100644 --- a/expo/data.yaml +++ b/expo/data.yaml @@ -152,6 +152,6 @@ datasets: \ eval data. Do not plot or make any visualizations.\n" -work_dir: D:/work/MG-open/MetaGPT/workspace # path to the workspace directory +work_dir: ../workspace # path to the workspace directory role_dir: storage/team/environment/roles/ResearchAssistant_David # analysis_pool_dir: D:/work/MG-open/MetaGPT/examples/MCTS_test/analysis_pool_sample.json \ No newline at end of file diff --git a/expo/dataset.py b/expo/dataset.py index 4bce6e9fe..a507d0b7e 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -5,7 +5,7 @@ import json import yaml import pandas as pd -from examples.MCTS_test.insights.solution_designer import SolutionDesigner +from expo.insights.solution_designer import SolutionDesigner import asyncio BASE_USER_REQUIREMENT = """\ @@ -14,6 +14,35 @@ Report {metric} on the eval data. Do not plot or make any visualizations. """ +TASK_PROMPT = """\ +# User requirement +{user_requirement} +**Attention** Please do not leak the target label in any form during training. + +## Saving Dev and Test Predictions +Save the prediction results of the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory BEFORE printig out the results. +The file should contain a single `target` column with the predicted values. +Make sure the prediction results are in the same format as the target column in the training set. The labels should be transformed back to the original format if any transformation was applied during training. + +## Output Training Set Performance +Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. +Print the training set performance in the last step. Write in this format: +```python +... +print("Train score:", train_score) +``` + +# Data dir +training: {train_path} +dev: {dev_path} +testing: {test_path} + +# Output dir +{output_dir} + +""" + + SEED = 100 TRAIN_TEST_SPLIT = 0.8 TRAIN_DEV_SPLIT = 0.75 @@ -89,6 +118,20 @@ def create_dataset_dict(dataset): } return dataset_dict +def generate_task_requirement(task_name, data_config): + user_requirement = get_user_requirement(task_name, data_config) + split_dataset_path = get_split_dataset_path(task_name, data_config) + train_path = split_dataset_path["train"] + dev_path = split_dataset_path["dev_wo_target"] + test_path = split_dataset_path["test_wo_target"] + work_dir = data_config["work_dir"] + output_dir = f"{work_dir}/{task_name}" + user_requirement = TASK_PROMPT.format(user_requirement=user_requirement, + train_path=train_path, dev_path=dev_path, test_path=test_path, + output_dir=output_dir) + return user_requirement + + class ExpDataset: description : str = None metadata : dict = None diff --git a/expo/insights/InsightGenerate.py b/expo/insights/InsightGenerate.py index de58b7e4e..55ab64e30 100644 --- a/expo/insights/InsightGenerate.py +++ b/expo/insights/InsightGenerate.py @@ -23,7 +23,7 @@ import json from metagpt.llm import LLM from metagpt.schema import Message -from examples.MCTS_test.utils import load_data_config, mcts_logger +from expo.utils import load_data_config, mcts_logger DATA_CONFIG = load_data_config() diff --git a/expo/insights/solution_designer.py b/expo/insights/solution_designer.py index 0986c392a..e2bf57ae3 100644 --- a/expo/insights/solution_designer.py +++ b/expo/insights/solution_designer.py @@ -3,7 +3,7 @@ import json from metagpt.llm import LLM from metagpt.schema import Message -from examples.MCTS_test.utils import clean_json_from_rsp, load_data_config +from expo.utils import clean_json_from_rsp, load_data_config DATA_CONFIG = load_data_config() diff --git a/expo/research_assistant.py b/expo/research_assistant.py index fbd74f7db..7b844cf5e 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -10,6 +10,9 @@ from metagpt.const import MESSAGE_ROUTE_TO_ALL, SERDESER_PATH from metagpt.utils.recovery_util import save_history from expo.utils import mcts_logger, save_notebook +from pydantic import Field, model_validator +from metagpt.actions.di.write_analysis_code import CheckData, WriteAnalysisCode + import re import os @@ -84,6 +87,17 @@ async def llm_extract_score(self): json_block = CodeParser.parse_code(block=None, text=rsp) score_dict = json.loads(json_block) return score_dict + + + @model_validator(mode="after") + def set_plan_and_tool(self) -> "Interpreter": + if self.planner.plan.goal != '': + self.set_actions([WriteAnalysisCode]) + self._set_state(0) + print("Plan already exists, skipping initialization.") + return self + print("Initializing plan and tool...") + return super().set_plan_and_tool() async def _act_on_task(self, current_task: Task) -> TaskResult: """Useful in 'plan_and_act' mode. Wrap the output in a TaskResult for review and confirmation.""" diff --git a/expo/run_exp_augmentation.py b/expo/run_exp_augmentation.py index 492a424d4..f4d22093f 100644 --- a/expo/run_exp_augmentation.py +++ b/expo/run_exp_augmentation.py @@ -1,10 +1,11 @@ import os -from metagpt.roles.di.research_assistant import ResearchAssistant +from expo.research_assistant import ResearchAssistant import asyncio -from examples.MCTS_test.utils import DATA_CONFIG, generate_task_requirement, get_exp_pool_path -from examples.MCTS_test.insights.InsightGenerate import InsightGenerator -from examples.MCTS_test.MCTS import create_initial_state -from examples.MCTS_test.evaluation.evaluation import evaluate_score +from expo.utils import DATA_CONFIG, get_exp_pool_path +from expo.dataset import generate_task_requirement +from expo.insights.InsightGenerate import InsightGenerator +from expo.MCTS import create_initial_state +from expo.evaluation.evaluation import evaluate_score import json import argparse import pandas as pd diff --git a/expo/run_experiment.py b/expo/run_experiment.py index e75897f5a..0c7468ac9 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -1,6 +1,7 @@ -from examples.MCTS_test.MCTS import MCTS, Node, initialize_di_root_node -from examples.MCTS_test.utils import load_data_config, generate_task_requirement -from examples.MCTS_test.visualize_mcts import get_tree_text +from expo.MCTS import MCTS, Node, initialize_di_root_node +from expo.utils import load_data_config +from expo.dataset import generate_task_requirement +from expo.evaluation.visualize_mcts import get_tree_text import asyncio import argparse diff --git a/expo/run_mcts.py b/expo/run_mcts.py index 0c0c486db..6d2c421ec 100644 --- a/expo/run_mcts.py +++ b/expo/run_mcts.py @@ -1,5 +1,7 @@ from expo.MCTS import MCTS, Node, initialize_di_root_node -from expo.utils import load_data_config, generate_task_requirement +from expo.utils import load_data_config +from expo.dataset import generate_task_requirement + from expo.evaluation.visualize_mcts import get_tree_text import asyncio import argparse diff --git a/expo/utils.py b/expo/utils.py index ac4a64697..423889f29 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -1,5 +1,4 @@ import yaml -from examples.MCTS_test.dataset import get_user_requirement, get_split_dataset_path from metagpt.roles.role import Role from metagpt.actions.di.execute_nb_code import ExecuteNbCode from metagpt.utils.save_code import save_code_file @@ -13,34 +12,6 @@ import os import re -TASK_PROMPT = """\ -# User requirement -{user_requirement} -**Attention** Please do not leak the target label in any form during training. - -## Saving Dev and Test Predictions -Save the prediction results of the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory BEFORE printig out the results. -The file should contain a single `target` column with the predicted values. -Make sure the prediction results are in the same format as the target column in the training set. The labels should be transformed back to the original format if any transformation was applied during training. - -## Output Training Set Performance -Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. -Print the training set performance in the last step. Write in this format: -```python -... -print("Train score:", train_score) -``` - -# Data dir -training: {train_path} -dev: {dev_path} -testing: {test_path} - -# Output dir -{output_dir} - -""" - def load_data_config(file_path="data.yaml"): with open(file_path, 'r') as stream: data_config = yaml.safe_load(stream) @@ -78,18 +49,6 @@ def get_exp_pool_path(task_name, data_config, pool_name="analysis_pool"): exp_pool_path = os.path.join(data_path, f"{pool_name}.json") return exp_pool_path -def generate_task_requirement(task_name, data_config): - user_requirement = get_user_requirement(task_name, data_config) - split_dataset_path = get_split_dataset_path(task_name, data_config) - train_path = split_dataset_path["train"] - dev_path = split_dataset_path["dev_wo_target"] - test_path = split_dataset_path["test_wo_target"] - work_dir = data_config["work_dir"] - output_dir = f"{work_dir}/{task_name}" - user_requirement = TASK_PROMPT.format(user_requirement=user_requirement, - train_path=train_path, dev_path=dev_path, test_path=test_path, - output_dir=output_dir) - return user_requirement def change_plan(role, plan): print(f"Change next plan to: {plan}") From 32759f031c4c2e489e746c7c16d2e5017cdbebcc Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 30 Aug 2024 19:55:40 +0800 Subject: [PATCH 005/135] add experimenter --- expo/MCTS.py | 24 +++++---- expo/dataset.py | 5 +- expo/experimenter/__init__.py | 3 ++ expo/experimenter/aug.py | 60 +++++++++++++++++++++++ expo/experimenter/aug_experimenter.py | 0 expo/experimenter/experimenter.py | 59 ++++++++++++++++++---- expo/experimenter/mcts.py | 44 +++++++++++++++++ expo/experimenter/mcts_experimenter.py | 0 expo/insights/InsightGenerate.py | 15 +----- expo/research_assistant.py | 2 - expo/run_experiment.py | 18 ++++--- expo/run_mcts.py | 9 ++-- expo/utils.py | 4 +- metagpt/prompts/di/write_analysis_code.py | 2 +- 14 files changed, 195 insertions(+), 50 deletions(-) create mode 100644 expo/experimenter/__init__.py create mode 100644 expo/experimenter/aug.py delete mode 100644 expo/experimenter/aug_experimenter.py create mode 100644 expo/experimenter/mcts.py delete mode 100644 expo/experimenter/mcts_experimenter.py diff --git a/expo/MCTS.py b/expo/MCTS.py index af50ff7a0..5c502f917 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -169,8 +169,8 @@ async def expand(self, max_children): # return evaluate_score(predictions, gt, metric) def evaluate_prediction(self, split): - pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}-predictions.csv") - pred_node_path = os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}-predictions.csv") + pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}_predictions.csv") + pred_node_path = os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}_predictions.csv") gt_path = os.path.join(self.state["datasets_dir"][f"{split}_target"]) preds = pd.read_csv(pred_path)["target"] preds.to_csv(pred_node_path, index=False) @@ -201,12 +201,12 @@ async def run_node(self, role=None): score_dict = await role.get_score() score_dict = self.evaluate_simulation(score_dict) self.raw_reward = score_dict - if self.state["low_is_better"]: # normalized the score to be between 0 and 1, and higher is better def normalize_score(score): return 1 / (1 + score) score_dict = {k: normalize_score(v) for k, v in score_dict.items()} + self.normalized_reward = score_dict return score_dict @@ -262,19 +262,23 @@ def backpropagate(self, node : Node, reward): def best_path(self, root : Node): best_child = root best_score = 0 - def bfs(node : Node, best_score, best_child : Node): + def bfs(node : Node, best_score, best_child : Node, split): + assert split in ["test_score", "dev_score"] if node not in self.children: return best_score, best_child for child in self.children[node]: - print(child.id, child.raw_value) - if child.raw_value > best_score: - best_score = child.raw_value + score = child.normalized[split] + print(child.id, score) + if score > best_score: + best_score = score best_child = child best_score, best_child = bfs(child, best_score, best_child) return best_score, best_child - best_score, best_child = bfs(root, best_score, best_child) - mcts_logger.log("MCTS", f"Best Score: {best_score}, Best Node ID: {best_child.id}") - return best_child + _, best_child = bfs(root, best_score, best_child, "test_score") + _, dev_best_child = bfs(root, best_score, best_child, "dev_score") + + return {"dev_best": dev_best_child, + "global_best": best_child} def get_num_simulations(self): return self.root_node.visited diff --git a/expo/dataset.py b/expo/dataset.py index a507d0b7e..3f3fa1db1 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -20,8 +20,8 @@ **Attention** Please do not leak the target label in any form during training. ## Saving Dev and Test Predictions -Save the prediction results of the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory BEFORE printig out the results. -The file should contain a single `target` column with the predicted values. +Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. +Both files should contain a single `target` column with the predicted values. Make sure the prediction results are in the same format as the target column in the training set. The labels should be transformed back to the original format if any transformation was applied during training. ## Output Training Set Performance @@ -129,6 +129,7 @@ def generate_task_requirement(task_name, data_config): user_requirement = TASK_PROMPT.format(user_requirement=user_requirement, train_path=train_path, dev_path=dev_path, test_path=test_path, output_dir=output_dir) + print(user_requirement) return user_requirement diff --git a/expo/experimenter/__init__.py b/expo/experimenter/__init__.py new file mode 100644 index 000000000..d6b50c9d2 --- /dev/null +++ b/expo/experimenter/__init__.py @@ -0,0 +1,3 @@ +from .experimenter import Experimenter +from .mcts import MCTSExperimenter +from .aug import AugExperimenter \ No newline at end of file diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py new file mode 100644 index 000000000..28643d47f --- /dev/null +++ b/expo/experimenter/aug.py @@ -0,0 +1,60 @@ +from experimenter import Experimenter +from expo.MCTS import create_initial_state +from expo.dataset import generate_task_requirement +from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path +from expo.insights.InsightGenerate import InsightGenerator +from expo.research_assistant import ResearchAssistant + +EXPS_PROMPT = """ +When doing the tasks, you can refer to the insights below: +{experience} + +""" + + + + +class AugExperimenter(Experimenter): + result_path : str = "results/aug" + + async def run_experiment(self): + state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") + user_requirement = state["requirement"] + exp_pool_path = get_exp_pool_path(self.args.task, self.data_config, pool_name="ds_analysis_pool") + exp_pool = InsightGenerator.load_analysis_pool(exp_pool_path) + if self.args.aug_mode == "single": + exps = InsightGenerator._random_sample(exp_pool, self.args.num_experiments) + exps = [exp["Analysis"] for exp in exps] + elif self.args.aug_mode == "set": + exp_set = InsightGenerator.sample_instruction_set(exp_pool) + exp_set_text = "\n".join([f"{exp['task_id']}: {exp['Analysis']}" for exp in exp_set]) + exps = [exp_set_text] * self.args.num_experiments + else: + raise ValueError(f"Invalid mode: {self.args.aug_mode}") + + results = [] + for i in range(self.args.num_experiments): + di = ResearchAssistant(node_id=str(i), use_reflection=self.args.use_reflection) + di.role_dir = f"{di.role_dir}_{self.args.task}" + requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) + print(requirement) + await di.run(requirement) + score_dict = await di.get_score(low_is_better=False) + score_dict = self.evaluate(score_dict, state) + results.append({ + "idx": i, + "score_dict": score_dict, + "aug_mode": self.args.aug_mode, + "insights" : exps[i], + "user_requirement": user_requirement, + "args": self.args + }) + scores = [score_dict["test_score"] for score_dict in scores] + avg_score = sum(scores) / len(scores) + best_score = max(scores) if not self.args.low_is_better else min(scores) + best_score_idx = scores.index(best_score) + results.insert(0, {"avg_score": avg_score, "best_score": best_score, "best_score_idx": best_score_idx}) + self.save_results(results) + + + \ No newline at end of file diff --git a/expo/experimenter/aug_experimenter.py b/expo/experimenter/aug_experimenter.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index d7ed82070..092af3694 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -1,18 +1,57 @@ +from expo.utils import DATA_CONFIG +import os +import pandas as pd +from expo.evaluation.evaluation import evaluate_score +import datetime +import json +from expo.MCTS import create_initial_state +from expo.research_assistant import ResearchAssistant + class Experimenter: result_path : str = "results" + data_config = DATA_CONFIG + + + def __init__(self, args, **kwargs): + self.args = args + self.start_time = datetime.datetime.now().strftime("%Y%m%d%H%M") async def run_experiment(self): - pass + state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") + user_requirement = state["requirement"] + di = ResearchAssistant(node_id="0", use_reflection=self.args.use_reflection) + await di.run(user_requirement) + + score_dict = await di.get_score(low_is_better=False) + score_dict = self.evaluate(score_dict, state) + results = { + "score_dict": score_dict, + "aug_mode": self.args.aug_mode, + "user_requirement": user_requirement, + "args": self.args + } + self.save_result(results) + def evaluate_prediction(self, split, state): + pred_path = os.path.join(state["work_dir"], state["task"], f"{split}_predictions.csv") + pred_node_path = os.path.join(state["node_dir"], f"{self.start_time}-{split}_predictions.csv") + gt_path = os.path.join(state["datasets_dir"][f"{split}_target"]) + preds = pd.read_csv(pred_path)["target"] + preds.to_csv(pred_node_path, index=False) + gt = pd.read_csv(gt_path)["target"] + metric = state["dataset_config"]["metric"] + return evaluate_score(preds, gt, metric) - def save_scores(self): - pass + def evaluate(self, score_dict, state): + scores = { + "dev_score": self.evaluate_prediction("dev", state), + "test_score": self.evaluate_prediction("test", state), + } + score_dict.update(scores) + return score_dict - def save_result(self): - results = { - "test_score": self.test_score, - "num_experiments": self.num_experiments, - "insights": self.insights, - "avg_score": self.avg_score, - } \ No newline at end of file + + def save_result(self, result): + with open(f"{self.result_path}/{self.args.task}_{self.start_time}.json", "w") as f: + json.dump(result, f, indent=4) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py new file mode 100644 index 000000000..2523588b9 --- /dev/null +++ b/expo/experimenter/mcts.py @@ -0,0 +1,44 @@ +from expo.experimenter import Experimenter +from expo.dataset import generate_task_requirement +from expo.MCTS import MCTS +from expo.evaluation.visualize_mcts import get_tree_text + + +class MCTSExperimenter(Experimenter): + result_path : str = "results/mcts" + async def run_experiment(self): + mcts = MCTS(root_node=None, max_depth=5) + best_nodes = await mcts.search(self.args.task, self.data_config, + low_is_better=self.args.low_is_better, + load_tree=self.args.load_tree, + reflection=self.args.reflection, + rollout=self.args.rollout, + name=self.args.name) + best_node = best_nodes["global_best"] + dev_best_node = best_nodes["dev_best"] + + text, num_generated_codes = get_tree_text(mcts.root_node) + text += f"Generated {num_generated_codes} unique codes.\n" + text += f"Best node: {best_node}, score: {best_node.raw_reward}\n" + text += f"Dev best node: {dev_best_node}, score: {dev_best_node.raw_reward}\n" + print(text) + self.save_tree(text) + + results = { + "best_node": best_node, + "best_node_score": best_node.raw_reward, + "dev_best_node": dev_best_node, + "dev_best_node_score": dev_best_node.raw_reward, + "num_generated_codes": num_generated_codes, + "user_requirement": best_node.state["requirement"], + "args": self.args + } + self.save_result(results) + + + + def save_tree(self, tree_text): + fpath = f"{self.result_path}/{self.args.task}_tree_{self.args.name}.txt" + with open(fpath, "w") as f: + f.write(tree_text) + diff --git a/expo/experimenter/mcts_experimenter.py b/expo/experimenter/mcts_experimenter.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/expo/insights/InsightGenerate.py b/expo/insights/InsightGenerate.py index 55ab64e30..35bed976a 100644 --- a/expo/insights/InsightGenerate.py +++ b/expo/insights/InsightGenerate.py @@ -23,7 +23,7 @@ import json from metagpt.llm import LLM from metagpt.schema import Message -from expo.utils import load_data_config, mcts_logger +from expo.utils import load_data_config, mcts_logger, clean_json_from_rsp DATA_CONFIG = load_data_config() @@ -52,17 +52,6 @@ def sample_instruction_set(data): for task_id in sorted(data_dict.keys()): instruction_set.append(random.choice(data_dict[task_id])) return instruction_set - - - @staticmethod - def clean_json_from_rsp(text): - pattern = r"```json(.*?)```" - matches = re.findall(pattern, text, re.DOTALL) - if matches: - json_str = "\n".join(matches) - return json_str - else: - return "" @staticmethod def format_output(rsp): @@ -109,6 +98,6 @@ async def generate_new_instruction(original_instruction, insights): llm_response = await llm.aask( context, system_msgs=[REFLECTION_SYSTEM_MSG] ) - rsp = InsightGenerator.clean_json_from_rsp(llm_response) + rsp = clean_json_from_rsp(llm_response) new_instruction = json.loads(rsp)["New Instruction"] return new_instruction \ No newline at end of file diff --git a/expo/research_assistant.py b/expo/research_assistant.py index 7b844cf5e..c26f24586 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -8,7 +8,6 @@ from metagpt.utils.common import CodeParser from metagpt.utils.common import write_json_file, read_json_file, format_trackback_info from metagpt.const import MESSAGE_ROUTE_TO_ALL, SERDESER_PATH -from metagpt.utils.recovery_util import save_history from expo.utils import mcts_logger, save_notebook from pydantic import Field, model_validator from metagpt.actions.di.write_analysis_code import CheckData, WriteAnalysisCode @@ -126,7 +125,6 @@ def save_state(self, static_save=False): role_path = os.path.join(stg_path, f"{name}.json") # 将状态保存为 JSON 文件 write_json_file(role_path, self.model_dump()) - save_history(role=self, save_dir=stg_path, name=name) def remap_tasks(self): diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 0c7468ac9..1704faa06 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -1,7 +1,4 @@ -from expo.MCTS import MCTS, Node, initialize_di_root_node -from expo.utils import load_data_config -from expo.dataset import generate_task_requirement -from expo.evaluation.visualize_mcts import get_tree_text +from expo.experimenter import MCTSExperimenter, Experimenter, AugExperimenter import asyncio import argparse @@ -9,11 +6,10 @@ def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--name", type=str, default="") + parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base"]) get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) - - return parser.parse_args() @@ -38,7 +34,15 @@ def get_di_args(parser): async def main(args): - pass + if args.exp_mode == "mcts": + experimenter = MCTSExperimenter(args) + elif args.exp_mode == "aug": + experimenter = AugExperimenter(args) + elif args.exp_mode == "base": + experimenter = Experimenter(args) + else: + raise ValueError(f"Invalid exp_mode: {args.exp_mode}") + await experimenter.run_experiment() if __name__ == "__main__": args = get_args() diff --git a/expo/run_mcts.py b/expo/run_mcts.py index 6d2c421ec..7b2e2b4da 100644 --- a/expo/run_mcts.py +++ b/expo/run_mcts.py @@ -27,17 +27,19 @@ def get_args(): if __name__ == "__main__": args = get_args() - requirement = generate_task_requirement(args.task, data_config) - print(requirement) + # requirement = generate_task_requirement(args.task, data_config) + # print(requirement) # role, root_node = initialize_di_root_node(requirement, data_config) # asyncio.run(role.run(requirement)) # asyncio.run(root_node.run_node()) mcts = MCTS(root_node=None, max_depth=5) - best_node = asyncio.run(mcts.search(args.task, data_config, + best_nodes = asyncio.run(mcts.search(args.task, data_config, low_is_better=args.low_is_better, load_tree=args.load_tree, reflection=args.reflection, rollout=args.rollout, name=args.name)) + best_node = best_nodes["global_best"] + dev_best_node = best_nodes["dev_best"] text, num_generated_codes = get_tree_text(mcts.root_node) print(text) print(f"Generated {num_generated_codes} unique codes.") @@ -45,6 +47,7 @@ def get_args(): with open(f"results/{args.task}_tree{args.name}.txt", "w") as f: f.write(f"Generated {num_generated_codes} unique codes.\n") f.write(f"Best node: {best_node}, score: {best_node.raw_reward}\n") + f.write(f"Dev best node: {dev_best_node}, score: {dev_best_node.raw_reward}\n") f.write(text) diff --git a/expo/utils.py b/expo/utils.py index 423889f29..20e3fa7f5 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -1,7 +1,6 @@ import yaml from metagpt.roles.role import Role from metagpt.actions.di.execute_nb_code import ExecuteNbCode -from metagpt.utils.save_code import save_code_file # from nbclient import NotebookClient from nbformat.notebooknode import NotebookNode import nbformat @@ -86,7 +85,8 @@ def process_cells(nb: NotebookNode) -> NotebookNode: def save_notebook(role: Role, save_dir: str = "", name: str = ""): save_dir = Path(save_dir) nb = process_cells(role.execute_code.nb) - save_code_file(name=name, code_context=nb, file_format="ipynb", save_dir=save_dir) + file_path = save_dir / f"{name}.ipynb" + nbformat.write(nb, file_path) async def load_execute_notebook(role): tasks = role.planner.plan.tasks diff --git a/metagpt/prompts/di/write_analysis_code.py b/metagpt/prompts/di/write_analysis_code.py index f8b9a4c42..beee80679 100644 --- a/metagpt/prompts/di/write_analysis_code.py +++ b/metagpt/prompts/di/write_analysis_code.py @@ -69,7 +69,7 @@ def add(a: int, b: int) -> int: ```json {{ "reflection": str = "Reflection on previous implementation", - "improved_impl": str = "Refined code after reflection.", + "improved_impl": str = "Refined code after reflection (do not include nested code block here).", }} ``` """ From ae6a19575091786c44d5a296d309a90df0c7beef Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 30 Aug 2024 20:35:17 +0800 Subject: [PATCH 006/135] fix bug - make rollout more consistent --- expo/MCTS.py | 9 +++++++-- expo/README.md | 32 ++++++++++++++++++++++++++++++++ expo/dataset.py | 5 ++++- expo/experimenter/mcts.py | 2 +- expo/run_experiment.py | 6 +++--- expo/run_mcts.py | 4 ++-- metagpt/prompts/task_type.py | 2 +- 7 files changed, 50 insertions(+), 10 deletions(-) create mode 100644 expo/README.md diff --git a/expo/MCTS.py b/expo/MCTS.py index 5c502f917..5c448e3ac 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -284,7 +284,7 @@ def get_num_simulations(self): return self.root_node.visited async def search(self, task, data_config, name, - rollout=3, load_tree=False, low_is_better=False, reflection=False): + rollouts, load_tree=False, low_is_better=False, reflection=False): role, root = initialize_di_root_node(task, data_config, low_is_better=low_is_better, reflection=reflection, name=name) self.root_node = root @@ -292,7 +292,12 @@ async def search(self, task, data_config, name, if load_tree: tree_loaded = self.load_tree() mcts_logger.log("MCTS", f"Number of simulations: {self.get_num_simulations()}") + + if not tree_loaded: + rollouts -= 2 + if rollouts < 0: + raise ValueError("Rollouts must be greater than 2 if there is no tree to load") self.children[root] = [] reward = await self.simulate(root, role) self.backpropagate(root, reward) @@ -307,7 +312,7 @@ async def search(self, task, data_config, name, else: root = self.root_node # 后续迭代：使用UCT进行选择，expand并模拟和反向传播 - for _ in range(rollout): # 迭代次数 + for _ in range(rollouts): # 迭代次数 mcts_logger.log("MCTS", f"开始第{_+1}次迭代") leaf = self.select(root) if leaf.is_terminal(): diff --git a/expo/README.md b/expo/README.md new file mode 100644 index 000000000..91701cc18 --- /dev/null +++ b/expo/README.md @@ -0,0 +1,32 @@ +# Expo + + +## Instruction + +- 下载数据集：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink + + +## Examples + +### Run Base DI + +`python run_experiment.py --exp_mode base --task titanic` + +### Run DI RandExp + +- Single insight +`python run_experiment.py --exp_mode aug --task titanic --aug_mode single` + +- Set insight +`python run_experiment.py --exp_mode aug --task titanic --aug_mode set` + + + +### Run DI MCTS +`python run_experiment.py --exp_mode mcts --task titanic --rollout 5` + +`python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` + + + + diff --git a/expo/dataset.py b/expo/dataset.py index 3f3fa1db1..be7388365 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -17,7 +17,10 @@ TASK_PROMPT = """\ # User requirement {user_requirement} -**Attention** Please do not leak the target label in any form during training. +**Attention** +1. Please do not leak the target label in any form during training. +2. Dev and Test sets do not have the target column. +3. You should perform transformations on all sets at the same step. ## Saving Dev and Test Predictions Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 2523588b9..91dbd4c6c 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -12,7 +12,7 @@ async def run_experiment(self): low_is_better=self.args.low_is_better, load_tree=self.args.load_tree, reflection=self.args.reflection, - rollout=self.args.rollout, + rollouts=self.args.rollouts, name=self.args.name) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 1704faa06..4c8244803 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -16,12 +16,12 @@ def get_args(): def get_mcts_args(parser): parser.add_argument("--load_tree", dest="load_tree", action="store_true") parser.add_argument("--no_load_tree", dest="load_tree", action="store_false") - parser.set_defaults(load_tree=True) - parser.add_argument("--rollout", type=int, default=3) + parser.set_defaults(load_tree=False) + parser.add_argument("--rollouts", type=int, default=3) def get_aug_exp_args(parser): parser.add_argument("--aug_mode", type=str, default="single", choices=["single", "set"]) - parser.add_argument("--num_experiments", type=int, default=2) + parser.add_argument("--num_experiments", type=int, default=1) def get_di_args(parser): diff --git a/expo/run_mcts.py b/expo/run_mcts.py index 7b2e2b4da..20d4171f7 100644 --- a/expo/run_mcts.py +++ b/expo/run_mcts.py @@ -18,7 +18,7 @@ def get_args(): parser.add_argument("--reflection", dest="reflection", action="store_true") parser.add_argument("--no_reflection", dest="reflection", action="store_false") parser.set_defaults(reflection=True) - parser.add_argument("--rollout", type=int, default=3) + parser.add_argument("--rollouts", type=int, default=3) parser.add_argument("--name", type=str, default="") return parser.parse_args() @@ -37,7 +37,7 @@ def get_args(): mcts = MCTS(root_node=None, max_depth=5) best_nodes = asyncio.run(mcts.search(args.task, data_config, low_is_better=args.low_is_better, load_tree=args.load_tree, - reflection=args.reflection, rollout=args.rollout, name=args.name)) + reflection=args.reflection, rollouts=args.rollouts, name=args.name)) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] text, num_generated_codes = get_tree_text(mcts.root_node) diff --git a/metagpt/prompts/task_type.py b/metagpt/prompts/task_type.py index 5b1ffc744..116756edc 100644 --- a/metagpt/prompts/task_type.py +++ b/metagpt/prompts/task_type.py @@ -25,7 +25,7 @@ - Use available feature engineering tools if they are potential impactful. - Avoid creating redundant or excessively numerous features in one step. - Exclude ID columns from feature generation and remove them. -- Each feature engineering operation performed on the train set must also applies to the test separately at the same time. +- Each feature engineering operation performed on the train set must also applies to the dev/test separately at the same time. - Avoid using the label column to create features, except for cat encoding. - Use the data from previous task result if exist, do not mock or reload data yourself. - Always copy the DataFrame before processing it and use the copy to process. From 16d1bf0da0e46a9885d7cba9b12cdae93fac065a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 09:59:47 +0800 Subject: [PATCH 007/135] Rename insight generate to instruction generator --- expo/MCTS.py | 2 +- expo/experimenter/aug.py | 8 ++++---- .../{InsightGenerate.py => instruction_generator.py} | 8 ++++---- expo/run_exp_augmentation.py | 8 ++++---- 4 files changed, 13 insertions(+), 13 deletions(-) rename expo/insights/{InsightGenerate.py => instruction_generator.py} (92%) diff --git a/expo/MCTS.py b/expo/MCTS.py index 5c448e3ac..42c93f650 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -3,7 +3,7 @@ import os import pandas as pd from expo.research_assistant import ResearchAssistant -from expo.insights.InsightGenerate import InsightGenerator +from exp_optimizer.expo.insights.instruction_generator import InstructionGenerator from expo.dataset import get_split_dataset_path, generate_task_requirement from expo.evaluation.evaluation import evaluate_score from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 28643d47f..299c053cd 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -2,7 +2,7 @@ from expo.MCTS import create_initial_state from expo.dataset import generate_task_requirement from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path -from expo.insights.InsightGenerate import InsightGenerator +from exp_optimizer.expo.insights.instruction_generator import InstructionGenerator from expo.research_assistant import ResearchAssistant EXPS_PROMPT = """ @@ -21,12 +21,12 @@ async def run_experiment(self): state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") user_requirement = state["requirement"] exp_pool_path = get_exp_pool_path(self.args.task, self.data_config, pool_name="ds_analysis_pool") - exp_pool = InsightGenerator.load_analysis_pool(exp_pool_path) + exp_pool = InstructionGenerator.load_analysis_pool(exp_pool_path) if self.args.aug_mode == "single": - exps = InsightGenerator._random_sample(exp_pool, self.args.num_experiments) + exps = InstructionGenerator._random_sample(exp_pool, self.args.num_experiments) exps = [exp["Analysis"] for exp in exps] elif self.args.aug_mode == "set": - exp_set = InsightGenerator.sample_instruction_set(exp_pool) + exp_set = InstructionGenerator.sample_instruction_set(exp_pool) exp_set_text = "\n".join([f"{exp['task_id']}: {exp['Analysis']}" for exp in exp_set]) exps = [exp_set_text] * self.args.num_experiments else: diff --git a/expo/insights/InsightGenerate.py b/expo/insights/instruction_generator.py similarity index 92% rename from expo/insights/InsightGenerate.py rename to expo/insights/instruction_generator.py index 35bed976a..4f4155ff8 100644 --- a/expo/insights/InsightGenerate.py +++ b/expo/insights/instruction_generator.py @@ -27,7 +27,7 @@ DATA_CONFIG = load_data_config() -class InsightGenerator: +class InstructionGenerator: data_config = DATA_CONFIG @staticmethod @@ -68,7 +68,7 @@ def format_output(rsp): @staticmethod def load_analysis_pool(file_path, task_id=None): - data = InsightGenerator.load_json_data(file_path) + data = InstructionGenerator.load_json_data(file_path) for item in data: if "task_id" not in item: raise ValueError("task_id is not found in the analysis pool") @@ -79,14 +79,14 @@ def load_analysis_pool(file_path, task_id=None): @staticmethod async def generate_new_instructions(task_id, original_instruction, max_num, file_path): - data = InsightGenerator.load_analysis_pool(file_path, task_id) + data = InstructionGenerator.load_analysis_pool(file_path, task_id) new_instructions = [] if len(data) == 0: mcts_logger.log("MCTS", f"No insights available for task {task_id}") return [original_instruction] # Return the original instruction if no insights are available for item in data[:max_num]: insights = item["Analysis"] - new_instruction = await InsightGenerator.generate_new_instruction(original_instruction, insights) + new_instruction = await InstructionGenerator.generate_new_instruction(original_instruction, insights) new_instructions.append(new_instruction) return new_instructions diff --git a/expo/run_exp_augmentation.py b/expo/run_exp_augmentation.py index f4d22093f..edf01c164 100644 --- a/expo/run_exp_augmentation.py +++ b/expo/run_exp_augmentation.py @@ -3,7 +3,7 @@ import asyncio from expo.utils import DATA_CONFIG, get_exp_pool_path from expo.dataset import generate_task_requirement -from expo.insights.InsightGenerate import InsightGenerator +from exp_optimizer.expo.insights.instruction_generator import InstructionGenerator from expo.MCTS import create_initial_state from expo.evaluation.evaluation import evaluate_score import json @@ -48,12 +48,12 @@ async def main(task_name, use_reflection=True, mode="single", num_experiments=2) user_requirement = generate_task_requirement(task_name, data_config) exp_pool_path = get_exp_pool_path(task_name, data_config, pool_name="ds_analysis_pool") - exp_pool = InsightGenerator.load_analysis_pool(exp_pool_path) + exp_pool = InstructionGenerator.load_analysis_pool(exp_pool_path) if mode == "single": - exps = InsightGenerator._random_sample(exp_pool, num_experiments) + exps = InstructionGenerator._random_sample(exp_pool, num_experiments) exps = [exp["Analysis"] for exp in exps] elif mode == "set": - exp_set = InsightGenerator.sample_instruction_set(exp_pool) + exp_set = InstructionGenerator.sample_instruction_set(exp_pool) exp_set_text = "\n".join([f"{exp['task_id']}: {exp['Analysis']}" for exp in exp_set]) exps = [exp_set_text] * num_experiments else: From 27bbc927b09f0d62b33ba893cb0fc8795340c775 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 10:21:30 +0800 Subject: [PATCH 008/135] 1. Rewrite logger message 2. fix import --- expo/MCTS.py | 50 +++++++++++++++++------------------- expo/experimenter/aug.py | 2 +- expo/run_exp_augmentation.py | 2 +- 3 files changed, 25 insertions(+), 29 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 42c93f650..efb928d99 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -3,7 +3,7 @@ import os import pandas as pd from expo.research_assistant import ResearchAssistant -from exp_optimizer.expo.insights.instruction_generator import InstructionGenerator +from expo.insights.instruction_generator import InstructionGenerator from expo.dataset import get_split_dataset_path, generate_task_requirement from expo.evaluation.evaluation import evaluate_score from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path @@ -135,13 +135,13 @@ def save_new_role(self, role: ResearchAssistant): role.start_task_id = self.state['start_task_id'] role.state_saved = False role.change_next_instruction(self.action) - mcts_logger.log("MCTS", f"保存新的role: {role.node_id}") + mcts_logger.log("MCTS", f"Saving new role: {role.node_id}") role.save_state(static_save=True) async def expand(self, max_children): if self.is_fully_expanded(): return - insight_geneartor = InsightGenerator() + insight_geneartor = InstructionGenerator() role = self.load_role() original_instruction = role.get_next_instruction() insights = await insight_geneartor.generate_new_instructions(task_id=role.start_task_id + 1, @@ -224,7 +224,7 @@ def __init__(self, root_node, max_depth): def select(self, node: Node): node = self.best_child() - mcts_logger.log("MCTS", f"选择的叶子节点id: {node.id}") + mcts_logger.log("MCTS", f"Selected node id: {node.id}") return node def best_child(self): @@ -245,9 +245,11 @@ async def expand(self, node : Node, max_children=4): async def simulate(self, node : Node, role=None): "Returns the reward for a random simulation (to completion) of `node`" + mcts_logger.log("MCTS", f"Start simulating node {node.id}:") while node.children: node = random.choice(node.children) - reward = await node.run_node(role) + reward = await node.run_node(role) + mcts_logger.log("MCTS", f"Simulated node's reward: {reward}") return reward @@ -292,7 +294,7 @@ async def search(self, task, data_config, name, if load_tree: tree_loaded = self.load_tree() mcts_logger.log("MCTS", f"Number of simulations: {self.get_num_simulations()}") - + mcts_logger.log("MCTS", f"Tree loaded: {tree_loaded}") if not tree_loaded: rollouts -= 2 @@ -301,41 +303,36 @@ async def search(self, task, data_config, name, self.children[root] = [] reward = await self.simulate(root, role) self.backpropagate(root, reward) - mcts_logger.log("MCTS", f"Root node's value: {reward}") children = await self.expand(root) #目前是随机选择1个，后续可以改成多个 first_leaf = random.choice(children) - mcts_logger.log("MCTS", f"随机选择的叶子节点id: {first_leaf.id}") reward = await self.simulate(first_leaf) - mcts_logger.log("MCTS", f"模拟完毕的叶子节点的Normalized score: {reward}") self.backpropagate(first_leaf, reward) else: root = self.root_node # 后续迭代：使用UCT进行选择，expand并模拟和反向传播 - for _ in range(rollouts): # 迭代次数 - mcts_logger.log("MCTS", f"开始第{_+1}次迭代") - leaf = self.select(root) - if leaf.is_terminal(): - if leaf.raw_value == 0: - reward = await self.simulate(leaf) + for _ in range(rollouts): # number of rollouts + mcts_logger.log("MCTS", f"Start the next rollout {_+1}") + node = self.select(root) + if node.is_terminal(): + if node.raw_value == 0: + reward = await self.simulate(node) else: - reward = {"test_score": leaf.raw_value, "score": leaf.value} - mcts_logger.log("MCTS", f"终止节点的得分为: {reward}") - self.backpropagate(leaf, reward) + reward = {"test_score": node.raw_value, "score": node.value} + mcts_logger.log("MCTS", f"Terminal node's reward: {reward}") + self.backpropagate(node, reward) else: - if leaf.visited > 0: - children = await self.expand(leaf) - leaf = random.choice(children) - mcts_logger.log("MCTS", f"随机选择的叶子节点id: {leaf.id}") - reward = await self.simulate(leaf) - mcts_logger.log("MCTS", f"模拟完毕的叶子节点{leaf.id}的Normalized score: {reward}") - self.backpropagate(leaf, reward) + if node.visited > 0: + children = await self.expand(node) + node = random.choice(children) + reward = await self.simulate(node) + self.backpropagate(node, reward) return self.best_path(root) def load_tree(self): def load_children_node(node): - mcts_logger.log("MCTS", f"加载节点{node.id}的子节点：{node.children}") + mcts_logger.log("MCTS", f"Load node {node.id}'s child: {node.children}") if node.is_terminal() or not node.children: return for child in node.children: @@ -351,6 +348,5 @@ def load_children_node(node): self.children[self.root_node] = self.root_node.children load_children_node(self.root_node) if self.children: - mcts_logger.log("MCTS", "成功加载树") return True return False \ No newline at end of file diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 299c053cd..ae575ed75 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -2,7 +2,7 @@ from expo.MCTS import create_initial_state from expo.dataset import generate_task_requirement from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path -from exp_optimizer.expo.insights.instruction_generator import InstructionGenerator +from expo.insights.instruction_generator import InstructionGenerator from expo.research_assistant import ResearchAssistant EXPS_PROMPT = """ diff --git a/expo/run_exp_augmentation.py b/expo/run_exp_augmentation.py index edf01c164..3f8eff3b3 100644 --- a/expo/run_exp_augmentation.py +++ b/expo/run_exp_augmentation.py @@ -3,7 +3,7 @@ import asyncio from expo.utils import DATA_CONFIG, get_exp_pool_path from expo.dataset import generate_task_requirement -from exp_optimizer.expo.insights.instruction_generator import InstructionGenerator +from expo.insights.instruction_generator import InstructionGenerator from expo.MCTS import create_initial_state from expo.evaluation.evaluation import evaluate_score import json From 0b30866d109064bfbf76d9e4b852bbe983cff8b6 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 10:39:08 +0800 Subject: [PATCH 009/135] fix prompt's json format --- expo/research_assistant.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/research_assistant.py b/expo/research_assistant.py index c26f24586..e0dc73418 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -31,7 +31,7 @@ {{ "train_score": x.x, "dev_score": x.x, - "test_score": x.x, + "test_score": x.x }} ``` """ From 0e5db1c3642ccd5118a09e5d047b250a9c33d95a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 14:07:14 +0800 Subject: [PATCH 010/135] 1. add ml module 2. fix bug --- expo/MCTS.py | 2 +- expo/README.md | 1 + expo/dataset.py | 2 +- expo/experimenter/aug.py | 10 +++++----- expo/experimenter/experimenter.py | 13 +++++++------ expo/experimenter/mcts.py | 2 +- requirements.txt | 5 +++++ 7 files changed, 21 insertions(+), 14 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index efb928d99..02e80bb52 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -269,7 +269,7 @@ def bfs(node : Node, best_score, best_child : Node, split): if node not in self.children: return best_score, best_child for child in self.children[node]: - score = child.normalized[split] + score = child.normalized_reward[split] print(child.id, score) if score > best_score: best_score = score diff --git a/expo/README.md b/expo/README.md index 91701cc18..baf15bb75 100644 --- a/expo/README.md +++ b/expo/README.md @@ -25,6 +25,7 @@ ### Run DI MCTS `python run_experiment.py --exp_mode mcts --task titanic --rollout 5` +If the dataset has reg metric, remember to use `--low_is_better` `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` diff --git a/expo/dataset.py b/expo/dataset.py index be7388365..9667c0aef 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -24,7 +24,7 @@ ## Saving Dev and Test Predictions Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. -Both files should contain a single `target` column with the predicted values. +Both files should contain a single column named `target` with the predicted values. Make sure the prediction results are in the same format as the target column in the training set. The labels should be transformed back to the original format if any transformation was applied during training. ## Output Training Set Performance diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index ae575ed75..9c6915103 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -34,12 +34,12 @@ async def run_experiment(self): results = [] for i in range(self.args.num_experiments): - di = ResearchAssistant(node_id=str(i), use_reflection=self.args.use_reflection) + di = ResearchAssistant(node_id=str(i), use_reflection=self.args.reflection) di.role_dir = f"{di.role_dir}_{self.args.task}" requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) print(requirement) await di.run(requirement) - score_dict = await di.get_score(low_is_better=False) + score_dict = await di.get_score() score_dict = self.evaluate(score_dict, state) results.append({ "idx": i, @@ -47,14 +47,14 @@ async def run_experiment(self): "aug_mode": self.args.aug_mode, "insights" : exps[i], "user_requirement": user_requirement, - "args": self.args + "args": vars(self.args) }) - scores = [score_dict["test_score"] for score_dict in scores] + scores = [result["score_dict"]["test_score"] for result in results] avg_score = sum(scores) / len(scores) best_score = max(scores) if not self.args.low_is_better else min(scores) best_score_idx = scores.index(best_score) results.insert(0, {"avg_score": avg_score, "best_score": best_score, "best_score_idx": best_score_idx}) - self.save_results(results) + self.save_result(results) \ No newline at end of file diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 092af3694..4473866af 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -9,7 +9,7 @@ class Experimenter: - result_path : str = "results" + result_path : str = "results/base" data_config = DATA_CONFIG @@ -20,21 +20,21 @@ def __init__(self, args, **kwargs): async def run_experiment(self): state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") user_requirement = state["requirement"] - di = ResearchAssistant(node_id="0", use_reflection=self.args.use_reflection) + di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) await di.run(user_requirement) - score_dict = await di.get_score(low_is_better=False) + score_dict = await di.get_score() score_dict = self.evaluate(score_dict, state) results = { "score_dict": score_dict, - "aug_mode": self.args.aug_mode, "user_requirement": user_requirement, - "args": self.args + "args": vars(self.args) } self.save_result(results) def evaluate_prediction(self, split, state): pred_path = os.path.join(state["work_dir"], state["task"], f"{split}_predictions.csv") + os.makedirs(state["node_dir"], exist_ok=True) pred_node_path = os.path.join(state["node_dir"], f"{self.start_time}-{split}_predictions.csv") gt_path = os.path.join(state["datasets_dir"][f"{split}_target"]) preds = pd.read_csv(pred_path)["target"] @@ -53,5 +53,6 @@ def evaluate(self, score_dict, state): def save_result(self, result): - with open(f"{self.result_path}/{self.args.task}_{self.start_time}.json", "w") as f: + os.makedirs(self.result_path, exist_ok=True) + with open(f"{self.result_path}/{self.args.exp_mode}-{self.args.task}_{self.start_time}.json", "w") as f: json.dump(result, f, indent=4) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 91dbd4c6c..3399fcff9 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -31,7 +31,7 @@ async def run_experiment(self): "dev_best_node_score": dev_best_node.raw_reward, "num_generated_codes": num_generated_codes, "user_requirement": best_node.state["requirement"], - "args": self.args + "args": vars(self.args) } self.save_result(results) diff --git a/requirements.txt b/requirements.txt index 7ea849f5a..271fade14 100644 --- a/requirements.txt +++ b/requirements.txt @@ -80,3 +80,8 @@ boto3~=1.34.69 spark_ai_python~=0.3.30 agentops openml==0.14.2 + +# ml module to run in DI +xgboost +catboost +lightgbm From f28908e4c58ea1147cd0630e562a58dd9f836fbc Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 15:15:53 +0800 Subject: [PATCH 011/135] =?UTF-8?q?1.=20role.py=20-=20=E5=A6=82=E6=9E=9C?= =?UTF-8?q?=E5=B7=B2=E7=BB=8F=E6=9C=89plan=EF=BC=8C=E4=BE=BF=E4=B8=8D?= =?UTF-8?q?=E5=86=8D=E9=87=8D=E5=A4=8D=E7=94=9F=E6=88=90=202.=20=E4=BF=AE?= =?UTF-8?q?=E6=94=B9prompt=EF=BC=8C=E8=AE=A9predictions.csv=E7=94=9F?= =?UTF-8?q?=E6=88=90=E7=9A=84=E6=A0=BC=E5=BC=8F=E4=B8=8E=E5=8E=9Fgt?= =?UTF-8?q?=E6=A0=BC=E5=BC=8F=E4=B8=80=E6=A0=B7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/MCTS.py | 3 ++- expo/dataset.py | 8 +++++--- expo/experimenter/aug.py | 2 +- expo/experimenter/mcts.py | 4 ++-- metagpt/roles/role.py | 8 ++++---- 5 files changed, 14 insertions(+), 11 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 02e80bb52..5b1cb36dc 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -42,6 +42,7 @@ class Node(): value : float = 0 visited : int = 0 children : list = [] + normalized_reward : dict = {"train_score": 0, "dev_score": 0, "test_score": 0} parent = None def __init__(self, parent=None, state = None, action=None, value = 0, max_depth=4, **kwargs): @@ -274,7 +275,7 @@ def bfs(node : Node, best_score, best_child : Node, split): if score > best_score: best_score = score best_child = child - best_score, best_child = bfs(child, best_score, best_child) + best_score, best_child = bfs(child, best_score, best_child, split) return best_score, best_child _, best_child = bfs(root, best_score, best_child, "test_score") _, dev_best_child = bfs(root, best_score, best_child, "dev_score") diff --git a/expo/dataset.py b/expo/dataset.py index 9667c0aef..3f59c3367 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -23,9 +23,11 @@ 3. You should perform transformations on all sets at the same step. ## Saving Dev and Test Predictions -Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. -Both files should contain a single column named `target` with the predicted values. -Make sure the prediction results are in the same format as the target column in the training set. The labels should be transformed back to the original format if any transformation was applied during training. +1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. +- Both files should contain a single column named `target` with the predicted values. +2. Make sure the prediction results are in the same format as the target column in the training set. +- The labels should be transformed back to the original format if any transformation was applied during training. +- If the original target column was categorical, the predictions should be in the same format. ## Output Training Set Performance Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 9c6915103..956849717 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -46,7 +46,7 @@ async def run_experiment(self): "score_dict": score_dict, "aug_mode": self.args.aug_mode, "insights" : exps[i], - "user_requirement": user_requirement, + "user_requirement": requirement, "args": vars(self.args) }) scores = [result["score_dict"]["test_score"] for result in results] diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 3399fcff9..43c5f9868 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -25,9 +25,9 @@ async def run_experiment(self): self.save_tree(text) results = { - "best_node": best_node, + "best_node": best_node.id, "best_node_score": best_node.raw_reward, - "dev_best_node": dev_best_node, + "dev_best_node": dev_best_node.id, "dev_best_node_score": dev_best_node.raw_reward, "num_generated_codes": num_generated_codes, "user_requirement": best_node.state["requirement"], diff --git a/metagpt/roles/role.py b/metagpt/roles/role.py index 5ecc7ae33..1e786898c 100644 --- a/metagpt/roles/role.py +++ b/metagpt/roles/role.py @@ -478,10 +478,10 @@ async def _react(self) -> Message: async def _plan_and_act(self) -> Message: """first plan, then execute an action sequence, i.e. _think (of a plan) -> _act -> _act -> ... Use llm to come up with the plan dynamically.""" - - # create initial plan and update it until confirmation - goal = self.rc.memory.get()[-1].content # retreive latest user requirement - await self.planner.update_plan(goal=goal) + if not self.planner.plan.goal: + # create initial plan and update it until confirmation + goal = self.rc.memory.get()[-1].content # retreive latest user requirement + await self.planner.update_plan(goal=goal) # take on tasks until all finished while self.planner.current_task: From f2603a9d31d0509079cb891729f5efccfedab18a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 16:51:33 +0800 Subject: [PATCH 012/135] remove deprecated comments --- expo/research_assistant.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/expo/research_assistant.py b/expo/research_assistant.py index e0dc73418..ed935b4b8 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -101,11 +101,8 @@ def set_plan_and_tool(self) -> "Interpreter": async def _act_on_task(self, current_task: Task) -> TaskResult: """Useful in 'plan_and_act' mode. Wrap the output in a TaskResult for review and confirmation.""" mcts_logger.info(f"The current_task is: {current_task}") - - # 执行任务的代码 code, result, is_success = await self._write_and_exec_code() task_result = TaskResult(code=code, result=result, is_success=is_success) - # 只在任务类型为 'feature engineering' 时保存状态 if int(current_task.task_id) == self.start_task_id + 1: # fe_id = current_task.dependent_task_ids self.save_state() From ab8be7a18354bcff32b52c50b07fa0da948b259f Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 17:01:25 +0800 Subject: [PATCH 013/135] update readme --- expo/README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/expo/README.md b/expo/README.md index baf15bb75..02519f640 100644 --- a/expo/README.md +++ b/expo/README.md @@ -29,5 +29,16 @@ If the dataset has reg metric, remember to use `--low_is_better` `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` +## Code and Configs Explanation +`datasets.yaml` 提供数据集对应的指标和基础提示词 + +`data.yaml` 继承了`datasets.yaml`以及一些路径信息，需要将`datasets_dir`指到数据集合集的根目录下 + +完整的DI提示词参考`dataset.py`中的`generate_task_requirement`函数 + + +## Evaluation + +`evaluation.py` 提供pred和原始的gt（1D iterable）以及需要使用的metric，返回evaluation score From a1668a1d9dbf4622e46de634a47cac596b067b6c Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 20:00:46 +0800 Subject: [PATCH 014/135] add custom experimenter --- expo/MCTS.py | 2 ++ expo/README.md | 5 ++-- expo/dataset.py | 2 +- expo/experimenter/__init__.py | 3 ++- expo/experimenter/custom.py | 49 +++++++++++++++++++++++++++++++++++ expo/run_experiment.py | 8 +++--- 6 files changed, 62 insertions(+), 7 deletions(-) create mode 100644 expo/experimenter/custom.py diff --git a/expo/MCTS.py b/expo/MCTS.py index 5b1cb36dc..ec5ef9da0 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -205,6 +205,8 @@ async def run_node(self, role=None): if self.state["low_is_better"]: # normalized the score to be between 0 and 1, and higher is better def normalize_score(score): + if score == -1: + return 0 return 1 / (1 + score) score_dict = {k: normalize_score(v) for k, v in score_dict.items()} self.normalized_reward = score_dict diff --git a/expo/README.md b/expo/README.md index 02519f640..856d616b8 100644 --- a/expo/README.md +++ b/expo/README.md @@ -25,8 +25,9 @@ ### Run DI MCTS `python run_experiment.py --exp_mode mcts --task titanic --rollout 5` -If the dataset has reg metric, remember to use `--low_is_better` -`python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` +If the dataset has reg metric, remember to use `--low_is_better`: + +- `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` ## Code and Configs Explanation diff --git a/expo/dataset.py b/expo/dataset.py index 3f59c3367..3e292ba7c 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -27,7 +27,7 @@ - Both files should contain a single column named `target` with the predicted values. 2. Make sure the prediction results are in the same format as the target column in the training set. - The labels should be transformed back to the original format if any transformation was applied during training. -- If the original target column was categorical, the predictions should be in the same format. +- If the original target column was categorical or string, the predictions MUST be in the same format. ## Output Training Set Performance Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. diff --git a/expo/experimenter/__init__.py b/expo/experimenter/__init__.py index d6b50c9d2..2eab295f7 100644 --- a/expo/experimenter/__init__.py +++ b/expo/experimenter/__init__.py @@ -1,3 +1,4 @@ from .experimenter import Experimenter from .mcts import MCTSExperimenter -from .aug import AugExperimenter \ No newline at end of file +from .aug import AugExperimenter +from .custom import CustomExperimenter \ No newline at end of file diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py new file mode 100644 index 000000000..06df4efcf --- /dev/null +++ b/expo/experimenter/custom.py @@ -0,0 +1,49 @@ +from expo.experimenter import Experimenter +from expo.MCTS import create_initial_state +from expo.evaluation.evaluation import evaluate_score +import pandas as pd +import os + +class CustomExperimenter(Experimenter): + result_path : str = "results/custom" + + def __init__(self, args, **kwargs): + super().__init__(args, **kwargs) + self.framework = kwargs["framework"] + self.name = kwargs.get("name", "") + self.result_path = f"results/custom_{self.name}" + self.state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") + + async def run_experiment(self): + user_requirement = self.state["requirement"] + preds = await self.framework.run(user_requirement) + test_preds = preds["test_preds"] + dev_preds = preds["dev_preds"] + score_dict = { + "dev_score": self.evaluate_predictions(dev_preds, "dev"), + "test_score": self.evaluate_predictions(test_preds, "test") + } + results = { + "score_dict": score_dict, + "user_requirement": user_requirement, + "args": vars(self.args) + } + self.save_result(results) + + def evaluate_predictions(self, preds, split): + metric = self.state["dataset_config"]["metric"] + gt_path = os.path.join(self.state["datasets_dir"][f"{split}_target"]) + gt = pd.read_csv(gt_path)["target"] + score = evaluate_score(preds, gt, metric) + return score + + + def load_datasets(self): + train_path = self.state["datasets_dir"]["train"] + dev_path = self.state["datasets_dir"]["dev"] + test_path = self.state["datasets_dir"]["test"] + train = pd.read_csv(train_path) + dev = pd.read_csv(dev_path) + test = pd.read_csv(test_path) + return train, dev, test + diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 4c8244803..826019321 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -1,4 +1,4 @@ -from expo.experimenter import MCTSExperimenter, Experimenter, AugExperimenter +from expo.experimenter import MCTSExperimenter, Experimenter, AugExperimenter, CustomExperimenter import asyncio import argparse @@ -6,7 +6,7 @@ def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--name", type=str, default="") - parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base"]) + parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom"]) get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) @@ -17,7 +17,7 @@ def get_mcts_args(parser): parser.add_argument("--load_tree", dest="load_tree", action="store_true") parser.add_argument("--no_load_tree", dest="load_tree", action="store_false") parser.set_defaults(load_tree=False) - parser.add_argument("--rollouts", type=int, default=3) + parser.add_argument("--rollouts", type=int, default=5) def get_aug_exp_args(parser): parser.add_argument("--aug_mode", type=str, default="single", choices=["single", "set"]) @@ -40,6 +40,8 @@ async def main(args): experimenter = AugExperimenter(args) elif args.exp_mode == "base": experimenter = Experimenter(args) + elif args.exp_mode == "custom": + experimenter = CustomExperimenter(args) else: raise ValueError(f"Invalid exp_mode: {args.exp_mode}") await experimenter.run_experiment() From 6aafe680c10f6cdc316188a17ecb9b81ce6ce93d Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 2 Sep 2024 20:23:45 +0800 Subject: [PATCH 015/135] =?UTF-8?q?1.=20=E6=9A=82=E6=97=B6=E5=9C=A8expo?= =?UTF-8?q?=E6=96=87=E4=BB=B6=E5=A4=B9=E9=87=8C=E5=8D=95=E7=8B=AC=E6=94=BE?= =?UTF-8?q?=E4=B8=80=E4=B8=AArequirements.txt=202.=20Dummy=20CustomExperim?= =?UTF-8?q?enter?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/README.md | 14 ++++++++++++- expo/data.yaml | 47 ++++++++++++++++++++++--------------------- expo/requirements.txt | 5 +++++ requirements.txt | 6 ------ 4 files changed, 42 insertions(+), 30 deletions(-) create mode 100644 expo/requirements.txt diff --git a/expo/README.md b/expo/README.md index 856d616b8..6e4081031 100644 --- a/expo/README.md +++ b/expo/README.md @@ -1,10 +1,17 @@ # Expo +## Setup +In the root directory, `pip install -e .` + +`cd expo` + +`pip install -r requirements.txt` + ## Instruction - 下载数据集：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink - +- 修改`data.yaml`的`datasets_dir`为数据集合集根目录存储位置 ## Examples @@ -29,6 +36,11 @@ If the dataset has reg metric, remember to use `--low_is_better`: - `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` +## Custom Experimenter + + + + ## Code and Configs Explanation diff --git a/expo/data.yaml b/expo/data.yaml index df26e29e8..050b0b893 100644 --- a/expo/data.yaml +++ b/expo/data.yaml @@ -2,34 +2,35 @@ datasets_dir: "D:/work/automl/datasets" # path to the datasets directory datasets: titanic: - dataset: "04_titanic" - user_requirement: "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. Don't plot." - metric: "accuracy" + dataset: 04_titanic + metric: f1 + user_requirement: "This is a 04_titanic dataset. Your goal is to predict the target\ + \ column `Survived`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" house_prices: - dataset: "05_house-prices-advanced-regression-techniques" - user_requirement: "This is a house price dataset, your goal is to predict the sale price of a property based on its features. Make sure to generate at least 5 tasks each time, including eda, data preprocessing, feature engineering, model training to predict the target, and model evaluation. Report RMSE between the logarithm of the predicted value and the logarithm of the observed sale prices on the eval data. The target column is 'SalePrice'. Please do not include any processing of the target column in the data preprocessing and feature engineering stages. Don't plot." - metric: "log rmse" + dataset: 05_house-prices-advanced-regression-techniques + metric: rmse + user_requirement: "This is a 05_house-prices-advanced-regression-techniques dataset.\ + \ Your goal is to predict the target column `SalePrice`.\nPerform data analysis,\ + \ data preprocessing, feature engineering, and modeling to predict the target.\ + \ \nReport rmse on the eval data. Do not plot or make any visualizations.\n" santander_customers: - dataset: "06_santander-customer-transaction-prediction" - user_requirement: "This is a customers financial dataset. Your goal is to predict which customers will make a specific transaction in the future. The target column is target. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report AUC on the eval data. Don't plot." - metric: "auc" - + dataset: 06_santander-customer-transaction-prediction + metric: f1 + user_requirement: "This is a 06_santander-customer-transaction-prediction dataset.\ + \ Your goal is to predict the target column `target`.\nPerform data analysis,\ + \ data preprocessing, feature engineering, and modeling to predict the target.\ + \ \nReport f1 on the eval data. Do not plot or make any visualizations.\n" icr: - dataset: "07_icr-identify-age-related-conditions" - user_requirement: "ICR dataset 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. Make sure to generate at least 5 tasks each time, including eda, data preprocessing, feature engineering, model training to predict the target, and model evaluation. The target column is Class. Report F1 Score on the eval data. Don't plot." - metric: "f1" - - santander_value: - dataset: "08_santander-value-prediction-challenge" - user_requirement: "This is a regression problem. Your goal is to predict the value of transactions for potential customers. The target column is target. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report RMSE on the eval data. Don't plot." - metric: "rmse" - - load_wine: - dataset: None - user_requirement: "Analyze the 'load_wine' dataset from sklearn to predict wine quality. Visualize relationships between features, use machine learning for classification, and report model accuracy. Include analysis and prediction visualizations. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Don't plot!" - metric: "accuracy" + dataset: 07_icr-identify-age-related-conditions + metric: f1 + user_requirement: "This is a 07_icr-identify-age-related-conditions dataset. Your\ + \ goal is to predict the target column `Class`.\nPerform data analysis, data\ + \ preprocessing, feature engineering, and modeling to predict the target. \n\ + Report f1 on the eval data. Do not plot or make any visualizations.\n" lick_prediction_small: dataset: Click_prediction_small diff --git a/expo/requirements.txt b/expo/requirements.txt new file mode 100644 index 000000000..04de1a8bb --- /dev/null +++ b/expo/requirements.txt @@ -0,0 +1,5 @@ +# expo +openml==0.14.2 +# ml module to run in DI +xgboost +catboost diff --git a/requirements.txt b/requirements.txt index 271fade14..8bf0ee399 100644 --- a/requirements.txt +++ b/requirements.txt @@ -79,9 +79,3 @@ gymnasium==0.29.1 boto3~=1.34.69 spark_ai_python~=0.3.30 agentops -openml==0.14.2 - -# ml module to run in DI -xgboost -catboost -lightgbm From 3ec6dcd5dfa637231621457e9ab39262030165b9 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 3 Sep 2024 13:40:23 +0800 Subject: [PATCH 016/135] update readme --- expo/MCTS.py | 2 +- expo/README.md | 99 ++++++++++++++++++++++++------- expo/data.yaml | 42 ++++++------- expo/dataset.py | 24 ++++---- expo/datasets.yaml | 27 +++++++-- expo/experimenter/custom.py | 15 ++++- expo/experimenter/experimenter.py | 28 +++++---- expo/experimenter/mcts.py | 23 +++---- 8 files changed, 180 insertions(+), 80 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index ec5ef9da0..14f2c4e4b 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -300,7 +300,7 @@ async def search(self, task, data_config, name, mcts_logger.log("MCTS", f"Tree loaded: {tree_loaded}") if not tree_loaded: - rollouts -= 2 + rollouts -= 2 # 2 rollouts for the initial tree if rollouts < 0: raise ValueError("Rollouts must be greater than 2 if there is no tree to load") self.children[root] = [] diff --git a/expo/README.md b/expo/README.md index 6e4081031..2ecf2fd2f 100644 --- a/expo/README.md +++ b/expo/README.md @@ -1,25 +1,76 @@ # Expo -## Setup -In the root directory, `pip install -e .` -`cd expo` -`pip install -r requirements.txt` - -## Instruction +## 1. Data Preparation - 下载数据集：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink - 修改`data.yaml`的`datasets_dir`为数据集合集根目录存储位置 -## Examples -### Run Base DI - -`python run_experiment.py --exp_mode base --task titanic` +## 2. Configs + +### Data Config + +`datasets.yaml` 提供数据集对应的指标和基础提示词 + +`data.yaml` 继承了`datasets.yaml`以及一些路径信息，需要将`datasets_dir`指到数据集合集的根目录下 + + +### LLM Config + +``` +llm: + api_type: 'openai' + model: deepseek-coder + base_url: "https://oneapi.deepwisdom.ai/v1" + api_key: sk-xxx + temperature: 0.5 +``` + +### Budget +实验轮次 k = 10, 20 + + +### 提示词使用 + +通过执行`dataset.py`中的`generate_task_requirement`函数获取提示词 + + +## 3. Evaluation + +运行各个框架，运行后框架需要提供Dev和Test的`dev_predictions.csv`和`test_predictions.csv`， column name为target + +两种评估方式 + +1. `evaluation.py` 提供pred和原始的gt（1D iterable）以及需要使用的metric，返回evaluation score -### Run DI RandExp +2. 使用`CustomExperimenter` +``` +experimenter = CustomExperimenter(task="titanic") +score_dict = experimenter.evaluate_pred_files(dev_pred_path, test_pred_path) +``` + +## 4. Baselines +### DS Agent +提供github链接，并说明使用的命令以及参数设置 + + +### AIDE +提供github链接，并说明使用的命令以及参数设置 + +### Autogluon +提供github链接，并说明使用的命令以及参数设置 + +### Base DI +For setup, check 5. + +- `python run_experiment.py --exp_mode base --task titanic` + + +### DI RandomSearch +For setup, check 5. - Single insight `python run_experiment.py --exp_mode aug --task titanic --aug_mode single` @@ -28,30 +79,36 @@ In the root directory, `pip install -e .` `python run_experiment.py --exp_mode aug --task titanic --aug_mode set` +## 5. DI MCTS ### Run DI MCTS -`python run_experiment.py --exp_mode mcts --task titanic --rollout 5` -If the dataset has reg metric, remember to use `--low_is_better`: +#### Setup +In the root directory, -- `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` +``` +pip install -e . -## Custom Experimenter +cd expo +pip install -r requirements.txt +``` + +#### Run + +- `python run_experiment.py --exp_mode mcts --task titanic --rollout 5` + +If the dataset has reg metric, remember to use `--low_is_better`: + +- `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` -## Code and Configs Explanation -`datasets.yaml` 提供数据集对应的指标和基础提示词 -`data.yaml` 继承了`datasets.yaml`以及一些路径信息，需要将`datasets_dir`指到数据集合集的根目录下 -完整的DI提示词参考`dataset.py`中的`generate_task_requirement`函数 -## Evaluation -`evaluation.py` 提供pred和原始的gt（1D iterable）以及需要使用的metric，返回evaluation score diff --git a/expo/data.yaml b/expo/data.yaml index 050b0b893..d62e45309 100644 --- a/expo/data.yaml +++ b/expo/data.yaml @@ -4,22 +4,23 @@ datasets: titanic: dataset: 04_titanic metric: f1 + target_col: Survived user_requirement: "This is a 04_titanic dataset. Your goal is to predict the target\ \ column `Survived`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ \ or make any visualizations.\n" - - house_prices: + house-prices: dataset: 05_house-prices-advanced-regression-techniques metric: rmse + target_col: SalePrice user_requirement: "This is a 05_house-prices-advanced-regression-techniques dataset.\ \ Your goal is to predict the target column `SalePrice`.\nPerform data analysis,\ \ data preprocessing, feature engineering, and modeling to predict the target.\ \ \nReport rmse on the eval data. Do not plot or make any visualizations.\n" - - santander_customers: + santander-customer: dataset: 06_santander-customer-transaction-prediction metric: f1 + target_col: target user_requirement: "This is a 06_santander-customer-transaction-prediction dataset.\ \ Your goal is to predict the target column `target`.\nPerform data analysis,\ \ data preprocessing, feature engineering, and modeling to predict the target.\ @@ -27,126 +28,127 @@ datasets: icr: dataset: 07_icr-identify-age-related-conditions metric: f1 + target_col: Class user_requirement: "This is a 07_icr-identify-age-related-conditions dataset. Your\ \ goal is to predict the target column `Class`.\nPerform data analysis, data\ \ preprocessing, feature engineering, and modeling to predict the target. \n\ Report f1 on the eval data. Do not plot or make any visualizations.\n" - - lick_prediction_small: + Click_prediction_small: dataset: Click_prediction_small metric: f1 + target_col: click user_requirement: "This is a Click_prediction_small dataset. Your goal is to predict\ \ the target column `click`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ \ Do not plot or make any visualizations.\n" - GesturePhaseSegmentationProcessed: dataset: GesturePhaseSegmentationProcessed metric: f1 weighted + target_col: Phase user_requirement: "This is a GesturePhaseSegmentationProcessed dataset. Your goal\ \ is to predict the target column `Phase`.\nPerform data analysis, data preprocessing,\ \ feature engineering, and modeling to predict the target. \nReport f1 weighted\ \ on the eval data. Do not plot or make any visualizations.\n" - Moneyball: dataset: Moneyball metric: rmse + target_col: RS user_requirement: "This is a Moneyball dataset. Your goal is to predict the target\ \ column `RS`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ \ plot or make any visualizations.\n" - SAT11-HAND-runtime-regression: dataset: SAT11-HAND-runtime-regression metric: rmse + target_col: runtime user_requirement: "This is a SAT11-HAND-runtime-regression dataset. Your goal\ \ is to predict the target column `runtime`.\nPerform data analysis, data preprocessing,\ \ feature engineering, and modeling to predict the target. \nReport rmse on\ \ the eval data. Do not plot or make any visualizations.\n" - boston: dataset: boston metric: rmse + target_col: MEDV user_requirement: "This is a boston dataset. Your goal is to predict the target\ \ column `MEDV`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ \ plot or make any visualizations.\n" - colleges: dataset: colleges metric: rmse + target_col: percent_pell_grant user_requirement: "This is a colleges dataset. Your goal is to predict the target\ \ column `percent_pell_grant`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport rmse on the eval\ \ data. Do not plot or make any visualizations.\n" - credit-g: dataset: credit-g metric: f1 + target_col: class user_requirement: "This is a credit-g dataset. Your goal is to predict the target\ \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ \ or make any visualizations.\n" - diamonds: dataset: diamonds metric: rmse + target_col: price user_requirement: "This is a diamonds dataset. Your goal is to predict the target\ \ column `price`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ \ plot or make any visualizations.\n" - jasmine: dataset: jasmine metric: f1 + target_col: class user_requirement: "This is a jasmine dataset. Your goal is to predict the target\ \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ \ or make any visualizations.\n" - kc1: dataset: kc1 metric: f1 + target_col: defects user_requirement: "This is a kc1 dataset. Your goal is to predict the target column\ \ `defects`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ \ or make any visualizations.\n" - kick: dataset: kick metric: f1 + target_col: IsBadBuy user_requirement: "This is a kick dataset. Your goal is to predict the target\ \ column `IsBadBuy`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ \ or make any visualizations.\n" - mfeat-factors: dataset: mfeat-factors metric: f1 weighted + target_col: class user_requirement: "This is a mfeat-factors dataset. Your goal is to predict the\ \ target column `class`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ \ eval data. Do not plot or make any visualizations.\n" - segment: dataset: segment metric: f1 weighted + target_col: class user_requirement: "This is a segment dataset. Your goal is to predict the target\ \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ \ Do not plot or make any visualizations.\n" - steel-plates-fault: dataset: steel-plates-fault metric: f1 weighted + target_col: target user_requirement: "This is a steel-plates-fault dataset. Your goal is to predict\ \ the target column `target`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ \ eval data. Do not plot or make any visualizations.\n" - wine-quality-white: dataset: wine-quality-white metric: f1 weighted + target_col: Class user_requirement: "This is a wine-quality-white dataset. Your goal is to predict\ \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ diff --git a/expo/dataset.py b/expo/dataset.py index 3e292ba7c..62665d297 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -21,13 +21,13 @@ 1. Please do not leak the target label in any form during training. 2. Dev and Test sets do not have the target column. 3. You should perform transformations on all sets at the same step. +4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. - Both files should contain a single column named `target` with the predicted values. 2. Make sure the prediction results are in the same format as the target column in the training set. - The labels should be transformed back to the original format if any transformation was applied during training. -- If the original target column was categorical or string, the predictions MUST be in the same format. ## Output Training Set Performance Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. @@ -119,7 +119,8 @@ def create_dataset_dict(dataset): dataset_dict = { "dataset": dataset.name, "user_requirement": dataset.create_base_requirement(), - "metric": dataset.get_metric() + "metric": dataset.get_metric(), + "target_col": dataset.target_col } return dataset_dict @@ -289,23 +290,24 @@ def get_dataset_info(self): # def __init__(self, name, dataset_dir, dataset_name, **kwargs): # super().__init__(name, dataset_dir, **kwargs) - +async def process_dataset(dataset, solution_designer, save_analysis_pool, datasets_dict): + if save_analysis_pool: + asyncio.run(solution_designer.generate_solutions(dataset.get_dataset_info(), dataset.name)) + dataset_dict = create_dataset_dict(dataset) + datasets_dict["datasets"][dataset.name] = dataset_dict if __name__ == "__main__": datasets_dir = "D:/work/automl/datasets" - force_update = True + force_update = False + save_analysis_pool = False datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() for dataset_id in OPENML_DATASET_IDS: openml_dataset = OpenMLExpDataset("", datasets_dir, dataset_id, force_update=force_update) - asyncio.run(solution_designer.generate_solutions(openml_dataset.get_dataset_info(), openml_dataset.name)) - dataset_dict = create_dataset_dict(openml_dataset) - datasets_dict["datasets"][openml_dataset.name] = dataset_dict + asyncio.run(process_dataset(openml_dataset, solution_designer, save_analysis_pool, datasets_dict)) for dataset_name, target_col in CUSTOM_DATASETS: custom_dataset = ExpDataset(dataset_name, datasets_dir, target_col=target_col, force_update=force_update) - asyncio.run(solution_designer.generate_solutions(custom_dataset.get_dataset_info(), custom_dataset.name)) - dataset_dict = create_dataset_dict(custom_dataset) - datasets_dict["datasets"][custom_dataset.name] = dataset_dict - + asyncio.run(process_dataset(custom_dataset, solution_designer, save_analysis_pool, datasets_dict)) + save_datasets_dict_to_yaml(datasets_dict) diff --git a/expo/datasets.yaml b/expo/datasets.yaml index ec00e3d1f..8c28b03ca 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -1,28 +1,32 @@ datasets: - 04_titanic: + titanic: dataset: 04_titanic metric: f1 + target_col: Survived user_requirement: "This is a 04_titanic dataset. Your goal is to predict the target\ \ column `Survived`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ \ or make any visualizations.\n" - 05_house-prices-advanced-regression-techniques: + house-prices: dataset: 05_house-prices-advanced-regression-techniques metric: rmse + target_col: SalePrice user_requirement: "This is a 05_house-prices-advanced-regression-techniques dataset.\ \ Your goal is to predict the target column `SalePrice`.\nPerform data analysis,\ \ data preprocessing, feature engineering, and modeling to predict the target.\ \ \nReport rmse on the eval data. Do not plot or make any visualizations.\n" - 06_santander-customer-transaction-prediction: + santander-customer: dataset: 06_santander-customer-transaction-prediction metric: f1 + target_col: target user_requirement: "This is a 06_santander-customer-transaction-prediction dataset.\ \ Your goal is to predict the target column `target`.\nPerform data analysis,\ \ data preprocessing, feature engineering, and modeling to predict the target.\ \ \nReport f1 on the eval data. Do not plot or make any visualizations.\n" - 07_icr-identify-age-related-conditions: + icr: dataset: 07_icr-identify-age-related-conditions metric: f1 + target_col: Class user_requirement: "This is a 07_icr-identify-age-related-conditions dataset. Your\ \ goal is to predict the target column `Class`.\nPerform data analysis, data\ \ preprocessing, feature engineering, and modeling to predict the target. \n\ @@ -30,6 +34,7 @@ datasets: Click_prediction_small: dataset: Click_prediction_small metric: f1 + target_col: click user_requirement: "This is a Click_prediction_small dataset. Your goal is to predict\ \ the target column `click`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ @@ -37,6 +42,7 @@ datasets: GesturePhaseSegmentationProcessed: dataset: GesturePhaseSegmentationProcessed metric: f1 weighted + target_col: Phase user_requirement: "This is a GesturePhaseSegmentationProcessed dataset. Your goal\ \ is to predict the target column `Phase`.\nPerform data analysis, data preprocessing,\ \ feature engineering, and modeling to predict the target. \nReport f1 weighted\ @@ -44,6 +50,7 @@ datasets: Moneyball: dataset: Moneyball metric: rmse + target_col: RS user_requirement: "This is a Moneyball dataset. Your goal is to predict the target\ \ column `RS`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ @@ -51,6 +58,7 @@ datasets: SAT11-HAND-runtime-regression: dataset: SAT11-HAND-runtime-regression metric: rmse + target_col: runtime user_requirement: "This is a SAT11-HAND-runtime-regression dataset. Your goal\ \ is to predict the target column `runtime`.\nPerform data analysis, data preprocessing,\ \ feature engineering, and modeling to predict the target. \nReport rmse on\ @@ -58,6 +66,7 @@ datasets: boston: dataset: boston metric: rmse + target_col: MEDV user_requirement: "This is a boston dataset. Your goal is to predict the target\ \ column `MEDV`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ @@ -65,6 +74,7 @@ datasets: colleges: dataset: colleges metric: rmse + target_col: percent_pell_grant user_requirement: "This is a colleges dataset. Your goal is to predict the target\ \ column `percent_pell_grant`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport rmse on the eval\ @@ -72,6 +82,7 @@ datasets: credit-g: dataset: credit-g metric: f1 + target_col: class user_requirement: "This is a credit-g dataset. Your goal is to predict the target\ \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ @@ -79,6 +90,7 @@ datasets: diamonds: dataset: diamonds metric: rmse + target_col: price user_requirement: "This is a diamonds dataset. Your goal is to predict the target\ \ column `price`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ @@ -86,6 +98,7 @@ datasets: jasmine: dataset: jasmine metric: f1 + target_col: class user_requirement: "This is a jasmine dataset. Your goal is to predict the target\ \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ @@ -93,6 +106,7 @@ datasets: kc1: dataset: kc1 metric: f1 + target_col: defects user_requirement: "This is a kc1 dataset. Your goal is to predict the target column\ \ `defects`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ @@ -100,6 +114,7 @@ datasets: kick: dataset: kick metric: f1 + target_col: IsBadBuy user_requirement: "This is a kick dataset. Your goal is to predict the target\ \ column `IsBadBuy`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ @@ -107,6 +122,7 @@ datasets: mfeat-factors: dataset: mfeat-factors metric: f1 weighted + target_col: class user_requirement: "This is a mfeat-factors dataset. Your goal is to predict the\ \ target column `class`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ @@ -114,6 +130,7 @@ datasets: segment: dataset: segment metric: f1 weighted + target_col: class user_requirement: "This is a segment dataset. Your goal is to predict the target\ \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ @@ -121,6 +138,7 @@ datasets: steel-plates-fault: dataset: steel-plates-fault metric: f1 weighted + target_col: target user_requirement: "This is a steel-plates-fault dataset. Your goal is to predict\ \ the target column `target`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ @@ -128,6 +146,7 @@ datasets: wine-quality-white: dataset: wine-quality-white metric: f1 weighted + target_col: Class user_requirement: "This is a wine-quality-white dataset. Your goal is to predict\ \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py index 06df4efcf..ff5ba3546 100644 --- a/expo/experimenter/custom.py +++ b/expo/experimenter/custom.py @@ -9,10 +9,12 @@ class CustomExperimenter(Experimenter): def __init__(self, args, **kwargs): super().__init__(args, **kwargs) - self.framework = kwargs["framework"] + self.framework = kwargs["framework"] # todo + self.task = kwargs.get("task", self.args.task) + self.low_is_better = kwargs.get("low_is_better", self.args.low_is_better) self.name = kwargs.get("name", "") self.result_path = f"results/custom_{self.name}" - self.state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") + self.state = create_initial_state(self.task, start_task_id=1, data_config=self.data_config, low_is_better=self.low_is_better, name=self.name) async def run_experiment(self): user_requirement = self.state["requirement"] @@ -30,6 +32,15 @@ async def run_experiment(self): } self.save_result(results) + def evaluate_pred_files(self, dev_pred_path, test_pred_path): + dev_preds = pd.read_csv(dev_pred_path)["target"] + test_preds = pd.read_csv(test_pred_path)["target"] + score_dict = { + "dev_score": self.evaluate_score(dev_preds, "dev"), + "test_score": self.evaluate_score(test_preds, "test") + } + return score_dict + def evaluate_predictions(self, preds, split): metric = self.state["dataset_config"]["metric"] gt_path = os.path.join(self.state["datasets_dir"][f"{split}_target"]) diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 4473866af..678d48d6a 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -20,16 +20,24 @@ def __init__(self, args, **kwargs): async def run_experiment(self): state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") user_requirement = state["requirement"] - di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) - await di.run(user_requirement) - - score_dict = await di.get_score() - score_dict = self.evaluate(score_dict, state) - results = { - "score_dict": score_dict, - "user_requirement": user_requirement, - "args": vars(self.args) - } + results = [] + + for i in range(self.args.num_experiments): + di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) + await di.run(user_requirement) + score_dict = await di.get_score() + score_dict = self.evaluate(score_dict, state) + results.append({ + "idx": i, + "score_dict": score_dict, + "user_requirement": user_requirement, + "args": vars(self.args) + }) + scores = [result["score_dict"]["test_score"] for result in results] + avg_score = sum(scores) / len(scores) + best_score = max(scores) if not self.args.low_is_better else min(scores) + best_score_idx = scores.index(best_score) + results.insert(0, {"avg_score": avg_score, "best_score": best_score, "best_score_idx": best_score_idx}) self.save_result(results) def evaluate_prediction(self, split, state): diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 43c5f9868..0159abe24 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -22,18 +22,19 @@ async def run_experiment(self): text += f"Best node: {best_node}, score: {best_node.raw_reward}\n" text += f"Dev best node: {dev_best_node}, score: {dev_best_node.raw_reward}\n" print(text) - self.save_tree(text) + if self.args.rollouts > 0: + self.save_tree(text) - results = { - "best_node": best_node.id, - "best_node_score": best_node.raw_reward, - "dev_best_node": dev_best_node.id, - "dev_best_node_score": dev_best_node.raw_reward, - "num_generated_codes": num_generated_codes, - "user_requirement": best_node.state["requirement"], - "args": vars(self.args) - } - self.save_result(results) + results = { + "best_node": best_node.id, + "best_node_score": best_node.raw_reward, + "dev_best_node": dev_best_node.id, + "dev_best_node_score": dev_best_node.raw_reward, + "num_generated_codes": num_generated_codes, + "user_requirement": best_node.state["requirement"], + "args": vars(self.args) + } + self.save_result(results) From df877c973e64b1a2136292c575ea25f6161d2608 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 3 Sep 2024 14:03:07 +0800 Subject: [PATCH 017/135] update readme --- expo/README.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/expo/README.md b/expo/README.md index 2ecf2fd2f..99aa35016 100644 --- a/expo/README.md +++ b/expo/README.md @@ -42,11 +42,7 @@ llm: 运行各个框架，运行后框架需要提供Dev和Test的`dev_predictions.csv`和`test_predictions.csv`， column name为target -两种评估方式 - -1. `evaluation.py` 提供pred和原始的gt（1D iterable）以及需要使用的metric，返回evaluation score - -2. 使用`CustomExperimenter` +- 使用`CustomExperimenter` ``` experimenter = CustomExperimenter(task="titanic") score_dict = experimenter.evaluate_pred_files(dev_pred_path, test_pred_path) From 6972afb755db818ff7ea85afeb86d3b8859c8802 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 3 Sep 2024 16:31:03 +0800 Subject: [PATCH 018/135] update prompt (specify whether each set has target label) --- expo/MCTS.py | 4 ++-- expo/dataset.py | 8 ++++---- expo/evaluation/evaluation.py | 2 +- expo/evaluation/visualize_mcts.py | 6 ++++-- expo/experimenter/mcts.py | 23 +++++++++++------------ 5 files changed, 22 insertions(+), 21 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 14f2c4e4b..dd4ad50b1 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -240,7 +240,7 @@ def uct(node: Node): all_children = [child for children in self.children.values() for child in children] return max(all_children, key=uct) - async def expand(self, node : Node, max_children=4): + async def expand(self, node : Node, max_children=5): await node.expand(max_children) if node not in self.children or not self.children[node]: self.children[node] = node.children @@ -273,7 +273,7 @@ def bfs(node : Node, best_score, best_child : Node, split): return best_score, best_child for child in self.children[node]: score = child.normalized_reward[split] - print(child.id, score) + print(child.id, split, score) if score > best_score: best_score = score best_child = child diff --git a/expo/dataset.py b/expo/dataset.py index 62665d297..a43f1292a 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -20,7 +20,7 @@ **Attention** 1. Please do not leak the target label in any form during training. 2. Dev and Test sets do not have the target column. -3. You should perform transformations on all sets at the same step. +3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). 4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. ## Saving Dev and Test Predictions @@ -38,9 +38,9 @@ ``` # Data dir -training: {train_path} -dev: {dev_path} -testing: {test_path} +training (with labels): {train_path} +dev (without labels): {dev_path} +testing (without labels): {test_path} # Output dir {output_dir} diff --git a/expo/evaluation/evaluation.py b/expo/evaluation/evaluation.py index 20a35aa27..886bc036d 100644 --- a/expo/evaluation/evaluation.py +++ b/expo/evaluation/evaluation.py @@ -5,7 +5,7 @@ def evaluate_score(pred, gt, metric): if metric == "accuracy": return accuracy_score(gt, pred) elif metric == "f1": - unique_classes = np.unique(gt) + unique_classes = sorted(list(np.unique(gt))) if 1 in unique_classes and 0 in unique_classes: pos_label = 1 else: diff --git a/expo/evaluation/visualize_mcts.py b/expo/evaluation/visualize_mcts.py index 6e38576e2..4199def0e 100644 --- a/expo/evaluation/visualize_mcts.py +++ b/expo/evaluation/visualize_mcts.py @@ -48,7 +48,9 @@ def visualize_tree(node, depth=0, previous_plans=None): for child in node.children: text += textwrap.indent(visualize_tree(child, depth+1, previous_plans), "\t") return text - - return visualize_tree(node), len(code_set) + num_simulations = node.visited + text = f"Number of simulations: {num_simulations}\n" + text += visualize_tree(node) + return text, len(code_set) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 0159abe24..43c5f9868 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -22,19 +22,18 @@ async def run_experiment(self): text += f"Best node: {best_node}, score: {best_node.raw_reward}\n" text += f"Dev best node: {dev_best_node}, score: {dev_best_node.raw_reward}\n" print(text) - if self.args.rollouts > 0: - self.save_tree(text) + self.save_tree(text) - results = { - "best_node": best_node.id, - "best_node_score": best_node.raw_reward, - "dev_best_node": dev_best_node.id, - "dev_best_node_score": dev_best_node.raw_reward, - "num_generated_codes": num_generated_codes, - "user_requirement": best_node.state["requirement"], - "args": vars(self.args) - } - self.save_result(results) + results = { + "best_node": best_node.id, + "best_node_score": best_node.raw_reward, + "dev_best_node": dev_best_node.id, + "dev_best_node_score": dev_best_node.raw_reward, + "num_generated_codes": num_generated_codes, + "user_requirement": best_node.state["requirement"], + "args": vars(self.args) + } + self.save_result(results) From f23d2a72c9d8c52ca9f2b6e2cf68dce5f64a623c Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 4 Sep 2024 10:39:28 +0800 Subject: [PATCH 019/135] add autogluon --- expo/experimenter/autogluon.py | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 expo/experimenter/autogluon.py diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py new file mode 100644 index 000000000..d59fb3e83 --- /dev/null +++ b/expo/experimenter/autogluon.py @@ -0,0 +1,32 @@ +from expo.experimenter.custom import CustomExperimenter +from autogluon.tabular import TabularDataset, TabularPredictor + +class AGRunner(): + preset = "best_quality" + time_limit = 500 + + def __init__(self, datasets): + self.datasets = datasets + + def run(self): + train_path = self.datasets["train"] + test_wo_target_path = self.datasets["test_wo_target"] + dev_wo_target_path = self.datasets["dev_wo_target"] + target_col = self.state["dataset_config"]["target_col"] + train_data = TabularDataset(train_path) + test_data = TabularDataset(test_wo_target_path) + dev_data = TabularDataset(dev_wo_target_path) + + predictor = TabularPredictor(label=target_col).fit(train_data, presets=self.preset, time_limit=self.time_limit) + test_preds = predictor.predict(test_data) + dev_preds = predictor.predict(dev_data) + return {"test_preds": test_preds, "dev_preds": dev_preds} + +class GluonExperimenter(CustomExperimenter): + result_path : str = "results/autogluon" + + def __init__(self, args, **kwargs): + super().__init__(args, **kwargs) + self.framework = AGRunner(self.datasets) + + From 72bd1665b16c08d1c8215264f411d09509af879c Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 4 Sep 2024 10:41:10 +0800 Subject: [PATCH 020/135] ensure experimenter not evaluating csv from other experiments --- expo/MCTS.py | 2 ++ expo/experimenter/custom.py | 4 ++-- expo/experimenter/experimenter.py | 30 +++++++++++++++++++++++++----- expo/experimenter/mcts.py | 5 +++-- 4 files changed, 32 insertions(+), 9 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index dd4ad50b1..9787ea5e9 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -177,6 +177,8 @@ def evaluate_prediction(self, split): preds.to_csv(pred_node_path, index=False) gt = pd.read_csv(gt_path)["target"] metric = self.state["dataset_config"]["metric"] + # remove original predictions.csv + os.remove(pred_path) return evaluate_score(preds, gt, metric) def evaluate_simulation(self, score_dict): diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py index ff5ba3546..c3cb97b9c 100644 --- a/expo/experimenter/custom.py +++ b/expo/experimenter/custom.py @@ -16,9 +16,9 @@ def __init__(self, args, **kwargs): self.result_path = f"results/custom_{self.name}" self.state = create_initial_state(self.task, start_task_id=1, data_config=self.data_config, low_is_better=self.low_is_better, name=self.name) - async def run_experiment(self): + def run_experiment(self): user_requirement = self.state["requirement"] - preds = await self.framework.run(user_requirement) + preds = self.framework.run(user_requirement) test_preds = preds["test_preds"] dev_preds = preds["dev_preds"] score_dict = { diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 678d48d6a..949ab97f1 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -33,11 +33,22 @@ async def run_experiment(self): "user_requirement": user_requirement, "args": vars(self.args) }) - scores = [result["score_dict"]["test_score"] for result in results] - avg_score = sum(scores) / len(scores) - best_score = max(scores) if not self.args.low_is_better else min(scores) - best_score_idx = scores.index(best_score) - results.insert(0, {"avg_score": avg_score, "best_score": best_score, "best_score_idx": best_score_idx}) + self.save_result(results) # save intermediate results + dev_scores = [result["score_dict"]["dev_score"] for result in results] + best_dev_score = max(dev_scores) if not self.args.low_is_better else min(dev_scores) + best_score_idx = dev_scores.index(best_dev_score) + + test_scores = [result["score_dict"]["test_score"] for result in results] + avg_score = sum(test_scores) / len(test_scores) + global_best_score = max(test_scores) if not self.args.low_is_better else min(test_scores) + + results.insert(0, { + "best_dev_score": best_dev_score, + "best_score_idx": best_score_idx, + "best_test_score": test_scores[best_score_idx], + "avg_test_score": avg_score, + "best_score": global_best_score + }) self.save_result(results) def evaluate_prediction(self, split, state): @@ -49,6 +60,7 @@ def evaluate_prediction(self, split, state): preds.to_csv(pred_node_path, index=False) gt = pd.read_csv(gt_path)["target"] metric = state["dataset_config"]["metric"] + os.remove(pred_path) return evaluate_score(preds, gt, metric) def evaluate(self, score_dict, state): @@ -61,6 +73,14 @@ def evaluate(self, score_dict, state): def save_result(self, result): + end_time = datetime.datetime.now().strftime("%Y%m%d%H%M") + time_info = { + "start_time": self.start_time, + "end_time": end_time, + "duration (seconds)": float(end_time) - float(self.start_time) + } + result = result.copy() + result.insert(0, time_info) os.makedirs(self.result_path, exist_ok=True) with open(f"{self.result_path}/{self.args.exp_mode}-{self.args.task}_{self.start_time}.json", "w") as f: json.dump(result, f, indent=4) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 43c5f9868..e41f94d58 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -24,15 +24,16 @@ async def run_experiment(self): print(text) self.save_tree(text) - results = { + results = [{ "best_node": best_node.id, "best_node_score": best_node.raw_reward, "dev_best_node": dev_best_node.id, "dev_best_node_score": dev_best_node.raw_reward, "num_generated_codes": num_generated_codes, "user_requirement": best_node.state["requirement"], + "tree_text": text, "args": vars(self.args) - } + }] self.save_result(results) From aea524b4eafbc94e0529dfe8d00c03a8d9d7068d Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 4 Sep 2024 14:49:18 +0800 Subject: [PATCH 021/135] 1. update readme 2. fix duration --- expo/README.md | 12 ++++++++++++ expo/experimenter/experimenter.py | 2 +- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/expo/README.md b/expo/README.md index 99aa35016..dfaf1ab0a 100644 --- a/expo/README.md +++ b/expo/README.md @@ -57,6 +57,18 @@ score_dict = experimenter.evaluate_pred_files(dev_pred_path, test_pred_path) 提供github链接，并说明使用的命令以及参数设置 ### Autogluon +#### Setup +``` +pip install -U pip +pip install -U setuptools wheel + +CPU version of pytorch has smaller footprint - see installation instructions in +pytorch documentation - https://pytorch.org/get-started/locally/ +pip install torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cpu + +pip install autogluon +``` + 提供github链接，并说明使用的命令以及参数设置 ### Base DI diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 949ab97f1..e53bae972 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -77,7 +77,7 @@ def save_result(self, result): time_info = { "start_time": self.start_time, "end_time": end_time, - "duration (seconds)": float(end_time) - float(self.start_time) + "duration (minutes)": float(end_time) - float(self.start_time) } result = result.copy() result.insert(0, time_info) From fcd1ba66a6ae70f93e7e575f5a9395ebfea5d6ff Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 4 Sep 2024 16:38:33 +0800 Subject: [PATCH 022/135] =?UTF-8?q?=E5=A2=9E=E5=8A=A0try=20catch?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/MCTS.py | 26 +++++++++++++++++++------- expo/README.md | 5 +++-- expo/experimenter/aug.py | 8 +++----- expo/experimenter/experimenter.py | 31 +++++++++++++++++++++++++++---- 4 files changed, 52 insertions(+), 18 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 9787ea5e9..ab9957a7a 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -194,13 +194,25 @@ async def run_node(self, role=None): if self.is_terminal() and role is not None: if role.state_saved: return self.raw_reward - - if not role: - role = self.load_role() - await load_execute_notebook(role) # execute previous notebook's code - await role.run(with_message='continue') - else: - await role.run(with_message=self.state['requirement']) + + max_retries = 3 + num_runs = 1 + run_finished = False + while num_runs <= max_retries and not run_finished: + try: + if not role: + role = self.load_role() + await load_execute_notebook(role) # execute previous notebook's code + await role.run(with_message='continue') + else: + await role.run(with_message=self.state['requirement']) + run_finished = True + except Exception as e: + mcts_logger.log("MCTS", f"Error in running the role: {e}") + num_runs += 1 + if not run_finished: + mcts_logger.log("MCTS", f"Role {role.node_id} failed to run") + return {"test_score": 0, "dev_score": 0, "score": 0} score_dict = await role.get_score() score_dict = self.evaluate_simulation(score_dict) self.raw_reward = score_dict diff --git a/expo/README.md b/expo/README.md index dfaf1ab0a..4cc4daf25 100644 --- a/expo/README.md +++ b/expo/README.md @@ -35,7 +35,8 @@ llm: ### 提示词使用 -通过执行`dataset.py`中的`generate_task_requirement`函数获取提示词 +- 通过执行`dataset.py`中的`generate_task_requirement`函数获取提示词 +- 每一个数据集里有`dataset_info.json`，里面的内容需要提供给baselines以保证公平 ## 3. Evaluation @@ -74,7 +75,7 @@ pip install autogluon ### Base DI For setup, check 5. -- `python run_experiment.py --exp_mode base --task titanic` +- `python run_experiment.py --exp_mode base --task titanic --num_experiments 10` ### DI RandomSearch diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 956849717..86c98fd42 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -18,8 +18,8 @@ class AugExperimenter(Experimenter): result_path : str = "results/aug" async def run_experiment(self): - state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") - user_requirement = state["requirement"] + # state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") + user_requirement = self.state["requirement"] exp_pool_path = get_exp_pool_path(self.args.task, self.data_config, pool_name="ds_analysis_pool") exp_pool = InstructionGenerator.load_analysis_pool(exp_pool_path) if self.args.aug_mode == "single": @@ -38,9 +38,7 @@ async def run_experiment(self): di.role_dir = f"{di.role_dir}_{self.args.task}" requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) print(requirement) - await di.run(requirement) - score_dict = await di.get_score() - score_dict = self.evaluate(score_dict, state) + score_dict = await self.run_di(di, requirement) results.append({ "idx": i, "score_dict": score_dict, diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index e53bae972..709eefdfc 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -16,17 +16,40 @@ class Experimenter: def __init__(self, args, **kwargs): self.args = args self.start_time = datetime.datetime.now().strftime("%Y%m%d%H%M") + self.state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") + + + async def run_di(self, di, user_requirement): + max_retries = 3 + num_runs = 1 + run_finished = False + while num_runs <= max_retries and not run_finished: + try: + await di.run(user_requirement) + score_dict = await di.get_score() + score_dict = self.evaluate(score_dict, self.state) + run_finished = True + except Exception as e: + print(f"Error: {e}") + num_runs += 1 + if not run_finished: + score_dict = { + "train_score": -1, + "dev_score": -1, + "test_score": -1, + "score": -1 + } + return score_dict + async def run_experiment(self): - state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") + state = self.state user_requirement = state["requirement"] results = [] for i in range(self.args.num_experiments): di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) - await di.run(user_requirement) - score_dict = await di.get_score() - score_dict = self.evaluate(score_dict, state) + score_dict = await self.run_di(di, user_requirement) results.append({ "idx": i, "score_dict": score_dict, From ab8a1d682470f30c3abb2afb96d524e7fff8ab81 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 4 Sep 2024 17:52:02 +0800 Subject: [PATCH 023/135] format code --- expo/MCTS.py | 179 +++++++++++++------------ expo/dataset.py | 125 +++++++++-------- expo/evaluation/evaluation.py | 5 +- expo/evaluation/visualize_mcts.py | 21 +-- expo/experimenter/__init__.py | 4 - expo/experimenter/aug.py | 32 ++--- expo/experimenter/autogluon.py | 15 ++- expo/experimenter/custom.py | 35 +++-- expo/experimenter/experimenter.py | 63 +++++---- expo/experimenter/mcts.py | 48 +++---- expo/insights/instruction_generator.py | 35 +++-- expo/insights/solution_designer.py | 49 +++---- expo/research_assistant.py | 63 ++++----- expo/run_exp_augmentation.py | 50 ++++--- expo/run_experiment.py | 16 ++- expo/run_mcts.py | 25 ++-- expo/utils.py | 48 ++++--- 17 files changed, 425 insertions(+), 388 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index ab9957a7a..7c03e2e86 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -1,23 +1,28 @@ -import random import math import os +import pickle +import random + import pandas as pd -from expo.research_assistant import ResearchAssistant -from expo.insights.instruction_generator import InstructionGenerator -from expo.dataset import get_split_dataset_path, generate_task_requirement + +from expo.dataset import generate_task_requirement, get_split_dataset_path from expo.evaluation.evaluation import evaluate_score -from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path +from expo.insights.instruction_generator import InstructionGenerator +from expo.research_assistant import ResearchAssistant +from expo.utils import get_exp_pool_path, load_execute_notebook, mcts_logger +from metagpt.tools.tool_recommend import ToolRecommender +from metagpt.utils.common import read_json_file -from metagpt.tools.tool_recommend import BM25ToolRecommender, ToolRecommender -from metagpt.utils.common import write_json_file, read_json_file, format_trackback_info -import numpy as np -import pickle def initialize_di_root_node(task, data_config, low_is_better=False, reflection=True, name=""): start_task_id = 2 - state = create_initial_state(task, start_task_id=start_task_id, data_config=data_config, low_is_better=low_is_better, name=name) - role = ResearchAssistant(node_id="0", start_task_id=start_task_id, use_reflection=reflection, role_dir=state["node_dir"]) - return role, Node(parent=None, state=state, action=None, value=0) + state = create_initial_state( + task, start_task_id=start_task_id, data_config=data_config, low_is_better=low_is_better, name=name + ) + role = ResearchAssistant( + node_id="0", start_task_id=start_task_id, use_reflection=reflection, role_dir=state["node_dir"] + ) + return role, Node(parent=None, state=state, action=None, value=0) def create_initial_state(task, start_task_id, data_config, low_is_better, name): @@ -36,16 +41,16 @@ def create_initial_state(task, start_task_id, data_config, low_is_better, name): return initial_state -class Node(): - state : dict = {} - action : str = None - value : float = 0 - visited : int = 0 - children : list = [] - normalized_reward : dict = {"train_score": 0, "dev_score": 0, "test_score": 0} +class Node: + state: dict = {} + action: str = None + value: float = 0 + visited: int = 0 + children: list = [] + normalized_reward: dict = {"train_score": 0, "dev_score": 0, "test_score": 0} parent = None - def __init__(self, parent=None, state = None, action=None, value = 0, max_depth=4, **kwargs): + def __init__(self, parent=None, state=None, action=None, value=0, max_depth=4, **kwargs): self.state = state self.action = action self.value = value @@ -66,14 +71,14 @@ def avg_value(self): def __hash__(self): return hash(self.id) - + def save_node(self): - os.makedirs(self.state["node_dir"], exist_ok=True) - with open(os.path.join(self.state["node_dir"], f"Node-{self.id}.pkl"), 'wb') as f: + os.makedirs(self.state["node_dir"], exist_ok=True) + with open(os.path.join(self.state["node_dir"], f"Node-{self.id}.pkl"), "wb") as f: pickle.dump(self, f) - + def load_node(self): - with open(os.path.join(self.state["node_dir"], f"Node-{self.id}.pkl"), 'rb') as f: + with open(os.path.join(self.state["node_dir"], f"Node-{self.id}.pkl"), "rb") as f: return pickle.load(f) def get_depth(self): @@ -94,14 +99,14 @@ def generate_id(self): def is_terminal(self): return int(self.state["start_task_id"]) == self.max_depth + 1 - + def is_fully_expanded(self): return len(self.children) > 0 - + def add_child(self, child_node): self.children.append(child_node) - def update(self, reward:dict, child_node=None): + def update(self, reward: dict, child_node=None): if child_node is not None: child_role = child_node.load_role() role = self.load_role() @@ -117,46 +122,48 @@ def get_role_path(self): fname = f"Node-{self.id}.json" role_path = os.path.join(self.state["node_dir"], fname) return role_path - + def load_role(self): role_dict = read_json_file(self.get_role_path()) - if role_dict.get('tool_recommender') is None: - role_dict['tool_recommender'] = ToolRecommender() - elif isinstance(role_dict.get('tool_recommender', {}).get('tools'), dict): - role_dict['tool_recommender']['tools'] = list(role_dict['tool_recommender']['tools'].keys()) + if role_dict.get("tool_recommender") is None: + role_dict["tool_recommender"] = ToolRecommender() + elif isinstance(role_dict.get("tool_recommender", {}).get("tools"), dict): + role_dict["tool_recommender"]["tools"] = list(role_dict["tool_recommender"]["tools"].keys()) role = ResearchAssistant(**role_dict) - if self.parent is not None: # TODO: Check this + if self.parent is not None: # TODO: Check this parent_role = self.parent.load_role() role.update_til_start_task(parent_role, backward=False) role.remap_tasks() return role - + def save_new_role(self, role: ResearchAssistant): role.node_id = self.id - role.start_task_id = self.state['start_task_id'] + role.start_task_id = self.state["start_task_id"] role.state_saved = False - role.change_next_instruction(self.action) + role.change_next_instruction(self.action) mcts_logger.log("MCTS", f"Saving new role: {role.node_id}") role.save_state(static_save=True) - + async def expand(self, max_children): if self.is_fully_expanded(): return insight_geneartor = InstructionGenerator() role = self.load_role() original_instruction = role.get_next_instruction() - insights = await insight_geneartor.generate_new_instructions(task_id=role.start_task_id + 1, - original_instruction=original_instruction, - max_num=max_children, - file_path=self.state["exp_pool_path"]) + insights = await insight_geneartor.generate_new_instructions( + task_id=role.start_task_id + 1, + original_instruction=original_instruction, + max_num=max_children, + file_path=self.state["exp_pool_path"], + ) new_state = self.state.copy() - new_state['start_task_id'] += 1 + new_state["start_task_id"] += 1 for insight in insights: new_role = role.model_copy() node = Node(parent=self, state=new_state, action=insight, value=0) node.save_new_role(new_role) self.add_child(node) - + # def evaluate_test(self): # prediction_fpath = os.path.join(self.state["work_dir"], self.state["task"], "predictions.csv") # predictions = pd.read_csv(prediction_fpath)["target"] @@ -168,7 +175,7 @@ async def expand(self, max_children): # gt = pd.read_csv(os.path.join(split_datasets_dir["test_target"]))["target"] # metric = self.state["dataset_config"]["metric"] # return evaluate_score(predictions, gt, metric) - + def evaluate_prediction(self, split): pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}_predictions.csv") pred_node_path = os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}_predictions.csv") @@ -180,21 +187,17 @@ def evaluate_prediction(self, split): # remove original predictions.csv os.remove(pred_path) return evaluate_score(preds, gt, metric) - + def evaluate_simulation(self, score_dict): - scores = { - "dev_score": self.evaluate_prediction("dev"), - "test_score": self.evaluate_prediction("test") - } + scores = {"dev_score": self.evaluate_prediction("dev"), "test_score": self.evaluate_prediction("test")} score_dict.update(scores) return score_dict - - + async def run_node(self, role=None): if self.is_terminal() and role is not None: if role.state_saved: return self.raw_reward - + max_retries = 3 num_runs = 1 run_finished = False @@ -202,10 +205,10 @@ async def run_node(self, role=None): try: if not role: role = self.load_role() - await load_execute_notebook(role) # execute previous notebook's code - await role.run(with_message='continue') + await load_execute_notebook(role) # execute previous notebook's code + await role.run(with_message="continue") else: - await role.run(with_message=self.state['requirement']) + await role.run(with_message=self.state["requirement"]) run_finished = True except Exception as e: mcts_logger.log("MCTS", f"Error in running the role: {e}") @@ -222,18 +225,19 @@ def normalize_score(score): if score == -1: return 0 return 1 / (1 + score) + score_dict = {k: normalize_score(v) for k, v in score_dict.items()} self.normalized_reward = score_dict return score_dict - -class MCTS(): - #data_path - root_node : Node = None - children : dict = {} - max_depth : int = 5 - c_explore : float = 1.4 - c_unvisited : float = 0.8 + +class MCTS: + # data_path + root_node: Node = None + children: dict = {} + max_depth: int = 5 + c_explore: float = 1.4 + c_unvisited: float = 0.8 def __init__(self, root_node, max_depth): self.root_node = root_node @@ -243,34 +247,34 @@ def select(self, node: Node): node = self.best_child() mcts_logger.log("MCTS", f"Selected node id: {node.id}") return node - + def best_child(self): def uct(node: Node): n_visits = node.visited if node.visited else self.c_unvisited - avg_value = node.avg_value() if node.visited else node.value/self.c_unvisited + avg_value = node.avg_value() if node.visited else node.value / self.c_unvisited return avg_value + self.c_explore * math.sqrt(math.log(node.parent.visited) / n_visits) + if len(self.children) == 0: return self.root_node all_children = [child for children in self.children.values() for child in children] return max(all_children, key=uct) - async def expand(self, node : Node, max_children=5): + async def expand(self, node: Node, max_children=5): await node.expand(max_children) if node not in self.children or not self.children[node]: self.children[node] = node.children return node.children - - async def simulate(self, node : Node, role=None): + + async def simulate(self, node: Node, role=None): "Returns the reward for a random simulation (to completion) of `node`" mcts_logger.log("MCTS", f"Start simulating node {node.id}:") while node.children: node = random.choice(node.children) reward = await node.run_node(role) - mcts_logger.log("MCTS", f"Simulated node's reward: {reward}") + mcts_logger.log("MCTS", f"Simulated node's reward: {reward}") return reward - - def backpropagate(self, node : Node, reward): + def backpropagate(self, node: Node, reward): child_node = node node.update(reward) node = node.parent @@ -278,10 +282,11 @@ def backpropagate(self, node : Node, reward): node.update(reward, child_node) node, child_node = node.parent, node - def best_path(self, root : Node): + def best_path(self, root: Node): best_child = root best_score = 0 - def bfs(node : Node, best_score, best_child : Node, split): + + def bfs(node: Node, best_score, best_child: Node, split): assert split in ["test_score", "dev_score"] if node not in self.children: return best_score, best_child @@ -293,19 +298,19 @@ def bfs(node : Node, best_score, best_child : Node, split): best_child = child best_score, best_child = bfs(child, best_score, best_child, split) return best_score, best_child + _, best_child = bfs(root, best_score, best_child, "test_score") _, dev_best_child = bfs(root, best_score, best_child, "dev_score") - return {"dev_best": dev_best_child, - "global_best": best_child} - + return {"dev_best": dev_best_child, "global_best": best_child} + def get_num_simulations(self): return self.root_node.visited - async def search(self, task, data_config, name, - rollouts, load_tree=False, low_is_better=False, reflection=False): - - role, root = initialize_di_root_node(task, data_config, low_is_better=low_is_better, reflection=reflection, name=name) + async def search(self, task, data_config, name, rollouts, load_tree=False, low_is_better=False, reflection=False): + role, root = initialize_di_root_node( + task, data_config, low_is_better=low_is_better, reflection=reflection, name=name + ) self.root_node = root tree_loaded = False if load_tree: @@ -314,14 +319,14 @@ async def search(self, task, data_config, name, mcts_logger.log("MCTS", f"Tree loaded: {tree_loaded}") if not tree_loaded: - rollouts -= 2 # 2 rollouts for the initial tree + rollouts -= 2 # 2 rollouts for the initial tree if rollouts < 0: raise ValueError("Rollouts must be greater than 2 if there is no tree to load") self.children[root] = [] reward = await self.simulate(root, role) self.backpropagate(root, reward) children = await self.expand(root) - #目前是随机选择1个，后续可以改成多个 + # 目前是随机选择1个，后续可以改成多个 first_leaf = random.choice(children) reward = await self.simulate(first_leaf) self.backpropagate(first_leaf, reward) @@ -339,14 +344,13 @@ async def search(self, task, data_config, name, mcts_logger.log("MCTS", f"Terminal node's reward: {reward}") self.backpropagate(node, reward) else: - if node.visited > 0: + if node.visited > 0: children = await self.expand(node) node = random.choice(children) reward = await self.simulate(node) self.backpropagate(node, reward) return self.best_path(root) - def load_tree(self): def load_children_node(node): mcts_logger.log("MCTS", f"Load node {node.id}'s child: {node.children}") @@ -356,14 +360,15 @@ def load_children_node(node): child.load_node() self.children[child] = child.children load_children_node(child) + # Load all pkl files in the node_dir all_pkl_files = os.listdir(self.root_node.state["node_dir"]) all_pkl_files = [f for f in all_pkl_files if f.endswith(".pkl")] if os.path.exists(os.path.join(self.root_node.state["node_dir"], "Node-0.pkl")): - with open(os.path.join(self.root_node.state["node_dir"], "Node-0.pkl"), 'rb') as f: + with open(os.path.join(self.root_node.state["node_dir"], "Node-0.pkl"), "rb") as f: self.root_node = pickle.load(f) self.children[self.root_node] = self.root_node.children load_children_node(self.root_node) if self.children: return True - return False \ No newline at end of file + return False diff --git a/expo/dataset.py b/expo/dataset.py index a43f1292a..fee1199a9 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -1,12 +1,14 @@ -import openml -from pathlib import Path -from sklearn.model_selection import train_test_split -import os +import asyncio import json -import yaml +import os +from pathlib import Path + +import openml import pandas as pd +import yaml +from sklearn.model_selection import train_test_split + from expo.insights.solution_designer import SolutionDesigner -import asyncio BASE_USER_REQUIREMENT = """\ This is a {datasetname} dataset. Your goal is to predict the target column `{target_col}`. @@ -59,14 +61,12 @@ 41980, 42225, 531, - # cls 41143, 31, 42733, 41162, 1067, - # multi cls 40498, 40982, @@ -79,14 +79,15 @@ ("04_titanic", "Survived"), ("05_house-prices-advanced-regression-techniques", "SalePrice"), ("06_santander-customer-transaction-prediction", "target"), - ("07_icr-identify-age-related-conditions", "Class") + ("07_icr-identify-age-related-conditions", "Class"), ] + def get_split_dataset_path(dataset_name, config): - datasets_dir = config['datasets_dir'] - if dataset_name in config['datasets']: - dataset = config['datasets'][dataset_name] - data_path = os.path.join(datasets_dir, dataset['dataset']) + datasets_dir = config["datasets_dir"] + if dataset_name in config["datasets"]: + dataset = config["datasets"][dataset_name] + data_path = os.path.join(datasets_dir, dataset["dataset"]) split_datasets = { "train": os.path.join(data_path, "split_train.csv"), "dev": os.path.join(data_path, "split_dev.csv"), @@ -98,32 +99,39 @@ def get_split_dataset_path(dataset_name, config): } return split_datasets else: - raise ValueError(f"Dataset {dataset_name} not found in config file. Available datasets: {config['datasets'].keys()}") + raise ValueError( + f"Dataset {dataset_name} not found in config file. Available datasets: {config['datasets'].keys()}" + ) + def get_user_requirement(task_name, config): - datasets_dir = config['datasets_dir'] - if task_name in config['datasets']: - dataset = config['datasets'][task_name] - data_path = os.path.join(datasets_dir, dataset['dataset']) - user_requirement = dataset['user_requirement'] + datasets_dir = config["datasets_dir"] + if task_name in config["datasets"]: + dataset = config["datasets"][task_name] + data_path = os.path.join(datasets_dir, dataset["dataset"]) + user_requirement = dataset["user_requirement"] return data_path, user_requirement else: - raise ValueError(f"Dataset {task_name} not found in config file. Available datasets: {config['datasets'].keys()}") + raise ValueError( + f"Dataset {task_name} not found in config file. Available datasets: {config['datasets'].keys()}" + ) def save_datasets_dict_to_yaml(datasets_dict): with open("datasets.yaml", "w") as file: yaml.dump(datasets_dict, file) + def create_dataset_dict(dataset): dataset_dict = { "dataset": dataset.name, "user_requirement": dataset.create_base_requirement(), "metric": dataset.get_metric(), - "target_col": dataset.target_col + "target_col": dataset.target_col, } return dataset_dict + def generate_task_requirement(task_name, data_config): user_requirement = get_user_requirement(task_name, data_config) split_dataset_path = get_split_dataset_path(task_name, data_config) @@ -132,19 +140,23 @@ def generate_task_requirement(task_name, data_config): test_path = split_dataset_path["test_wo_target"] work_dir = data_config["work_dir"] output_dir = f"{work_dir}/{task_name}" - user_requirement = TASK_PROMPT.format(user_requirement=user_requirement, - train_path=train_path, dev_path=dev_path, test_path=test_path, - output_dir=output_dir) + user_requirement = TASK_PROMPT.format( + user_requirement=user_requirement, + train_path=train_path, + dev_path=dev_path, + test_path=test_path, + output_dir=output_dir, + ) print(user_requirement) return user_requirement class ExpDataset: - description : str = None - metadata : dict = None - dataset_dir : str = None - target_col : str = None - name : str = None + description: str = None + metadata: dict = None + dataset_dir: str = None + target_col: str = None + name: str = None def __init__(self, name, dataset_dir, **kwargs): self.name = name @@ -154,18 +166,23 @@ def __init__(self, name, dataset_dir, **kwargs): self.save_dataset(target_col=self.target_col) def check_dataset_exists(self): - fnames = ["split_train.csv", "split_dev.csv", "split_test.csv", - "split_dev_wo_target.csv", "split_dev_target.csv", - "split_test_wo_target.csv", "split_test_target.csv"] + fnames = [ + "split_train.csv", + "split_dev.csv", + "split_test.csv", + "split_dev_wo_target.csv", + "split_dev_target.csv", + "split_test_wo_target.csv", + "split_test_target.csv", + ] for fname in fnames: if not os.path.exists(Path(self.dataset_dir, self.name, fname)): return False return True - + def check_datasetinfo_exists(self): return os.path.exists(Path(self.dataset_dir, self.name, "dataset_info.json")) - def get_raw_dataset(self): raw_dir = Path(self.dataset_dir, self.name, "raw") if not os.path.exists(Path(raw_dir, "train.csv")): @@ -173,17 +190,17 @@ def get_raw_dataset(self): else: df = pd.read_csv(Path(raw_dir, "train.csv")) return df - + def get_dataset_info(self): raw_df = pd.read_csv(Path(self.dataset_dir, self.name, "raw", "train.csv")) metadata = { - 'NumberOfClasses': raw_df[self.target_col].nunique(), - 'NumberOfFeatures': raw_df.shape[1], - 'NumberOfInstances': raw_df.shape[0], - 'NumberOfInstancesWithMissingValues': int(raw_df.isnull().any(axis=1).sum()), - 'NumberOfMissingValues': int(raw_df.isnull().sum().sum()), - 'NumberOfNumericFeatures': raw_df.select_dtypes(include=['number']).shape[1], - 'NumberOfSymbolicFeatures': raw_df.select_dtypes(include=['object']).shape[1], + "NumberOfClasses": raw_df[self.target_col].nunique(), + "NumberOfFeatures": raw_df.shape[1], + "NumberOfInstances": raw_df.shape[0], + "NumberOfInstancesWithMissingValues": int(raw_df.isnull().any(axis=1).sum()), + "NumberOfMissingValues": int(raw_df.isnull().sum().sum()), + "NumberOfNumericFeatures": raw_df.select_dtypes(include=["number"]).shape[1], + "NumberOfSymbolicFeatures": raw_df.select_dtypes(include=["object"]).shape[1], } df_head_text = raw_df.head().to_string(index=False) @@ -193,10 +210,10 @@ def get_dataset_info(self): "description": "", "target_col": self.target_col, "metadata": metadata, - "df_head": df_head_text + "df_head": df_head_text, } return dataset_info - + def get_metric(self): dataset_info = self.get_dataset_info() num_classes = dataset_info["metadata"]["NumberOfClasses"] @@ -216,7 +233,6 @@ def create_base_requirement(self): return req def save_dataset(self, target_col): - df = self.get_raw_dataset() if not self.check_dataset_exists() or self.force_update: print(f"Saving Dataset {self.name} in {self.dataset_dir}") @@ -249,25 +265,22 @@ def save_split_datasets(self, df, split, target_col=None): def split_and_save(self, df, target_col): if not target_col: raise ValueError("Target column not provided") - train, test = train_test_split(df, test_size=1-TRAIN_TEST_SPLIT, random_state=SEED) - train, dev = train_test_split(train, test_size=1-TRAIN_DEV_SPLIT, random_state=SEED) + train, test = train_test_split(df, test_size=1 - TRAIN_TEST_SPLIT, random_state=SEED) + train, dev = train_test_split(train, test_size=1 - TRAIN_DEV_SPLIT, random_state=SEED) self.save_split_datasets(train, "train") self.save_split_datasets(dev, "dev", target_col) self.save_split_datasets(test, "test", target_col) - - + class OpenMLExpDataset(ExpDataset): def __init__(self, name, dataset_dir, dataset_id, **kwargs): self.dataset_id = dataset_id - self.dataset = openml.datasets.get_dataset(self.dataset_id, - download_data=False, - download_qualities=False, - download_features_meta_data=True) + self.dataset = openml.datasets.get_dataset( + self.dataset_id, download_data=False, download_qualities=False, download_features_meta_data=True + ) self.name = self.dataset.name self.target_col = self.dataset.default_target_attribute super().__init__(self.name, dataset_dir, target_col=self.target_col, **kwargs) - def get_raw_dataset(self): dataset = self.dataset @@ -276,7 +289,7 @@ def get_raw_dataset(self): os.makedirs(raw_dir, exist_ok=True) dataset_df.to_csv(Path(raw_dir, "train.csv"), index=False) return dataset_df - + def get_dataset_info(self): dataset_info = super().get_dataset_info() dataset = self.dataset @@ -290,12 +303,14 @@ def get_dataset_info(self): # def __init__(self, name, dataset_dir, dataset_name, **kwargs): # super().__init__(name, dataset_dir, **kwargs) + async def process_dataset(dataset, solution_designer, save_analysis_pool, datasets_dict): if save_analysis_pool: asyncio.run(solution_designer.generate_solutions(dataset.get_dataset_info(), dataset.name)) dataset_dict = create_dataset_dict(dataset) datasets_dict["datasets"][dataset.name] = dataset_dict + if __name__ == "__main__": datasets_dir = "D:/work/automl/datasets" force_update = False diff --git a/expo/evaluation/evaluation.py b/expo/evaluation/evaluation.py index 886bc036d..16b3acb71 100644 --- a/expo/evaluation/evaluation.py +++ b/expo/evaluation/evaluation.py @@ -1,5 +1,6 @@ -from sklearn.metrics import f1_score, accuracy_score, roc_auc_score, mean_squared_error import numpy as np +from sklearn.metrics import accuracy_score, f1_score, mean_squared_error, roc_auc_score + def evaluate_score(pred, gt, metric): if metric == "accuracy": @@ -20,4 +21,4 @@ def evaluate_score(pred, gt, metric): elif metric == "log rmse": return mean_squared_error(np.log1p(gt), np.log1p(pred), squared=False) else: - raise ValueError(f"Metric {metric} not supported") \ No newline at end of file + raise ValueError(f"Metric {metric} not supported") diff --git a/expo/evaluation/visualize_mcts.py b/expo/evaluation/visualize_mcts.py index 4199def0e..d310036c0 100644 --- a/expo/evaluation/visualize_mcts.py +++ b/expo/evaluation/visualize_mcts.py @@ -1,7 +1,7 @@ - -from expo.MCTS import Node, MCTS import textwrap +from expo.MCTS import Node + NODE_TEMPLATE = """\ [Node {id}] Plans: @@ -11,21 +11,23 @@ """ + def get_role_plans(role): plans = role.planner.plan.tasks instruct_plans = [f"{i+1}. {task.instruction}" for i, task in enumerate(plans)] return instruct_plans -def get_tree_text(node : Node): +def get_tree_text(node: Node): role_dict = {} code_set = set() + def load_role(node): if node.id not in role_dict: role_dict[node.id] = node.load_role() return role_dict[node.id] - - def visualize_node(node : Node, previous_plans=None): + + def visualize_node(node: Node, previous_plans=None): role = load_role(node) node_id = node.id plans = role.planner.plan.tasks @@ -36,7 +38,9 @@ def visualize_node(node : Node, previous_plans=None): simulated = role.state_saved score = f"avg score: {node.avg_value()}, simulated score: {node.raw_reward}" num_visits = node.visited - return NODE_TEMPLATE.format(id=node_id, plans=instruct_plans_text, simulated=simulated, score=score, num_visits=num_visits) + return NODE_TEMPLATE.format( + id=node_id, plans=instruct_plans_text, simulated=simulated, score=score, num_visits=num_visits + ) def visualize_tree(node, depth=0, previous_plans=None): text = "" @@ -46,11 +50,10 @@ def visualize_tree(node, depth=0, previous_plans=None): code_set.update({task.instruction for task in role.planner.plan.tasks}) previous_plans = get_role_plans(role) for child in node.children: - text += textwrap.indent(visualize_tree(child, depth+1, previous_plans), "\t") + text += textwrap.indent(visualize_tree(child, depth + 1, previous_plans), "\t") return text + num_simulations = node.visited text = f"Number of simulations: {num_simulations}\n" text += visualize_tree(node) return text, len(code_set) - - diff --git a/expo/experimenter/__init__.py b/expo/experimenter/__init__.py index 2eab295f7..e69de29bb 100644 --- a/expo/experimenter/__init__.py +++ b/expo/experimenter/__init__.py @@ -1,4 +0,0 @@ -from .experimenter import Experimenter -from .mcts import MCTSExperimenter -from .aug import AugExperimenter -from .custom import CustomExperimenter \ No newline at end of file diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 86c98fd42..9b14123d3 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -1,9 +1,8 @@ from experimenter import Experimenter -from expo.MCTS import create_initial_state -from expo.dataset import generate_task_requirement -from expo.utils import mcts_logger, load_execute_notebook, get_exp_pool_path + from expo.insights.instruction_generator import InstructionGenerator from expo.research_assistant import ResearchAssistant +from expo.utils import get_exp_pool_path EXPS_PROMPT = """ When doing the tasks, you can refer to the insights below: @@ -12,10 +11,8 @@ """ - - class AugExperimenter(Experimenter): - result_path : str = "results/aug" + result_path: str = "results/aug" async def run_experiment(self): # state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") @@ -31,7 +28,7 @@ async def run_experiment(self): exps = [exp_set_text] * self.args.num_experiments else: raise ValueError(f"Invalid mode: {self.args.aug_mode}") - + results = [] for i in range(self.args.num_experiments): di = ResearchAssistant(node_id=str(i), use_reflection=self.args.reflection) @@ -39,20 +36,19 @@ async def run_experiment(self): requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) print(requirement) score_dict = await self.run_di(di, requirement) - results.append({ - "idx": i, - "score_dict": score_dict, - "aug_mode": self.args.aug_mode, - "insights" : exps[i], - "user_requirement": requirement, - "args": vars(self.args) - }) + results.append( + { + "idx": i, + "score_dict": score_dict, + "aug_mode": self.args.aug_mode, + "insights": exps[i], + "user_requirement": requirement, + "args": vars(self.args), + } + ) scores = [result["score_dict"]["test_score"] for result in results] avg_score = sum(scores) / len(scores) best_score = max(scores) if not self.args.low_is_better else min(scores) best_score_idx = scores.index(best_score) results.insert(0, {"avg_score": avg_score, "best_score": best_score, "best_score_idx": best_score_idx}) self.save_result(results) - - - \ No newline at end of file diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index d59fb3e83..4f5d151ef 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -1,13 +1,15 @@ -from expo.experimenter.custom import CustomExperimenter from autogluon.tabular import TabularDataset, TabularPredictor -class AGRunner(): +from expo.experimenter.custom import CustomExperimenter + + +class AGRunner: preset = "best_quality" time_limit = 500 def __init__(self, datasets): self.datasets = datasets - + def run(self): train_path = self.datasets["train"] test_wo_target_path = self.datasets["test_wo_target"] @@ -16,17 +18,16 @@ def run(self): train_data = TabularDataset(train_path) test_data = TabularDataset(test_wo_target_path) dev_data = TabularDataset(dev_wo_target_path) - + predictor = TabularPredictor(label=target_col).fit(train_data, presets=self.preset, time_limit=self.time_limit) test_preds = predictor.predict(test_data) dev_preds = predictor.predict(dev_data) return {"test_preds": test_preds, "dev_preds": dev_preds} + class GluonExperimenter(CustomExperimenter): - result_path : str = "results/autogluon" + result_path: str = "results/autogluon" def __init__(self, args, **kwargs): super().__init__(args, **kwargs) self.framework = AGRunner(self.datasets) - - diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py index c3cb97b9c..ba009bdb0 100644 --- a/expo/experimenter/custom.py +++ b/expo/experimenter/custom.py @@ -1,21 +1,26 @@ +import os + +import pandas as pd + +from expo.evaluation.evaluation import evaluate_score from expo.experimenter import Experimenter from expo.MCTS import create_initial_state -from expo.evaluation.evaluation import evaluate_score -import pandas as pd -import os + class CustomExperimenter(Experimenter): - result_path : str = "results/custom" - + result_path: str = "results/custom" + def __init__(self, args, **kwargs): super().__init__(args, **kwargs) - self.framework = kwargs["framework"] # todo + self.framework = kwargs["framework"] # todo self.task = kwargs.get("task", self.args.task) self.low_is_better = kwargs.get("low_is_better", self.args.low_is_better) self.name = kwargs.get("name", "") self.result_path = f"results/custom_{self.name}" - self.state = create_initial_state(self.task, start_task_id=1, data_config=self.data_config, low_is_better=self.low_is_better, name=self.name) - + self.state = create_initial_state( + self.task, start_task_id=1, data_config=self.data_config, low_is_better=self.low_is_better, name=self.name + ) + def run_experiment(self): user_requirement = self.state["requirement"] preds = self.framework.run(user_requirement) @@ -23,13 +28,9 @@ def run_experiment(self): dev_preds = preds["dev_preds"] score_dict = { "dev_score": self.evaluate_predictions(dev_preds, "dev"), - "test_score": self.evaluate_predictions(test_preds, "test") - } - results = { - "score_dict": score_dict, - "user_requirement": user_requirement, - "args": vars(self.args) + "test_score": self.evaluate_predictions(test_preds, "test"), } + results = {"score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} self.save_result(results) def evaluate_pred_files(self, dev_pred_path, test_pred_path): @@ -37,7 +38,7 @@ def evaluate_pred_files(self, dev_pred_path, test_pred_path): test_preds = pd.read_csv(test_pred_path)["target"] score_dict = { "dev_score": self.evaluate_score(dev_preds, "dev"), - "test_score": self.evaluate_score(test_preds, "test") + "test_score": self.evaluate_score(test_preds, "test"), } return score_dict @@ -46,8 +47,7 @@ def evaluate_predictions(self, preds, split): gt_path = os.path.join(self.state["datasets_dir"][f"{split}_target"]) gt = pd.read_csv(gt_path)["target"] score = evaluate_score(preds, gt, metric) - return score - + return score def load_datasets(self): train_path = self.state["datasets_dir"]["train"] @@ -57,4 +57,3 @@ def load_datasets(self): dev = pd.read_csv(dev_path) test = pd.read_csv(test_path) return train, dev, test - diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 709eefdfc..83dde80b9 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -1,23 +1,29 @@ -from expo.utils import DATA_CONFIG +import datetime +import json import os + import pandas as pd + from expo.evaluation.evaluation import evaluate_score -import datetime -import json from expo.MCTS import create_initial_state from expo.research_assistant import ResearchAssistant +from expo.utils import DATA_CONFIG class Experimenter: - result_path : str = "results/base" + result_path: str = "results/base" data_config = DATA_CONFIG - def __init__(self, args, **kwargs): self.args = args self.start_time = datetime.datetime.now().strftime("%Y%m%d%H%M") - self.state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") - + self.state = create_initial_state( + self.args.task, + start_task_id=1, + data_config=self.data_config, + low_is_better=self.args.low_is_better, + name="", + ) async def run_di(self, di, user_requirement): max_retries = 3 @@ -33,14 +39,8 @@ async def run_di(self, di, user_requirement): print(f"Error: {e}") num_runs += 1 if not run_finished: - score_dict = { - "train_score": -1, - "dev_score": -1, - "test_score": -1, - "score": -1 - } + score_dict = {"train_score": -1, "dev_score": -1, "test_score": -1, "score": -1} return score_dict - async def run_experiment(self): state = self.state @@ -50,28 +50,28 @@ async def run_experiment(self): for i in range(self.args.num_experiments): di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) score_dict = await self.run_di(di, user_requirement) - results.append({ - "idx": i, - "score_dict": score_dict, - "user_requirement": user_requirement, - "args": vars(self.args) - }) - self.save_result(results) # save intermediate results + results.append( + {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} + ) + self.save_result(results) # save intermediate results dev_scores = [result["score_dict"]["dev_score"] for result in results] best_dev_score = max(dev_scores) if not self.args.low_is_better else min(dev_scores) best_score_idx = dev_scores.index(best_dev_score) - + test_scores = [result["score_dict"]["test_score"] for result in results] avg_score = sum(test_scores) / len(test_scores) global_best_score = max(test_scores) if not self.args.low_is_better else min(test_scores) - results.insert(0, { - "best_dev_score": best_dev_score, - "best_score_idx": best_score_idx, - "best_test_score": test_scores[best_score_idx], - "avg_test_score": avg_score, - "best_score": global_best_score - }) + results.insert( + 0, + { + "best_dev_score": best_dev_score, + "best_score_idx": best_score_idx, + "best_test_score": test_scores[best_score_idx], + "avg_test_score": avg_score, + "best_score": global_best_score, + }, + ) self.save_result(results) def evaluate_prediction(self, split, state): @@ -85,7 +85,7 @@ def evaluate_prediction(self, split, state): metric = state["dataset_config"]["metric"] os.remove(pred_path) return evaluate_score(preds, gt, metric) - + def evaluate(self, score_dict, state): scores = { "dev_score": self.evaluate_prediction("dev", state), @@ -94,13 +94,12 @@ def evaluate(self, score_dict, state): score_dict.update(scores) return score_dict - def save_result(self, result): end_time = datetime.datetime.now().strftime("%Y%m%d%H%M") time_info = { "start_time": self.start_time, "end_time": end_time, - "duration (minutes)": float(end_time) - float(self.start_time) + "duration (minutes)": float(end_time) - float(self.start_time), } result = result.copy() result.insert(0, time_info) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index e41f94d58..921b81412 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -1,22 +1,25 @@ +from expo.evaluation.visualize_mcts import get_tree_text from expo.experimenter import Experimenter -from expo.dataset import generate_task_requirement from expo.MCTS import MCTS -from expo.evaluation.visualize_mcts import get_tree_text class MCTSExperimenter(Experimenter): - result_path : str = "results/mcts" + result_path: str = "results/mcts" + async def run_experiment(self): mcts = MCTS(root_node=None, max_depth=5) - best_nodes = await mcts.search(self.args.task, self.data_config, - low_is_better=self.args.low_is_better, - load_tree=self.args.load_tree, - reflection=self.args.reflection, - rollouts=self.args.rollouts, - name=self.args.name) + best_nodes = await mcts.search( + self.args.task, + self.data_config, + low_is_better=self.args.low_is_better, + load_tree=self.args.load_tree, + reflection=self.args.reflection, + rollouts=self.args.rollouts, + name=self.args.name, + ) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] - + text, num_generated_codes = get_tree_text(mcts.root_node) text += f"Generated {num_generated_codes} unique codes.\n" text += f"Best node: {best_node}, score: {best_node.raw_reward}\n" @@ -24,22 +27,21 @@ async def run_experiment(self): print(text) self.save_tree(text) - results = [{ - "best_node": best_node.id, - "best_node_score": best_node.raw_reward, - "dev_best_node": dev_best_node.id, - "dev_best_node_score": dev_best_node.raw_reward, - "num_generated_codes": num_generated_codes, - "user_requirement": best_node.state["requirement"], - "tree_text": text, - "args": vars(self.args) - }] + results = [ + { + "best_node": best_node.id, + "best_node_score": best_node.raw_reward, + "dev_best_node": dev_best_node.id, + "dev_best_node_score": dev_best_node.raw_reward, + "num_generated_codes": num_generated_codes, + "user_requirement": best_node.state["requirement"], + "tree_text": text, + "args": vars(self.args), + } + ] self.save_result(results) - - def save_tree(self, tree_text): fpath = f"{self.result_path}/{self.args.task}_tree_{self.args.name}.txt" with open(fpath, "w") as f: f.write(tree_text) - diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 4f4155ff8..065565c89 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -1,3 +1,10 @@ +import json +import random + +from expo.utils import clean_json_from_rsp, load_data_config, mcts_logger +from metagpt.llm import LLM +from metagpt.schema import Message + REFLECTION_SYSTEM_MSG = "As a Kaggle grandmaster participating in a competition, you need to analyze your experience and propose evolutionary points that are more likely to improve the performance of baseline code." CHANGE_INSTRUCTION = """ @@ -18,12 +25,6 @@ ``` """ -import re -import random -import json -from metagpt.llm import LLM -from metagpt.schema import Message -from expo.utils import load_data_config, mcts_logger, clean_json_from_rsp DATA_CONFIG = load_data_config() @@ -31,7 +32,7 @@ class InstructionGenerator: data_config = DATA_CONFIG @staticmethod - def load_json_data(json_dir): + def load_json_data(json_dir): with open(json_dir, "r") as file: json_data = json.load(file) return json_data @@ -39,7 +40,7 @@ def load_json_data(json_dir): @staticmethod def _random_sample(analysis, num_samples): return random.sample(analysis, num_samples) - + @staticmethod def sample_instruction_set(data): data_dict = {} @@ -52,12 +53,12 @@ def sample_instruction_set(data): for task_id in sorted(data_dict.keys()): instruction_set.append(random.choice(data_dict[task_id])) return instruction_set - + @staticmethod def format_output(rsp): rsp_list = [] - new_data = [] - rsp_list.append(rsp) + new_data = [] + rsp_list.append(rsp) for item in rsp_list: item_dict = json.loads(item) data = { @@ -83,21 +84,19 @@ async def generate_new_instructions(task_id, original_instruction, max_num, file new_instructions = [] if len(data) == 0: mcts_logger.log("MCTS", f"No insights available for task {task_id}") - return [original_instruction] # Return the original instruction if no insights are available + return [original_instruction] # Return the original instruction if no insights are available for item in data[:max_num]: insights = item["Analysis"] new_instruction = await InstructionGenerator.generate_new_instruction(original_instruction, insights) new_instructions.append(new_instruction) return new_instructions - + @staticmethod async def generate_new_instruction(original_instruction, insights): prompt = CHANGE_INSTRUCTION.format(instruction=original_instruction, insights=insights) llm = LLM() - context = llm.format_msg([Message(content=prompt, role="user")]) - llm_response = await llm.aask( - context, system_msgs=[REFLECTION_SYSTEM_MSG] - ) + context = llm.format_msg([Message(content=prompt, role="user")]) + llm_response = await llm.aask(context, system_msgs=[REFLECTION_SYSTEM_MSG]) rsp = clean_json_from_rsp(llm_response) new_instruction = json.loads(rsp)["New Instruction"] - return new_instruction \ No newline at end of file + return new_instruction diff --git a/expo/insights/solution_designer.py b/expo/insights/solution_designer.py index e2bf57ae3..fc05afeea 100644 --- a/expo/insights/solution_designer.py +++ b/expo/insights/solution_designer.py @@ -1,10 +1,7 @@ -import re -import random import json -from metagpt.llm import LLM -from metagpt.schema import Message -from expo.utils import clean_json_from_rsp, load_data_config +from expo.utils import clean_json_from_rsp, load_data_config +from metagpt.llm import LLM DATA_CONFIG = load_data_config() @@ -72,56 +69,50 @@ """ KEY_DATASET_FEATURES = [ - 'NumberOfClasses', - 'NumberOfFeatures', - 'NumberOfInstances', - 'NumberOfInstancesWithMissingValues', - 'NumberOfMissingValues', - 'NumberOfNumericFeatures', - 'NumberOfSymbolicFeatures' + "NumberOfClasses", + "NumberOfFeatures", + "NumberOfInstances", + "NumberOfInstancesWithMissingValues", + "NumberOfMissingValues", + "NumberOfNumericFeatures", + "NumberOfSymbolicFeatures", ] -TASK_TO_ID = { - "EDA": 1, - "Data Preprocessing": 2, - "Feature Engineering": 3, - "Model Training": 4, - "Model Evaluation": 5 -} +TASK_TO_ID = {"EDA": 1, "Data Preprocessing": 2, "Feature Engineering": 3, "Model Training": 4, "Model Evaluation": 5} + class SolutionDesigner: - data_dir : str= DATA_CONFIG["datasets_dir"] + data_dir: str = DATA_CONFIG["datasets_dir"] async def generate_solutions(self, dataset_info, dataset_name): llm = LLM() - context = DATASET_INSIGHT_PROMPT.format(dataset=dataset_info["description"], - metadata=self.metadata_builder(dataset_info["metadata"]), - head=dataset_info["df_head"]) + context = DATASET_INSIGHT_PROMPT.format( + dataset=dataset_info["description"], + metadata=self.metadata_builder(dataset_info["metadata"]), + head=dataset_info["df_head"], + ) rsp = await llm.aask(context) rsp = clean_json_from_rsp(rsp) analysis_pool = self.process_analysis_pool(json.loads(rsp)) dataset_path = f"{self.data_dir}/{dataset_name}" self.save_analysis_pool(dataset_path, analysis_pool) - - + def process_analysis_pool(self, insights_rsp): analysis_pool = [] for task_type_insights in insights_rsp: task_type = task_type_insights["task_type"] for insight in task_type_insights["insights"]: - analysis_pool.append({"Analysis": insight, "Category": task_type, "task_id": TASK_TO_ID[task_type]}) + analysis_pool.append({"Analysis": insight, "Category": task_type, "task_id": TASK_TO_ID[task_type]}) return analysis_pool - def metadata_builder(self, qualities): metadata = {} for key in KEY_DATASET_FEATURES: metadata[key] = qualities.get(key, "N/A") metadata_text = json.dumps(metadata, indent=4) return metadata_text - + def save_analysis_pool(self, dataset_path, analysis_pool): fpath = f"{dataset_path}/ds_analysis_pool.json" with open(fpath, "w") as file: json.dump(analysis_pool, file, indent=4) - \ No newline at end of file diff --git a/expo/research_assistant.py b/expo/research_assistant.py index ed935b4b8..b21fc1a55 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -1,19 +1,16 @@ from __future__ import annotations import json +import os + +from pydantic import model_validator + +from expo.utils import mcts_logger, save_notebook +from metagpt.actions.di.write_analysis_code import WriteAnalysisCode +from metagpt.const import SERDESER_PATH from metagpt.roles.di.data_interpreter import DataInterpreter from metagpt.schema import Message, Task, TaskResult -from metagpt.strategy.task_type import TaskType -from metagpt.tools.tool_recommend import BM25ToolRecommender, ToolRecommender -from metagpt.utils.common import CodeParser -from metagpt.utils.common import write_json_file, read_json_file, format_trackback_info -from metagpt.const import MESSAGE_ROUTE_TO_ALL, SERDESER_PATH -from expo.utils import mcts_logger, save_notebook -from pydantic import Field, model_validator -from metagpt.actions.di.write_analysis_code import CheckData, WriteAnalysisCode - -import re -import os +from metagpt.utils.common import CodeParser, write_json_file EXTRACT_SCORE_PROMPT = """ # Code: @@ -36,39 +33,48 @@ ``` """ + class ResearchAssistant(DataInterpreter): node_id: str = "0" start_task_id: int = 1 - state_saved : bool = False - role_dir : str = SERDESER_PATH.joinpath("team", "environment", "roles", f"Experimenter") + state_saved: bool = False + role_dir: str = SERDESER_PATH.joinpath("team", "environment", "roles", "Experimenter") def get_node_name(self): return f"Node-{self.node_id}" - + def get_next_instruction(self): return self.planner.plan.tasks[self.start_task_id] - + def change_next_instruction(self, new_instruction): if new_instruction is not None: self.planner.plan.task_map[str(self.start_task_id)].instruction = new_instruction self.remap_tasks() - def update_til_start_task(self, role: ResearchAssistant, backward: bool = True): if backward: # make sure the previous task instructions are matched - assert self.start_task_id == role.start_task_id - 1, f"start_task_id: {self.start_task_id}, role.start_task_id: {role.start_task_id}" + assert ( + self.start_task_id == role.start_task_id - 1 + ), f"start_task_id: {self.start_task_id}, role.start_task_id: {role.start_task_id}" for i in range(self.start_task_id): - if self.planner.plan.task_map[str(self.start_task_id)].instruction != role.planner.plan.task_map[str(self.start_task_id)].instruction: + if ( + self.planner.plan.task_map[str(self.start_task_id)].instruction + != role.planner.plan.task_map[str(self.start_task_id)].instruction + ): mcts_logger.info("Previous task instructions not matched") self.remap_tasks() return # copy new role's task (self.start_task_id) to current role - self.planner.plan.task_map[str(self.start_task_id)] = role.planner.plan.task_map[str(self.start_task_id)].model_copy() + self.planner.plan.task_map[str(self.start_task_id)] = role.planner.plan.task_map[ + str(self.start_task_id) + ].model_copy() self.remap_tasks() else: - assert self.start_task_id == role.start_task_id + 1, f"start_task_id: {self.start_task_id}, role.start_task_id: {role.start_task_id}" + assert ( + self.start_task_id == role.start_task_id + 1 + ), f"start_task_id: {self.start_task_id}, role.start_task_id: {role.start_task_id}" if int(role.planner.plan.current_task_id) > self.start_task_id: for i in range(role.start_task_id): self.planner.plan.task_map[str(i)] = role.planner.plan.task_map[str(i)].model_copy() @@ -86,11 +92,10 @@ async def llm_extract_score(self): json_block = CodeParser.parse_code(block=None, text=rsp) score_dict = json.loads(json_block) return score_dict - @model_validator(mode="after") def set_plan_and_tool(self) -> "Interpreter": - if self.planner.plan.goal != '': + if self.planner.plan.goal != "": self.set_actions([WriteAnalysisCode]) self._set_state(0) print("Plan already exists, skipping initialization.") @@ -116,17 +121,17 @@ def save_state(self, static_save=False): self.state_saved = True mcts_logger.log("MCTS", f"Saving state at task {self.start_task_id}") else: - mcts_logger.log("MCTS", f"Static Saving") + mcts_logger.log("MCTS", "Static Saving") stg_path = self.role_dir name = self.get_node_name() role_path = os.path.join(stg_path, f"{name}.json") # 将状态保存为 JSON 文件 write_json_file(role_path, self.model_dump()) - def remap_tasks(self): - self.planner.plan.tasks = [self.planner.plan.task_map[task_id] for task_id in sorted(self.planner.plan.task_map.keys())] - + self.planner.plan.tasks = [ + self.planner.plan.task_map[task_id] for task_id in sorted(self.planner.plan.task_map.keys()) + ] async def run(self, with_message=None) -> Message | None: """Observe, and think and act based on the results of the observation""" @@ -138,13 +143,9 @@ async def run(self, with_message=None) -> Message | None: self.rc.working_memory.clear() self.working_memory.clear() # self.rc.todo = WriteAnalysisCode() - rsp = await self.react() + rsp = await self.react() # 发送响应消息给 Environment 对象，以便它将消息传递给订阅者 self.set_todo(None) self.publish_message(rsp) return rsp return await super().run(with_message) - - - - \ No newline at end of file diff --git a/expo/run_exp_augmentation.py b/expo/run_exp_augmentation.py index 3f8eff3b3..7fb174ff7 100644 --- a/expo/run_exp_augmentation.py +++ b/expo/run_exp_augmentation.py @@ -1,15 +1,17 @@ -import os -from expo.research_assistant import ResearchAssistant +import argparse import asyncio -from expo.utils import DATA_CONFIG, get_exp_pool_path +import datetime +import json +import os + +import pandas as pd + from expo.dataset import generate_task_requirement +from expo.evaluation.evaluation import evaluate_score from expo.insights.instruction_generator import InstructionGenerator from expo.MCTS import create_initial_state -from expo.evaluation.evaluation import evaluate_score -import json -import argparse -import pandas as pd -import datetime +from expo.research_assistant import ResearchAssistant +from expo.utils import DATA_CONFIG, get_exp_pool_path EXPS_PROMPT = """ When doing the tasks, you can refer to the insights below: @@ -18,13 +20,14 @@ """ data_config = DATA_CONFIG + def evaluate_test(score, state): datetime_text = datetime.datetime.now().strftime("%Y%m%d%H%M") task_name = state["task"] prediction_fpath = os.path.join(state["work_dir"], task_name, "predictions.csv") predictions = pd.read_csv(prediction_fpath)["target"] # copy predictions.csv to the node_dir - + predictions_node_fpath = os.path.join("results", f"{task_name}-{datetime_text}-predictions.csv") predictions.to_csv(predictions_node_fpath, index=False) # load test_target.csv @@ -35,8 +38,6 @@ def evaluate_test(score, state): return score - - async def main(task_name, use_reflection=True, mode="single", num_experiments=2): """ mode: single or set @@ -44,8 +45,10 @@ async def main(task_name, use_reflection=True, mode="single", num_experiments=2) set: sample a set of instructions """ low_is_better = False - state = create_initial_state(task_name, start_task_id=1, data_config=data_config, low_is_better=low_is_better, name="") - + state = create_initial_state( + task_name, start_task_id=1, data_config=data_config, low_is_better=low_is_better, name="" + ) + user_requirement = generate_task_requirement(task_name, data_config) exp_pool_path = get_exp_pool_path(task_name, data_config, pool_name="ds_analysis_pool") exp_pool = InstructionGenerator.load_analysis_pool(exp_pool_path) @@ -58,7 +61,7 @@ async def main(task_name, use_reflection=True, mode="single", num_experiments=2) exps = [exp_set_text] * num_experiments else: raise ValueError(f"Invalid mode: {mode}") - + scores = [] for i in range(num_experiments): di = ResearchAssistant(node_id=str(i), use_reflection=use_reflection) @@ -70,16 +73,18 @@ async def main(task_name, use_reflection=True, mode="single", num_experiments=2) score = evaluate_test(score, state) scores.append(score) - with open(f"results/{task_name}_scores.json", "w") as f: # save scores and corresponding insights - results = {"avg_score": sum([score["test_score"] for score in scores if score])/num_experiments, - "max_score": max([score["test_score"] for score in scores]), - "scores": scores, "insights": exps} + results = { + "avg_score": sum([score["test_score"] for score in scores if score]) / num_experiments, + "max_score": max([score["test_score"] for score in scores]), + "scores": scores, + "insights": exps, + } json.dump(results, f, indent=4) - - + + def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="titanic") @@ -90,8 +95,9 @@ def parse_args(): parser.add_argument("--num_experiments", type=int, default=2) return parser.parse_args() - if __name__ == "__main__": args = parse_args() - asyncio.run(main(args.task, use_reflection=args.use_reflection, mode=args.mode, num_experiments=args.num_experiments)) + asyncio.run( + main(args.task, use_reflection=args.use_reflection, mode=args.mode, num_experiments=args.num_experiments) + ) diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 826019321..f8e58ce4f 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -1,6 +1,12 @@ -from expo.experimenter import MCTSExperimenter, Experimenter, AugExperimenter, CustomExperimenter -import asyncio import argparse +import asyncio + +from expo.experimenter import ( + AugExperimenter, + CustomExperimenter, + Experimenter, + MCTSExperimenter, +) def get_args(): @@ -19,6 +25,7 @@ def get_mcts_args(parser): parser.set_defaults(load_tree=False) parser.add_argument("--rollouts", type=int, default=5) + def get_aug_exp_args(parser): parser.add_argument("--aug_mode", type=str, default="single", choices=["single", "set"]) parser.add_argument("--num_experiments", type=int, default=1) @@ -31,7 +38,7 @@ def get_di_args(parser): parser.add_argument("--reflection", dest="reflection", action="store_true") parser.add_argument("--no_reflection", dest="reflection", action="store_false") parser.set_defaults(reflection=True) - + async def main(args): if args.exp_mode == "mcts": @@ -46,6 +53,7 @@ async def main(args): raise ValueError(f"Invalid exp_mode: {args.exp_mode}") await experimenter.run_experiment() + if __name__ == "__main__": args = get_args() - asyncio.run(main(args)) \ No newline at end of file + asyncio.run(main(args)) diff --git a/expo/run_mcts.py b/expo/run_mcts.py index 20d4171f7..4577417a9 100644 --- a/expo/run_mcts.py +++ b/expo/run_mcts.py @@ -1,10 +1,9 @@ -from expo.MCTS import MCTS, Node, initialize_di_root_node -from expo.utils import load_data_config -from expo.dataset import generate_task_requirement +import argparse +import asyncio from expo.evaluation.visualize_mcts import get_tree_text -import asyncio -import argparse +from expo.MCTS import MCTS +from expo.utils import load_data_config def get_args(): @@ -35,9 +34,17 @@ def get_args(): # asyncio.run(root_node.run_node()) mcts = MCTS(root_node=None, max_depth=5) - best_nodes = asyncio.run(mcts.search(args.task, data_config, - low_is_better=args.low_is_better, load_tree=args.load_tree, - reflection=args.reflection, rollouts=args.rollouts, name=args.name)) + best_nodes = asyncio.run( + mcts.search( + args.task, + data_config, + low_is_better=args.low_is_better, + load_tree=args.load_tree, + reflection=args.reflection, + rollouts=args.rollouts, + name=args.name, + ) + ) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] text, num_generated_codes = get_tree_text(mcts.root_node) @@ -49,5 +56,3 @@ def get_args(): f.write(f"Best node: {best_node}, score: {best_node.raw_reward}\n") f.write(f"Dev best node: {dev_best_node}, score: {dev_best_node.raw_reward}\n") f.write(text) - - diff --git a/expo/utils.py b/expo/utils.py index 20e3fa7f5..d67ceb5a1 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -1,50 +1,58 @@ +import os +import re +import sys +from datetime import datetime +from pathlib import Path + +import nbformat import yaml -from metagpt.roles.role import Role -from metagpt.actions.di.execute_nb_code import ExecuteNbCode +from loguru import logger as _logger + # from nbclient import NotebookClient from nbformat.notebooknode import NotebookNode -import nbformat -from pathlib import Path -from loguru import logger as _logger -from datetime import datetime -import sys -import os -import re + +from metagpt.roles.role import Role + def load_data_config(file_path="data.yaml"): - with open(file_path, 'r') as stream: + with open(file_path, "r") as stream: data_config = yaml.safe_load(stream) return data_config + DATA_CONFIG = load_data_config() + def get_mcts_logger(): print_level = "INFO" print_level2 = "MCTS" - logfile_level="MCTS" + logfile_level = "MCTS" name: str = None current_date = datetime.now() formatted_date = current_date.strftime("%Y%m%d") log_name = f"{name}_{formatted_date}" if name else formatted_date # name a log with prefix name _logger.remove() - new_level = _logger.level(logfile_level, color="", no=25) + _logger.level(logfile_level, color="", no=25) _logger.add(sys.stderr, level=print_level) _logger.add(sys.stderr, level=print_level2) _logger.add(Path(DATA_CONFIG["work_dir"]) / DATA_CONFIG["role_dir"] / f"{log_name}.txt", level=logfile_level) _logger.propagate = False return _logger + mcts_logger = get_mcts_logger() def get_exp_pool_path(task_name, data_config, pool_name="analysis_pool"): - datasets_dir = data_config['datasets_dir'] - if task_name in data_config['datasets']: - dataset = data_config['datasets'][task_name] - data_path = os.path.join(datasets_dir, dataset['dataset']) + datasets_dir = data_config["datasets_dir"] + if task_name in data_config["datasets"]: + dataset = data_config["datasets"][task_name] + data_path = os.path.join(datasets_dir, dataset["dataset"]) else: - raise ValueError(f"Dataset {task_name} not found in config file. Available datasets: {data_config['datasets'].keys()}") + raise ValueError( + f"Dataset {task_name} not found in config file. Available datasets: {data_config['datasets'].keys()}" + ) exp_pool_path = os.path.join(data_path, f"{pool_name}.json") return exp_pool_path @@ -60,7 +68,6 @@ def change_plan(role, plan): if not finished: tasks[i].plan = plan return finished - def is_cell_to_delete(cell: NotebookNode) -> bool: @@ -82,12 +89,14 @@ def process_cells(nb: NotebookNode) -> NotebookNode: nb["cells"] = new_cells return nb + def save_notebook(role: Role, save_dir: str = "", name: str = ""): save_dir = Path(save_dir) nb = process_cells(role.execute_code.nb) file_path = save_dir / f"{name}.ipynb" nbformat.write(nb, file_path) + async def load_execute_notebook(role): tasks = role.planner.plan.tasks codes = [task.code for task in tasks if task.code] @@ -99,6 +108,7 @@ async def load_execute_notebook(role): print("Finish executing the loaded notebook") return executor + def clean_json_from_rsp(text): pattern = r"```json(.*?)```" matches = re.findall(pattern, text, re.DOTALL) @@ -106,4 +116,4 @@ def clean_json_from_rsp(text): json_str = "\n".join(matches) return json_str else: - return "" \ No newline at end of file + return "" From 2c43944ec016e42c61bc476bbe761f8388c98324 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 4 Sep 2024 18:08:59 +0800 Subject: [PATCH 024/135] fix import --- expo/run_experiment.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/expo/run_experiment.py b/expo/run_experiment.py index f8e58ce4f..8871c04a6 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -1,12 +1,10 @@ import argparse import asyncio -from expo.experimenter import ( - AugExperimenter, - CustomExperimenter, - Experimenter, - MCTSExperimenter, -) +from expo.experimenter.aug import AugExperimenter +from expo.experimenter.custom import CustomExperimenter +from expo.experimenter.experimenter import Experimenter +from expo.experimenter.mcts import MCTSExperimenter def get_args(): From 58d7b14007684afa947b1c91bf7da0eca9734919 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 4 Sep 2024 18:46:16 +0800 Subject: [PATCH 025/135] fix import --- expo/experimenter/aug.py | 3 +-- expo/experimenter/custom.py | 2 +- expo/experimenter/mcts.py | 2 +- 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 9b14123d3..1bf927cc1 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -1,5 +1,4 @@ -from experimenter import Experimenter - +from expo.experimenter.experimenter import Experimenter from expo.insights.instruction_generator import InstructionGenerator from expo.research_assistant import ResearchAssistant from expo.utils import get_exp_pool_path diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py index ba009bdb0..4a5486af0 100644 --- a/expo/experimenter/custom.py +++ b/expo/experimenter/custom.py @@ -3,7 +3,7 @@ import pandas as pd from expo.evaluation.evaluation import evaluate_score -from expo.experimenter import Experimenter +from expo.experimenter.experimenter import Experimenter from expo.MCTS import create_initial_state diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 921b81412..2805cae51 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -1,5 +1,5 @@ from expo.evaluation.visualize_mcts import get_tree_text -from expo.experimenter import Experimenter +from expo.experimenter.experimenter import Experimenter from expo.MCTS import MCTS From c16286a006c5d85c8b9f34c0183cde4c2c9849e8 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 5 Sep 2024 10:12:37 +0800 Subject: [PATCH 026/135] Refactor MCTS class to handle role running errors and improve error logging --- expo/MCTS.py | 13 +++++++++---- expo/experimenter/experimenter.py | 8 ++++++-- 2 files changed, 15 insertions(+), 6 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 7c03e2e86..b2ad824e5 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -3,6 +3,7 @@ import pickle import random +import numpy as np import pandas as pd from expo.dataset import generate_task_requirement, get_split_dataset_path @@ -209,16 +210,20 @@ async def run_node(self, role=None): await role.run(with_message="continue") else: await role.run(with_message=self.state["requirement"]) + score_dict = await role.get_score() + score_dict = self.evaluate_simulation(score_dict) + self.raw_reward = score_dict run_finished = True except Exception as e: mcts_logger.log("MCTS", f"Error in running the role: {e}") num_runs += 1 if not run_finished: mcts_logger.log("MCTS", f"Role {role.node_id} failed to run") - return {"test_score": 0, "dev_score": 0, "score": 0} - score_dict = await role.get_score() - score_dict = self.evaluate_simulation(score_dict) - self.raw_reward = score_dict + if self.state["low_is_better"]: + score_dict = {"test_score": np.inf, "dev_score": np.inf, "score": np.inf} + else: + score_dict = {"test_score": 0, "dev_score": 0, "score": 0} + self.raw_reward = score_dict if self.state["low_is_better"]: # normalized the score to be between 0 and 1, and higher is better def normalize_score(score): diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 83dde80b9..4161aef3d 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -54,11 +54,15 @@ async def run_experiment(self): {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} ) self.save_result(results) # save intermediate results - dev_scores = [result["score_dict"]["dev_score"] for result in results] + dev_scores = [ + result["score_dict"]["dev_score"] for result in results if result["score_dict"]["dev_score"] != -1 + ] best_dev_score = max(dev_scores) if not self.args.low_is_better else min(dev_scores) best_score_idx = dev_scores.index(best_dev_score) - test_scores = [result["score_dict"]["test_score"] for result in results] + test_scores = [ + result["score_dict"]["test_score"] for result in results if result["score_dict"]["dev_score"] != -1 + ] avg_score = sum(test_scores) / len(test_scores) global_best_score = max(test_scores) if not self.args.low_is_better else min(test_scores) From 45d176b48bde9e67a91758e412034e3af60aa2f6 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 5 Sep 2024 11:00:07 +0800 Subject: [PATCH 027/135] remove deprecated scripts --- expo/run_exp_augmentation.py | 103 ----------------------------------- expo/run_mcts.py | 58 -------------------- 2 files changed, 161 deletions(-) delete mode 100644 expo/run_exp_augmentation.py delete mode 100644 expo/run_mcts.py diff --git a/expo/run_exp_augmentation.py b/expo/run_exp_augmentation.py deleted file mode 100644 index 7fb174ff7..000000000 --- a/expo/run_exp_augmentation.py +++ /dev/null @@ -1,103 +0,0 @@ -import argparse -import asyncio -import datetime -import json -import os - -import pandas as pd - -from expo.dataset import generate_task_requirement -from expo.evaluation.evaluation import evaluate_score -from expo.insights.instruction_generator import InstructionGenerator -from expo.MCTS import create_initial_state -from expo.research_assistant import ResearchAssistant -from expo.utils import DATA_CONFIG, get_exp_pool_path - -EXPS_PROMPT = """ -When doing the tasks, you can refer to the insights below: -{experience} - -""" -data_config = DATA_CONFIG - - -def evaluate_test(score, state): - datetime_text = datetime.datetime.now().strftime("%Y%m%d%H%M") - task_name = state["task"] - prediction_fpath = os.path.join(state["work_dir"], task_name, "predictions.csv") - predictions = pd.read_csv(prediction_fpath)["target"] - # copy predictions.csv to the node_dir - - predictions_node_fpath = os.path.join("results", f"{task_name}-{datetime_text}-predictions.csv") - predictions.to_csv(predictions_node_fpath, index=False) - # load test_target.csv - split_datasets_dir = state["datasets_dir"] - gt = pd.read_csv(os.path.join(split_datasets_dir["test_target"]))["target"] - metric = state["dataset_config"]["metric"] - score["test_score"] = evaluate_score(predictions, gt, metric) - return score - - -async def main(task_name, use_reflection=True, mode="single", num_experiments=2): - """ - mode: single or set - single: sample one instruction - set: sample a set of instructions - """ - low_is_better = False - state = create_initial_state( - task_name, start_task_id=1, data_config=data_config, low_is_better=low_is_better, name="" - ) - - user_requirement = generate_task_requirement(task_name, data_config) - exp_pool_path = get_exp_pool_path(task_name, data_config, pool_name="ds_analysis_pool") - exp_pool = InstructionGenerator.load_analysis_pool(exp_pool_path) - if mode == "single": - exps = InstructionGenerator._random_sample(exp_pool, num_experiments) - exps = [exp["Analysis"] for exp in exps] - elif mode == "set": - exp_set = InstructionGenerator.sample_instruction_set(exp_pool) - exp_set_text = "\n".join([f"{exp['task_id']}: {exp['Analysis']}" for exp in exp_set]) - exps = [exp_set_text] * num_experiments - else: - raise ValueError(f"Invalid mode: {mode}") - - scores = [] - for i in range(num_experiments): - di = ResearchAssistant(node_id=str(i), use_reflection=use_reflection) - di.role_dir = f"{di.role_dir}_{task_name}" - requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) - print(requirement) - await di.run(requirement) - score = await di.get_score(low_is_better=False) - score = evaluate_test(score, state) - - scores.append(score) - - with open(f"results/{task_name}_scores.json", "w") as f: - # save scores and corresponding insights - results = { - "avg_score": sum([score["test_score"] for score in scores if score]) / num_experiments, - "max_score": max([score["test_score"] for score in scores]), - "scores": scores, - "insights": exps, - } - json.dump(results, f, indent=4) - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--task", type=str, default="titanic") - parser.add_argument("--use_reflection", dest="use_reflection", action="store_true") - parser.add_argument("--no_use_reflection", dest="use_reflection", action="store_false") - parser.set_defaults(use_reflection=True) - parser.add_argument("--mode", type=str, default="single") - parser.add_argument("--num_experiments", type=int, default=2) - return parser.parse_args() - - -if __name__ == "__main__": - args = parse_args() - asyncio.run( - main(args.task, use_reflection=args.use_reflection, mode=args.mode, num_experiments=args.num_experiments) - ) diff --git a/expo/run_mcts.py b/expo/run_mcts.py deleted file mode 100644 index 4577417a9..000000000 --- a/expo/run_mcts.py +++ /dev/null @@ -1,58 +0,0 @@ -import argparse -import asyncio - -from expo.evaluation.visualize_mcts import get_tree_text -from expo.MCTS import MCTS -from expo.utils import load_data_config - - -def get_args(): - parser = argparse.ArgumentParser() - parser.add_argument("--task", type=str, default="titanic") - parser.add_argument("--low_is_better", dest="low_is_better", action="store_true") - parser.set_defaults(low_is_better=False) - parser.add_argument("--load_tree", dest="load_tree", action="store_true") - parser.add_argument("--no_load_tree", dest="load_tree", action="store_false") - parser.set_defaults(load_tree=True) - parser.add_argument("--reflection", dest="reflection", action="store_true") - parser.add_argument("--no_reflection", dest="reflection", action="store_false") - parser.set_defaults(reflection=True) - parser.add_argument("--rollouts", type=int, default=3) - parser.add_argument("--name", type=str, default="") - return parser.parse_args() - - -data_config = load_data_config() - -if __name__ == "__main__": - args = get_args() - # requirement = generate_task_requirement(args.task, data_config) - # print(requirement) - - # role, root_node = initialize_di_root_node(requirement, data_config) - # asyncio.run(role.run(requirement)) - - # asyncio.run(root_node.run_node()) - mcts = MCTS(root_node=None, max_depth=5) - best_nodes = asyncio.run( - mcts.search( - args.task, - data_config, - low_is_better=args.low_is_better, - load_tree=args.load_tree, - reflection=args.reflection, - rollouts=args.rollouts, - name=args.name, - ) - ) - best_node = best_nodes["global_best"] - dev_best_node = best_nodes["dev_best"] - text, num_generated_codes = get_tree_text(mcts.root_node) - print(text) - print(f"Generated {num_generated_codes} unique codes.") - - with open(f"results/{args.task}_tree{args.name}.txt", "w") as f: - f.write(f"Generated {num_generated_codes} unique codes.\n") - f.write(f"Best node: {best_node}, score: {best_node.raw_reward}\n") - f.write(f"Dev best node: {dev_best_node}, score: {dev_best_node.raw_reward}\n") - f.write(text) From d27a48adb234dee1b45209b7c02deb0d4cff6a40 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 5 Sep 2024 13:01:00 +0800 Subject: [PATCH 028/135] update prompt to include dataset info path --- expo/dataset.py | 34 +++++++++++++++++++++++++--------- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/expo/dataset.py b/expo/dataset.py index fee1199a9..f7e0301b5 100644 --- a/expo/dataset.py +++ b/expo/dataset.py @@ -16,9 +16,8 @@ Report {metric} on the eval data. Do not plot or make any visualizations. """ -TASK_PROMPT = """\ -# User requirement -{user_requirement} + +DI_INSTRUCTION = """\ **Attention** 1. Please do not leak the target label in any form during training. 2. Dev and Test sets do not have the target column. @@ -39,14 +38,19 @@ print("Train score:", train_score) ``` +# Output dir +{output_dir} +""" + +TASK_PROMPT = """\ +# User requirement +{user_requirement} +{additional_instruction} # Data dir training (with labels): {train_path} dev (without labels): {dev_path} testing (without labels): {test_path} - -# Output dir -{output_dir} - +dataset description: {data_info_path} (You can use this file to get additional information about the dataset) """ @@ -132,7 +136,12 @@ def create_dataset_dict(dataset): return dataset_dict -def generate_task_requirement(task_name, data_config): +def generate_di_instruction(output_dir): + additional_instruction = DI_INSTRUCTION.format(output_dir=output_dir) + return additional_instruction + + +def generate_task_requirement(task_name, data_config, is_di=True): user_requirement = get_user_requirement(task_name, data_config) split_dataset_path = get_split_dataset_path(task_name, data_config) train_path = split_dataset_path["train"] @@ -140,12 +149,19 @@ def generate_task_requirement(task_name, data_config): test_path = split_dataset_path["test_wo_target"] work_dir = data_config["work_dir"] output_dir = f"{work_dir}/{task_name}" + datasets_dir = data_config["datasets_dir"] + data_info_path = f"{datasets_dir}/{task_name}/dataset_info.json" + if is_di: + additional_instruction = generate_di_instruction(output_dir) + else: + additional_instruction = "" user_requirement = TASK_PROMPT.format( user_requirement=user_requirement, train_path=train_path, dev_path=dev_path, test_path=test_path, - output_dir=output_dir, + additional_instruction=additional_instruction, + data_info_path=data_info_path, ) print(user_requirement) return user_requirement From 96ffcd285f13556182c9f5dfece10e563a79086b Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 5 Sep 2024 14:30:36 +0800 Subject: [PATCH 029/135] =?UTF-8?q?=E6=9B=B4=E6=96=B0Prompt=E7=9B=B8?= =?UTF-8?q?=E5=85=B3readme?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/README.md | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/expo/README.md b/expo/README.md index 4cc4daf25..eab3298dc 100644 --- a/expo/README.md +++ b/expo/README.md @@ -33,15 +33,17 @@ llm: 实验轮次 k = 10, 20 -### 提示词使用 +### Prompt Usage - 通过执行`dataset.py`中的`generate_task_requirement`函数获取提示词 -- 每一个数据集里有`dataset_info.json`，里面的内容需要提供给baselines以保证公平 + - 非DI-based方法设置`is_di=False` + - `data_config`用`utils.DATA_CONFIG` +- 每一个数据集里有`dataset_info.json`，里面的内容需要提供给baselines以保证公平（`generate_task_requirement`已经默认提供） ## 3. Evaluation -运行各个框架，运行后框架需要提供Dev和Test的`dev_predictions.csv`和`test_predictions.csv`， column name为target +运行各个框架，运行后框架需要提供Dev和Test的`dev_predictions.csv`和`test_predictions.csv`，每个csv文件只需要单个名为target的列 - 使用`CustomExperimenter` ``` @@ -62,11 +64,6 @@ score_dict = experimenter.evaluate_pred_files(dev_pred_path, test_pred_path) ``` pip install -U pip pip install -U setuptools wheel - -CPU version of pytorch has smaller footprint - see installation instructions in -pytorch documentation - https://pytorch.org/get-started/locally/ -pip install torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cpu - pip install autogluon ``` @@ -105,11 +102,11 @@ pip install -r requirements.txt #### Run -- `python run_experiment.py --exp_mode mcts --task titanic --rollout 5` +- `python run_experiment.py --exp_mode mcts --task titanic --rollout 10` If the dataset has reg metric, remember to use `--low_is_better`: -- `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 5 --low_is_better` +- `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 10 --low_is_better` From defca81ebbd84f6bdeb26a0ca3d06fefa3e0e830 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 10:24:49 +0800 Subject: [PATCH 030/135] fix time calculation --- expo/experimenter/experimenter.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 4161aef3d..29c4e380d 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -16,7 +16,8 @@ class Experimenter: def __init__(self, args, **kwargs): self.args = args - self.start_time = datetime.datetime.now().strftime("%Y%m%d%H%M") + 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=1, @@ -99,11 +100,12 @@ def evaluate(self, score_dict, state): return score_dict def save_result(self, result): - end_time = datetime.datetime.now().strftime("%Y%m%d%H%M") + end_time_raw = datetime.datetime.now() + end_time = end_time_raw.strftime("%Y%m%d%H%M") time_info = { "start_time": self.start_time, "end_time": end_time, - "duration (minutes)": float(end_time) - float(self.start_time), + "duration (seconds)": (end_time_raw - self.start_time_raw).seconds, } result = result.copy() result.insert(0, time_info) From 0e27e3d8be18f6e36600b169c9408de3c644a71c Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 10:31:01 +0800 Subject: [PATCH 031/135] =?UTF-8?q?=E4=BD=BF=E7=94=A8code=20block=E5=81=9A?= =?UTF-8?q?reflection?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- metagpt/actions/di/write_analysis_code.py | 9 ++++----- metagpt/prompts/di/write_analysis_code.py | 20 +++++++++++--------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/metagpt/actions/di/write_analysis_code.py b/metagpt/actions/di/write_analysis_code.py index 711e56d39..149543b4b 100644 --- a/metagpt/actions/di/write_analysis_code.py +++ b/metagpt/actions/di/write_analysis_code.py @@ -6,8 +6,6 @@ """ from __future__ import annotations -import json - from metagpt.actions import Action from metagpt.prompts.di.write_analysis_code import ( CHECK_DATA_PROMPT, @@ -30,9 +28,10 @@ async def _debug_with_reflection(self, context: list[Message], working_memory: l ) rsp = await self._aask(reflection_prompt, system_msgs=[REFLECTION_SYSTEM_MSG]) - reflection = json.loads(CodeParser.parse_code(block=None, text=rsp)) - - return reflection["improved_impl"] + # reflection = json.loads(CodeParser.parse_code(block=None, text=rsp)) + # return reflection["improved_impl"] + reflection = CodeParser.parse_code(block=None, text=rsp) + return reflection async def run( self, diff --git a/metagpt/prompts/di/write_analysis_code.py b/metagpt/prompts/di/write_analysis_code.py index beee80679..1b5ae9743 100644 --- a/metagpt/prompts/di/write_analysis_code.py +++ b/metagpt/prompts/di/write_analysis_code.py @@ -40,15 +40,17 @@ def add(a: int, b: int) -> int: assert add(1, 2) == 3 # output: -1 assert add(1, 3) == 4 # output: -2 -[reflection on previous impl]: +[reflection on previous impl] The implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input. -[improved impl]: +[improved impl] +```python def add(a: int, b: int) -> int: """ Given integers a and b, return the total value of a and b. """ return a + b +``` ''' REFLECTION_PROMPT = """ @@ -60,17 +62,17 @@ def add(a: int, b: int) -> int: [context] {context} -[previous impl]: +[previous impl] {previous_impl} [instruction] Analyze your previous code and error in [context] step by step, provide me with improved method and code. Remember to follow [context] requirement. Don't forget to write code for steps behind the error step. -Output a json following the format: -```json -{{ - "reflection": str = "Reflection on previous implementation", - "improved_impl": str = "Refined code after reflection (do not include nested code block here).", -}} +Output in the following format: +[reflection on previous impl] +... +[improved impl]: +```python +# your code ``` """ From e07ed0df8bcee81cd7889964d01a45cba5d92fd2 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 10:42:24 +0800 Subject: [PATCH 032/135] fix mcts bug --- expo/MCTS.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index b2ad824e5..365315330 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -191,6 +191,7 @@ def evaluate_prediction(self, split): def evaluate_simulation(self, score_dict): scores = {"dev_score": self.evaluate_prediction("dev"), "test_score": self.evaluate_prediction("test")} + scores["score"] = scores["dev_score"] score_dict.update(scores) return score_dict @@ -345,7 +346,7 @@ async def search(self, task, data_config, name, rollouts, load_tree=False, low_i if node.raw_value == 0: reward = await self.simulate(node) else: - reward = {"test_score": node.raw_value, "score": node.value} + reward = {"test_score": node.raw_value, "score": node.raw_reward["score"]} mcts_logger.log("MCTS", f"Terminal node's reward: {reward}") self.backpropagate(node, reward) else: From a6f71f449873eea1c1b3486e93d84c340279fada Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 13:09:18 +0800 Subject: [PATCH 033/135] 1. avoid circular reference 2. add greedy --- expo/Greedy.py | 9 +++++++++ expo/MCTS.py | 37 ++++++++++++++----------------------- 2 files changed, 23 insertions(+), 23 deletions(-) create mode 100644 expo/Greedy.py diff --git a/expo/Greedy.py b/expo/Greedy.py new file mode 100644 index 000000000..f6f60db01 --- /dev/null +++ b/expo/Greedy.py @@ -0,0 +1,9 @@ +from expo.MCTS import MCTS + + +class Greedy(MCTS): + def best_child(self): + if len(self.children) == 0: + return self.root_node + all_children = [child for children in self.children.values() for child in children] + return max(all_children, key=lambda x: x.normalized_reward.get("dev_score", 0)) diff --git a/expo/MCTS.py b/expo/MCTS.py index 365315330..3331f35fa 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -143,6 +143,7 @@ def save_new_role(self, role: ResearchAssistant): role.state_saved = False role.change_next_instruction(self.action) mcts_logger.log("MCTS", f"Saving new role: {role.node_id}") + role = role.model_copy() role.save_state(static_save=True) async def expand(self, max_children): @@ -165,18 +166,6 @@ async def expand(self, max_children): node.save_new_role(new_role) self.add_child(node) - # def evaluate_test(self): - # prediction_fpath = os.path.join(self.state["work_dir"], self.state["task"], "predictions.csv") - # predictions = pd.read_csv(prediction_fpath)["target"] - # # copy predictions.csv to the node_dir - # predictions_node_fpath = os.path.join(self.state["node_dir"], "Node-{self.id}-predictions.csv") - # predictions.to_csv(predictions_node_fpath, index=False) - # # load test_target.csv - # split_datasets_dir = self.state["datasets_dir"] - # gt = pd.read_csv(os.path.join(split_datasets_dir["test_target"]))["target"] - # metric = self.state["dataset_config"]["metric"] - # return evaluate_score(predictions, gt, metric) - def evaluate_prediction(self, split): pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}_predictions.csv") pred_node_path = os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}_predictions.csv") @@ -331,14 +320,10 @@ async def search(self, task, data_config, name, rollouts, load_tree=False, low_i self.children[root] = [] reward = await self.simulate(root, role) self.backpropagate(root, reward) - children = await self.expand(root) - # 目前是随机选择1个，后续可以改成多个 - first_leaf = random.choice(children) - reward = await self.simulate(first_leaf) - self.backpropagate(first_leaf, reward) + node, reward = await self.expand_and_simulate(root) + # self.backpropagate(node, reward) else: root = self.root_node - # 后续迭代：使用UCT进行选择，expand并模拟和反向传播 for _ in range(rollouts): # number of rollouts mcts_logger.log("MCTS", f"Start the next rollout {_+1}") node = self.select(root) @@ -350,13 +335,19 @@ async def search(self, task, data_config, name, rollouts, load_tree=False, low_i mcts_logger.log("MCTS", f"Terminal node's reward: {reward}") self.backpropagate(node, reward) else: - if node.visited > 0: - children = await self.expand(node) - node = random.choice(children) - reward = await self.simulate(node) - self.backpropagate(node, reward) + node, reward = await self.expand_and_simulate(node) + # self.backpropagate(node, reward) return self.best_path(root) + async def expand_and_simulate(self, node): + # Expand and randomly select a child node, then simulate it + if node.visited > 0: + children = await self.expand(node) + node = random.choice(children) + reward = await self.simulate(node) + self.backpropagate(node, reward) + return node, reward + def load_tree(self): def load_children_node(node): mcts_logger.log("MCTS", f"Load node {node.id}'s child: {node.children}") From 6d40ec463fce4b8d2eff34334bc54f2e611ed2b5 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 14:18:04 +0800 Subject: [PATCH 034/135] update result key --- expo/experimenter/experimenter.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 29c4e380d..e81c64701 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -71,10 +71,10 @@ async def run_experiment(self): 0, { "best_dev_score": best_dev_score, - "best_score_idx": best_score_idx, - "best_test_score": test_scores[best_score_idx], + "best_dev_score_idx": best_score_idx, + "best_dev_test_score": test_scores[best_score_idx], "avg_test_score": avg_score, - "best_score": global_best_score, + "global_best_test_score": global_best_score, }, ) self.save_result(results) From 376d1b7661889fae78ec7d883f1bb15d6a8b3fc1 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 17:02:49 +0800 Subject: [PATCH 035/135] allow new instruction even if there's no insights --- expo/insights/instruction_generator.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 065565c89..2cfee3107 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -85,8 +85,12 @@ async def generate_new_instructions(task_id, original_instruction, max_num, file if len(data) == 0: mcts_logger.log("MCTS", f"No insights available for task {task_id}") return [original_instruction] # Return the original instruction if no insights are available - for item in data[:max_num]: - insights = item["Analysis"] + for i in range(max_num): + if len(data) == 0: + insights = "No insights available" + else: + item = data[i] + insights = item["Analysis"] new_instruction = await InstructionGenerator.generate_new_instruction(original_instruction, insights) new_instructions.append(new_instruction) return new_instructions From c0262bcd8f1c8803da18d5a0c80bc0094e168ed2 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 19:05:10 +0800 Subject: [PATCH 036/135] 1. add support to hf dataset 2. add support to datasets that have both train and test 3. create data folder 4. fix new instruction bug --- expo/MCTS.py | 2 +- expo/{ => data}/dataset.py | 50 +++++++++++--------- expo/data/hf_data.py | 64 ++++++++++++++++++++++++++ expo/experimenter/mcts.py | 4 +- expo/insights/instruction_generator.py | 2 +- 5 files changed, 97 insertions(+), 25 deletions(-) rename expo/{ => data}/dataset.py (87%) create mode 100644 expo/data/hf_data.py diff --git a/expo/MCTS.py b/expo/MCTS.py index 3331f35fa..4090331cd 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -6,7 +6,7 @@ import numpy as np import pandas as pd -from expo.dataset import generate_task_requirement, get_split_dataset_path +from expo.data.dataset import generate_task_requirement, get_split_dataset_path from expo.evaluation.evaluation import evaluate_score from expo.insights.instruction_generator import InstructionGenerator from expo.research_assistant import ResearchAssistant diff --git a/expo/dataset.py b/expo/data/dataset.py similarity index 87% rename from expo/dataset.py rename to expo/data/dataset.py index f7e0301b5..21dc19519 100644 --- a/expo/dataset.py +++ b/expo/data/dataset.py @@ -86,6 +86,8 @@ ("07_icr-identify-age-related-conditions", "Class"), ] +DSAGENT_DATASETS = [("concrete-strength", "Strength"), ("smoker-status", "smoking"), ("software-defects", "defects")] + def get_split_dataset_path(dataset_name, config): datasets_dir = config["datasets_dir"] @@ -121,8 +123,8 @@ def get_user_requirement(task_name, config): ) -def save_datasets_dict_to_yaml(datasets_dict): - with open("datasets.yaml", "w") as file: +def save_datasets_dict_to_yaml(datasets_dict, name="datasets.yaml"): + with open(name, "w") as file: yaml.dump(datasets_dict, file) @@ -201,11 +203,15 @@ def check_datasetinfo_exists(self): def get_raw_dataset(self): raw_dir = Path(self.dataset_dir, self.name, "raw") + train_df = None + test_df = None if not os.path.exists(Path(raw_dir, "train.csv")): raise FileNotFoundError(f"Raw dataset `train.csv` not found in {raw_dir}") else: - df = pd.read_csv(Path(raw_dir, "train.csv")) - return df + train_df = pd.read_csv(Path(raw_dir, "train.csv")) + if os.path.exists(Path(raw_dir, "test.csv")): + test_df = pd.read_csv(Path(raw_dir, "test.csv")) + return train_df, test_df def get_dataset_info(self): raw_df = pd.read_csv(Path(self.dataset_dir, self.name, "raw", "train.csv")) @@ -249,10 +255,10 @@ def create_base_requirement(self): return req def save_dataset(self, target_col): - df = self.get_raw_dataset() + df, test_df = self.get_raw_dataset() if not self.check_dataset_exists() or self.force_update: print(f"Saving Dataset {self.name} in {self.dataset_dir}") - self.split_and_save(df, target_col) + self.split_and_save(df, target_col, test_df=test_df) else: print(f"Dataset {self.name} already exists") if not self.check_datasetinfo_exists() or self.force_update: @@ -278,10 +284,13 @@ def save_split_datasets(self, df, split, target_col=None): df_target = df_target.drop(columns=[target_col]) df_target.to_csv(Path(path, f"split_{split}_target.csv"), index=False) - def split_and_save(self, df, target_col): + def split_and_save(self, df, target_col, test_df=None): if not target_col: raise ValueError("Target column not provided") - train, test = train_test_split(df, test_size=1 - TRAIN_TEST_SPLIT, random_state=SEED) + if test_df is None: + train, test = train_test_split(df, test_size=1 - TRAIN_TEST_SPLIT, random_state=SEED) + else: + train = df train, dev = train_test_split(train, test_size=1 - TRAIN_DEV_SPLIT, random_state=SEED) self.save_split_datasets(train, "train") self.save_split_datasets(dev, "dev", target_col) @@ -304,7 +313,7 @@ def get_raw_dataset(self): raw_dir = Path(self.dataset_dir, self.name, "raw") os.makedirs(raw_dir, exist_ok=True) dataset_df.to_csv(Path(raw_dir, "train.csv"), index=False) - return dataset_df + return dataset_df, None def get_dataset_info(self): dataset_info = super().get_dataset_info() @@ -315,14 +324,9 @@ def get_dataset_info(self): return dataset_info -# class HFExpDataset(ExpDataset): -# def __init__(self, name, dataset_dir, dataset_name, **kwargs): -# super().__init__(name, dataset_dir, **kwargs) - - -async def process_dataset(dataset, solution_designer, save_analysis_pool, datasets_dict): +async def process_dataset(dataset, solution_designer: SolutionDesigner, save_analysis_pool, datasets_dict): if save_analysis_pool: - asyncio.run(solution_designer.generate_solutions(dataset.get_dataset_info(), dataset.name)) + await solution_designer.generate_solutions(dataset.get_dataset_info(), dataset.name) dataset_dict = create_dataset_dict(dataset) datasets_dict["datasets"][dataset.name] = dataset_dict @@ -330,14 +334,18 @@ async def process_dataset(dataset, solution_designer, save_analysis_pool, datase if __name__ == "__main__": datasets_dir = "D:/work/automl/datasets" force_update = False - save_analysis_pool = False + save_analysis_pool = True datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() - for dataset_id in OPENML_DATASET_IDS: - openml_dataset = OpenMLExpDataset("", datasets_dir, dataset_id, force_update=force_update) - asyncio.run(process_dataset(openml_dataset, solution_designer, save_analysis_pool, datasets_dict)) + # for dataset_id in OPENML_DATASET_IDS: + # openml_dataset = OpenMLExpDataset("", datasets_dir, dataset_id, force_update=force_update) + # asyncio.run(process_dataset(openml_dataset, solution_designer, save_analysis_pool, datasets_dict)) + + # for dataset_name, target_col in CUSTOM_DATASETS: + # custom_dataset = ExpDataset(dataset_name, datasets_dir, target_col=target_col, force_update=force_update) + # asyncio.run(process_dataset(custom_dataset, solution_designer, save_analysis_pool, datasets_dict)) - for dataset_name, target_col in CUSTOM_DATASETS: + for dataset_name, target_col in DSAGENT_DATASETS: custom_dataset = ExpDataset(dataset_name, datasets_dir, target_col=target_col, force_update=force_update) asyncio.run(process_dataset(custom_dataset, solution_designer, save_analysis_pool, datasets_dict)) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py new file mode 100644 index 000000000..a7e2a1afe --- /dev/null +++ b/expo/data/hf_data.py @@ -0,0 +1,64 @@ +import asyncio +import os +from pathlib import Path + +import pandas as pd +from datasets import load_dataset + +from expo.data.dataset import ExpDataset, process_dataset, save_datasets_dict_to_yaml +from expo.insights.solution_designer import SolutionDesigner + +HFDATSETS = [ + {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label"}, + {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label"}, + {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label"}, + {"name": "oxford-iiit-pet", "dataset_name": "timm/oxford-iiit-pet", "target_col": "label"}, + {"name": "stanford_cars", "dataset_name": "tanganke/stanford_cars", "target_col": "label"}, + {"name": "fashion_mnist", "dataset_name": "zalando-datasets/fashion_mnist", "target_col": "label"}, +] + + +class HFExpDataset(ExpDataset): + train_ratio = 0.6 + dev_ratio = 0.2 + test_ratio = 0.2 + + def __init__(self, name, dataset_dir, dataset_name, **kwargs): + self.name = name + self.dataset_dir = dataset_dir + self.dataset_name = dataset_name + self.target_col = kwargs.get("target_col", "label") + self.dataset = load_dataset(dataset_name) + super().__init__(self.name, dataset_dir, **kwargs) + + def get_raw_dataset(self): + raw_dir = Path(self.dataset_dir, self.name, "raw") + raw_dir.mkdir(parents=True, exist_ok=True) + if os.path.exists(Path(raw_dir, "train.csv")): + df = pd.read_csv(Path(raw_dir, "train.csv")) + else: + df = self.dataset["train"].to_pandas() + df.to_csv(Path(raw_dir, "train.csv")) + + if os.path.exists(Path(raw_dir, "test.csv")): + test_df = pd.read_csv(Path(raw_dir, "test.csv")) + else: + if "test" in self.dataset: + test_df = self.dataset["test"].to_pandas() + test_df.to_csv(Path(raw_dir, "test.csv")) + else: + test_df = None + return df, test_df + + +if __name__ == "__main__": + dataset_dir = "D:/work/automl/datasets" + save_analysis_pool = True + datasets_dict = {"datasets": {}} + solution_designer = SolutionDesigner() + for dataset_meta in HFDATSETS: + hf_dataset = HFExpDataset( + dataset_meta["name"], dataset_dir, dataset_meta["dataset_name"], target_col=dataset_meta["target_col"] + ) + asyncio.run(process_dataset(hf_dataset, solution_designer, save_analysis_pool, datasets_dict)) + save_datasets_dict_to_yaml(datasets_dict, "hf_datasets.yaml") diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 2805cae51..9db6e0807 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -22,8 +22,8 @@ async def run_experiment(self): text, num_generated_codes = get_tree_text(mcts.root_node) text += f"Generated {num_generated_codes} unique codes.\n" - text += f"Best node: {best_node}, score: {best_node.raw_reward}\n" - text += f"Dev best node: {dev_best_node}, score: {dev_best_node.raw_reward}\n" + text += f"Best node: {best_node.id}, score: {best_node.raw_reward}\n" + text += f"Dev best node: {dev_best_node.id}, score: {dev_best_node.raw_reward}\n" print(text) self.save_tree(text) diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 2cfee3107..c9ff7ec6e 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -84,7 +84,7 @@ async def generate_new_instructions(task_id, original_instruction, max_num, file new_instructions = [] if len(data) == 0: mcts_logger.log("MCTS", f"No insights available for task {task_id}") - return [original_instruction] # Return the original instruction if no insights are available + # return [original_instruction] # Return the original instruction if no insights are available for i in range(max_num): if len(data) == 0: insights = "No insights available" From df6fe9854d1b97ba613e161cba477cb181325413 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 6 Sep 2024 19:27:36 +0800 Subject: [PATCH 037/135] fix dataset bug --- expo/data/dataset.py | 1 + 1 file changed, 1 insertion(+) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 21dc19519..2efaf692b 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -291,6 +291,7 @@ def split_and_save(self, df, target_col, test_df=None): train, test = train_test_split(df, test_size=1 - TRAIN_TEST_SPLIT, random_state=SEED) else: train = df + test = test_df train, dev = train_test_split(train, test_size=1 - TRAIN_DEV_SPLIT, random_state=SEED) self.save_split_datasets(train, "train") self.save_split_datasets(dev, "dev", target_col) From 9728b3a891b502fb8f0d260f3d139b1ca3c5502a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 9 Sep 2024 13:44:38 +0800 Subject: [PATCH 038/135] add greedy to run_experiment; add save_notebook to experimenter.py --- expo/data/dataset.py | 6 +++--- expo/experimenter/aug.py | 2 +- expo/experimenter/experimenter.py | 7 ++++--- expo/experimenter/mcts.py | 10 +++++++++- expo/run_experiment.py | 4 +++- expo/utils.py | 12 ++++++++++-- 6 files changed, 30 insertions(+), 11 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 2efaf692b..4ac931d9f 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -111,12 +111,12 @@ def get_split_dataset_path(dataset_name, config): def get_user_requirement(task_name, config): - datasets_dir = config["datasets_dir"] + # datasets_dir = config["datasets_dir"] if task_name in config["datasets"]: dataset = config["datasets"][task_name] - data_path = os.path.join(datasets_dir, dataset["dataset"]) + # data_path = os.path.join(datasets_dir, dataset["dataset"]) user_requirement = dataset["user_requirement"] - return data_path, user_requirement + return user_requirement else: raise ValueError( f"Dataset {task_name} not found in config file. Available datasets: {config['datasets'].keys()}" diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 1bf927cc1..8312f57fc 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -34,7 +34,7 @@ async def run_experiment(self): di.role_dir = f"{di.role_dir}_{self.args.task}" requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) print(requirement) - score_dict = await self.run_di(di, requirement) + score_dict = await self.run_di(di, requirement, run_idx=i) results.append( { "idx": i, diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index e81c64701..b1b5a93c0 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -7,7 +7,7 @@ from expo.evaluation.evaluation import evaluate_score from expo.MCTS import create_initial_state from expo.research_assistant import ResearchAssistant -from expo.utils import DATA_CONFIG +from expo.utils import DATA_CONFIG, save_notebook class Experimenter: @@ -26,7 +26,7 @@ def __init__(self, args, **kwargs): name="", ) - async def run_di(self, di, user_requirement): + async def run_di(self, di, user_requirement, run_idx): max_retries = 3 num_runs = 1 run_finished = False @@ -39,6 +39,7 @@ async def run_di(self, di, user_requirement): except Exception as e: print(f"Error: {e}") num_runs += 1 + save_notebook(role=di, save_dir=self.result_path, name=f"{self.args.task}_{self.start_time}_{run_idx}") if not run_finished: score_dict = {"train_score": -1, "dev_score": -1, "test_score": -1, "score": -1} return score_dict @@ -50,7 +51,7 @@ async def run_experiment(self): for i in range(self.args.num_experiments): di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) - score_dict = await self.run_di(di, user_requirement) + score_dict = await self.run_di(di, user_requirement, run_idx=i) results.append( {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} ) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 9db6e0807..9bf7306c4 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -1,13 +1,21 @@ from expo.evaluation.visualize_mcts import get_tree_text from expo.experimenter.experimenter import Experimenter +from expo.Greedy import Greedy from expo.MCTS import MCTS class MCTSExperimenter(Experimenter): result_path: str = "results/mcts" + def __init__(self, args, greedy=False, **kwargs): + super().__init__(args, **kwargs) + self.greedy = greedy + async def run_experiment(self): - mcts = MCTS(root_node=None, max_depth=5) + if self.greedy: + mcts = Greedy(root_node=None, max_depth=5) + else: + mcts = MCTS(root_node=None, max_depth=5) best_nodes = await mcts.search( self.args.task, self.data_config, diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 8871c04a6..83237741a 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -10,7 +10,7 @@ def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--name", type=str, default="") - parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom"]) + parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy"]) get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) @@ -41,6 +41,8 @@ def get_di_args(parser): async def main(args): if args.exp_mode == "mcts": experimenter = MCTSExperimenter(args) + elif args.exp_mode == "greedy": + experimenter = MCTSExperimenter(args, greedy=True) elif args.exp_mode == "aug": experimenter = AugExperimenter(args) elif args.exp_mode == "base": diff --git a/expo/utils.py b/expo/utils.py index d67ceb5a1..65701c3ec 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -7,8 +7,7 @@ import nbformat import yaml from loguru import logger as _logger - -# from nbclient import NotebookClient +from nbclient import NotebookClient from nbformat.notebooknode import NotebookNode from metagpt.roles.role import Role @@ -92,15 +91,24 @@ def process_cells(nb: NotebookNode) -> NotebookNode: def save_notebook(role: Role, save_dir: str = "", name: str = ""): save_dir = Path(save_dir) + tasks = role.planner.plan.tasks + codes = [task.code for task in tasks if task.code] + clean_nb = nbformat.v4.new_notebook() + for code in codes: + clean_nb.cells.append(nbformat.v4.new_code_cell(code)) nb = process_cells(role.execute_code.nb) file_path = save_dir / f"{name}.ipynb" + clean_file_path = save_dir / f"{name}_clean.ipynb" nbformat.write(nb, file_path) + nbformat.write(clean_nb, clean_file_path) async def load_execute_notebook(role): tasks = role.planner.plan.tasks codes = [task.code for task in tasks if task.code] executor = role.execute_code + executor.nb = nbformat.v4.new_notebook() + executor.nb.client = NotebookClient(executor.nb) # await executor.build() for code in codes: outputs, success = await executor.run(code) From 72dd44ae3295830c72c604748c85226f91415b1b Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 9 Sep 2024 13:47:59 +0800 Subject: [PATCH 039/135] add ds agent's datasets --- expo/datasets.yaml | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/expo/datasets.yaml b/expo/datasets.yaml index 8c28b03ca..45150833a 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -151,3 +151,28 @@ datasets: \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ \ eval data. Do not plot or make any visualizations.\n" + concrete-strength: + dataset: concrete-strength + metric: rmse + target_col: Strength + user_requirement: "This is a concrete-strength dataset. Your goal is to predict\ + \ the target column `Strength`.\nPerform data analysis, data preprocessing,\ + \ feature engineering, and modeling to predict the target. \nReport rmse on\ + \ the eval data. Do not plot or make any visualizations.\n" + smoker-status: + dataset: smoker-status + metric: f1 + target_col: smoking + user_requirement: "This is a smoker-status dataset. Your goal is to predict the\ + \ target column `smoking`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + \ Do not plot or make any visualizations.\n" + software-defects: + dataset: software-defects + metric: f1 + target_col: defects + user_requirement: "This is a software-defects dataset. Your goal is to predict\ + \ the target column `defects`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + \ Do not plot or make any visualizations.\n" + From 93ff1f8f2bcf3f7304cd2282cfe17383a39532e4 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 9 Sep 2024 13:50:36 +0800 Subject: [PATCH 040/135] update datasets.yaml --- expo/datasets.yaml | 2 +- expo/utils.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/expo/datasets.yaml b/expo/datasets.yaml index 45150833a..512cbc292 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -151,7 +151,7 @@ datasets: \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ \ eval data. Do not plot or make any visualizations.\n" - concrete-strength: + concrete-strength: dataset: concrete-strength metric: rmse target_col: Strength diff --git a/expo/utils.py b/expo/utils.py index 65701c3ec..9c6295fa9 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -19,7 +19,9 @@ def load_data_config(file_path="data.yaml"): return data_config +DATASET_CONFIG = load_data_config("datasets.yaml") DATA_CONFIG = load_data_config() +DATA_CONFIG["datasets"].update(DATASET_CONFIG["datasets"]) def get_mcts_logger(): From 401ca97846b91a97629c4a8144b29c44d79fe1bb Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 9 Sep 2024 13:54:09 +0800 Subject: [PATCH 041/135] update dataset.py --- expo/data/dataset.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 4ac931d9f..8bcce0b1a 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -338,13 +338,13 @@ async def process_dataset(dataset, solution_designer: SolutionDesigner, save_ana save_analysis_pool = True datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() - # for dataset_id in OPENML_DATASET_IDS: - # openml_dataset = OpenMLExpDataset("", datasets_dir, dataset_id, force_update=force_update) - # asyncio.run(process_dataset(openml_dataset, solution_designer, save_analysis_pool, datasets_dict)) + for dataset_id in OPENML_DATASET_IDS: + openml_dataset = OpenMLExpDataset("", datasets_dir, dataset_id, force_update=force_update) + asyncio.run(process_dataset(openml_dataset, solution_designer, save_analysis_pool, datasets_dict)) - # for dataset_name, target_col in CUSTOM_DATASETS: - # custom_dataset = ExpDataset(dataset_name, datasets_dir, target_col=target_col, force_update=force_update) - # asyncio.run(process_dataset(custom_dataset, solution_designer, save_analysis_pool, datasets_dict)) + for dataset_name, target_col in CUSTOM_DATASETS: + custom_dataset = ExpDataset(dataset_name, datasets_dir, target_col=target_col, force_update=force_update) + asyncio.run(process_dataset(custom_dataset, solution_designer, save_analysis_pool, datasets_dict)) for dataset_name, target_col in DSAGENT_DATASETS: custom_dataset = ExpDataset(dataset_name, datasets_dir, target_col=target_col, force_update=force_update) From 9ba0d217fc25ae4732394173a1c3626de5679128 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 9 Sep 2024 14:52:25 +0800 Subject: [PATCH 042/135] update mcts logic --- expo/MCTS.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 4090331cd..360baac8d 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -279,7 +279,8 @@ def backpropagate(self, node: Node, reward): def best_path(self, root: Node): best_child = root - best_score = 0 + global_best_score = root.normalized_reward["test_score"] + dev_best_score = root.normalized_reward["dev_score"] def bfs(node: Node, best_score, best_child: Node, split): assert split in ["test_score", "dev_score"] @@ -294,10 +295,10 @@ def bfs(node: Node, best_score, best_child: Node, split): best_score, best_child = bfs(child, best_score, best_child, split) return best_score, best_child - _, best_child = bfs(root, best_score, best_child, "test_score") - _, dev_best_child = bfs(root, best_score, best_child, "dev_score") + _, global_best_child = bfs(root, global_best_score, best_child, "test_score") + _, dev_best_child = bfs(root, dev_best_score, best_child, "dev_score") - return {"dev_best": dev_best_child, "global_best": best_child} + return {"dev_best": dev_best_child, "global_best": global_best_child} def get_num_simulations(self): return self.root_node.visited From 294d0fe70968ae66ff45200da3569e90c127951a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 9 Sep 2024 16:59:39 +0800 Subject: [PATCH 043/135] fix nbclient --- expo/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/utils.py b/expo/utils.py index 9c6295fa9..270842e41 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -110,7 +110,7 @@ async def load_execute_notebook(role): codes = [task.code for task in tasks if task.code] executor = role.execute_code executor.nb = nbformat.v4.new_notebook() - executor.nb.client = NotebookClient(executor.nb) + executor.nb_client = NotebookClient(executor.nb) # await executor.build() for code in codes: outputs, success = await executor.run(code) From af41f1f1cffb5a6da3429ed39d77e6d5cb507741 Mon Sep 17 00:00:00 2001 From: duyipan Date: Mon, 9 Sep 2024 11:26:23 +0000 Subject: [PATCH 044/135] Update README.md add aide setup and run --- expo/README.md | 146 ++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 145 insertions(+), 1 deletion(-) diff --git a/expo/README.md b/expo/README.md index eab3298dc..0d47ab3e4 100644 --- a/expo/README.md +++ b/expo/README.md @@ -57,7 +57,151 @@ score_dict = experimenter.evaluate_pred_files(dev_pred_path, test_pred_path) ### AIDE -提供github链接，并说明使用的命令以及参数设置 + +#### Setup + +``` +git clone https://github.com/WecoAI/aideml.git +``` + +修改 `aideml/aide/utils/config.yaml` 内容如下 + +```yaml +# path to the task data directory +data_dir: null + +# either provide a path to a plaintext file describing the task +desc_file: null +# or provide the task goal (and optionally evaluation information) as arguments +goal: null +eval: null + +log_dir: logs +workspace_dir: workspaces + +# whether to unzip any archives in the data directory +preprocess_data: True +# whether to copy the data to the workspace directory (otherwise it will be symlinked) +# copying is recommended to prevent the agent from accidentally modifying the original data +copy_data: True + +exp_name: null # a random experiment name will be generated if not provided + +# settings for code execution +exec: + timeout: 3600 + agent_file_name: runfile.py + format_tb_ipython: False + +# agent hyperparams +agent: + # how many improvement iterations to run + steps: 10 + # whether to instruct the agent to use CV (set to 1 to disable) + k_fold_validation: 1 + # whether to instruct the agent to generate a prediction function + expose_prediction: False + # whether to provide the agent with a preview of the data + data_preview: True + + # LLM settings for coding + code: + model: deepseek-coder + temp: 0.5 + + # LLM settings for evaluating program output / tracebacks + feedback: + model: deepseek-coder + temp: 0.5 + + # hyperparameters for the tree search + search: + max_debug_depth: 3 + debug_prob: 0.5 + num_drafts: 5 +``` + +由于 deepseek 完全兼容 OpenAI 的 API，修改`base_url`为`自己的url`，`api_key`为`自己的key`即可 + +``` +export OPENAI_API_KEY="自己的key" +export OPENAI_BASE_URL="自己的url" +``` + +修改`aideml/aide/backend/__init__.py` 30 行内容如下： + +```python +model_kwargs = model_kwargs | { + "model": model, + "temperature": temperature, + "max_tokens": max_tokens, + } + if "claude-" in model: + query_func = backend_anthropic.query + else: + query_func = backend_openai.query +``` + +由于 deepseekV2.5 不再支持 system message 使用 function call，修改 `aideml/aide/agent.py` 312 行内容如下： + +```python +response = cast( + dict, + query( + system_message=None, + user_message=prompt, + func_spec=review_func_spec, + model=self.acfg.feedback.model, + temperature=self.acfg.feedback.temp, + ), + ) +``` + +修改完后 + +``` +cd aideml +pip install -e . +``` + +#### Run + +运行下面脚本获取运行结果，在当前目录下将生成一个 log 文件夹以及 workspace 文件夹 +log 文件夹中将包含实验使用配置以及生成方案记录，workspace 文件夹下将保存 aide 最后生成的结果文件 + +```python +import aide +import os +import time + +os.environ["OPENAI_API_KEY"] = "sk-xxx" +os.environ["OPENAI_BASE_URL"] = "your url" +start_time = time.time() +data_dir = "xxx/data/titanic" +goal = f""" +# User requirement +({data_dir}, 'This is a 04_titanic dataset. Your goal is to predict the target column `Survived`.\nPerform data analysis, data preprocessing, feature engineering, and modeling to predict the target. \nReport f1 on the eval data. Do not plot or make any visualizations.\n') + +# Data dir +training (with labels): train.csv +testing (without labels): test.csv +dataset description: dataset_info.json (You can use this file to get additional information about the dataset)""" + +exp = aide.Experiment( + data_dir=data_dir, # replace this with your own directory + goal=goal, + eval="f1", # replace with your own evaluation metric +) + +best_solution = exp.run(steps=10) + +print(f"Best solution has validation metric: {best_solution.valid_metric}") +print(f"Best solution code: {best_solution.code}") +end_time = time.time() +execution_time = end_time - start_time + +print(f"run time : {execution_time} seconds") +``` ### Autogluon #### Setup From 60e8e3eab8f41b52a03137bfb8f8c99be9bf926b Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 10 Sep 2024 13:51:54 +0800 Subject: [PATCH 045/135] fix hfdataset; make dirs when save notebook --- expo/data/hf_data.py | 9 ++++++--- expo/utils.py | 1 + 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index a7e2a1afe..9ed2b2c48 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -38,18 +38,21 @@ def get_raw_dataset(self): df = pd.read_csv(Path(raw_dir, "train.csv")) else: df = self.dataset["train"].to_pandas() - df.to_csv(Path(raw_dir, "train.csv")) + df.to_csv(Path(raw_dir, "train.csv"), index=False) if os.path.exists(Path(raw_dir, "test.csv")): - test_df = pd.read_csv(Path(raw_dir, "test.csv")) + test_df = pd.read_csv(Path(raw_dir, "test.csv"), index=False) else: if "test" in self.dataset: test_df = self.dataset["test"].to_pandas() - test_df.to_csv(Path(raw_dir, "test.csv")) + test_df.to_csv(Path(raw_dir, "test.csv"), index=False) else: test_df = None return df, test_df + # def get_df_head(self, raw_df): + # return raw_df.head() + if __name__ == "__main__": dataset_dir = "D:/work/automl/datasets" diff --git a/expo/utils.py b/expo/utils.py index 270842e41..44de8cf9b 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -99,6 +99,7 @@ def save_notebook(role: Role, save_dir: str = "", name: str = ""): for code in codes: clean_nb.cells.append(nbformat.v4.new_code_cell(code)) nb = process_cells(role.execute_code.nb) + os.makedirs(save_dir, exist_ok=True) file_path = save_dir / f"{name}.ipynb" clean_file_path = save_dir / f"{name}_clean.ipynb" nbformat.write(nb, file_path) From d34a482faf0ec5478f9dad0291f65e6a85931006 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 10 Sep 2024 14:11:47 +0800 Subject: [PATCH 046/135] give dev label --- expo/data/dataset.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 8bcce0b1a..88528eb5c 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -20,23 +20,19 @@ DI_INSTRUCTION = """\ **Attention** 1. Please do not leak the target label in any form during training. -2. Dev and Test sets do not have the target column. +2. Test set does not have the target column. 3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). 4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. +5. You could split the training set further to make cross-validation and hyperparameter tuning. ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. - Both files should contain a single column named `target` with the predicted values. 2. Make sure the prediction results are in the same format as the target column in the training set. -- The labels should be transformed back to the original format if any transformation was applied during training. -## Output Training Set Performance +## Output Performance Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. -Print the training set performance in the last step. Write in this format: -```python -... -print("Train score:", train_score) -``` +Print the training set performance in the last step. # Output dir {output_dir} @@ -48,7 +44,7 @@ {additional_instruction} # Data dir training (with labels): {train_path} -dev (without labels): {dev_path} +dev (with labels): {dev_path} testing (without labels): {test_path} dataset description: {data_info_path} (You can use this file to get additional information about the dataset) """ @@ -147,7 +143,7 @@ def generate_task_requirement(task_name, data_config, is_di=True): user_requirement = get_user_requirement(task_name, data_config) split_dataset_path = get_split_dataset_path(task_name, data_config) train_path = split_dataset_path["train"] - dev_path = split_dataset_path["dev_wo_target"] + dev_path = split_dataset_path["dev"] test_path = split_dataset_path["test_wo_target"] work_dir = data_config["work_dir"] output_dir = f"{work_dir}/{task_name}" @@ -225,7 +221,7 @@ def get_dataset_info(self): "NumberOfSymbolicFeatures": raw_df.select_dtypes(include=["object"]).shape[1], } - df_head_text = raw_df.head().to_string(index=False) + df_head_text = self.get_df_head(raw_df) dataset_info = { "name": self.name, @@ -236,6 +232,9 @@ def get_dataset_info(self): } return dataset_info + def get_df_head(self, raw_df): + return raw_df.head().to_string(index=False) + def get_metric(self): dataset_info = self.get_dataset_info() num_classes = dataset_info["metadata"]["NumberOfClasses"] From e3663f2322b9ad97e4566b08f1dca53fedd70d0f Mon Sep 17 00:00:00 2001 From: Rayhao Date: Mon, 9 Sep 2024 23:21:56 -0700 Subject: [PATCH 047/135] implement autuglu exp --- .gitignore | 1 + expo/experimenter/autogluon.py | 35 ++++++++++++++++++++++++---------- expo/experimenter/custom.py | 2 +- expo/run_experiment.py | 5 ++++- 4 files changed, 31 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index 3fc66cecb..6e1fc7f74 100644 --- a/.gitignore +++ b/.gitignore @@ -29,6 +29,7 @@ share/python-wheels/ MANIFEST metagpt/tools/schemas/ examples/data/search_kb/*.json +expo/AutogluonModels # PyInstaller # Usually these files are written by a python scripts from a template diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index 4f5d151ef..b33411773 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -1,27 +1,29 @@ +from datetime import datetime from autogluon.tabular import TabularDataset, TabularPredictor - from expo.experimenter.custom import CustomExperimenter class AGRunner: preset = "best_quality" - time_limit = 500 + time_limit = 1000 # 1000s - def __init__(self, datasets): - self.datasets = datasets + def __init__(self, state=None): + self.state = state + self.datasets = self.state["datasets_dir"] def run(self): train_path = self.datasets["train"] - test_wo_target_path = self.datasets["test_wo_target"] dev_wo_target_path = self.datasets["dev_wo_target"] + test_wo_target_path = self.datasets["test_wo_target"] target_col = self.state["dataset_config"]["target_col"] train_data = TabularDataset(train_path) - test_data = TabularDataset(test_wo_target_path) dev_data = TabularDataset(dev_wo_target_path) - - predictor = TabularPredictor(label=target_col).fit(train_data, presets=self.preset, time_limit=self.time_limit) - test_preds = predictor.predict(test_data) + test_data = TabularDataset(test_wo_target_path) + eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_") + # predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, presets=self.preset, time_limit=self.time_limit, fit_weighted_ensemble=False, num_gpus=1) + predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, num_gpus=1) dev_preds = predictor.predict(dev_data) + test_preds = predictor.predict(test_data) return {"test_preds": test_preds, "dev_preds": dev_preds} @@ -30,4 +32,17 @@ class GluonExperimenter(CustomExperimenter): def __init__(self, args, **kwargs): super().__init__(args, **kwargs) - self.framework = AGRunner(self.datasets) + self.framework = AGRunner(self.state) + + def run_experiment(self): + result = self.framework.run() + user_requirement = self.state["requirement"] + dev_preds = result["dev_preds"] + test_preds = result["test_preds"] + score_dict = { + "dev_score": self.evaluate_predictions(dev_preds, "dev"), + "test_score": self.evaluate_predictions(test_preds, "test"), + } + results = [0, {"score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)}] + self.save_result(results) + return results \ No newline at end of file diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py index 4a5486af0..df090fb58 100644 --- a/expo/experimenter/custom.py +++ b/expo/experimenter/custom.py @@ -12,7 +12,7 @@ class CustomExperimenter(Experimenter): def __init__(self, args, **kwargs): super().__init__(args, **kwargs) - self.framework = kwargs["framework"] # todo + self.framework = kwargs.get("framework", None) # todo self.task = kwargs.get("task", self.args.task) self.low_is_better = kwargs.get("low_is_better", self.args.low_is_better) self.name = kwargs.get("name", "") diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 83237741a..74f9c6e57 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -5,12 +5,13 @@ from expo.experimenter.custom import CustomExperimenter from expo.experimenter.experimenter import Experimenter from expo.experimenter.mcts import MCTSExperimenter +from expo.experimenter.autogluon import GluonExperimenter def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--name", type=str, default="") - parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy"]) + parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy", "autoglu"]) get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) @@ -47,6 +48,8 @@ async def main(args): experimenter = AugExperimenter(args) elif args.exp_mode == "base": experimenter = Experimenter(args) + elif args.exp_mode == "autoglu": + experimenter = GluonExperimenter(args) elif args.exp_mode == "custom": experimenter = CustomExperimenter(args) else: From b776c7309bc64c0ec62c8efa4acbe93fdbd1a75f Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 10 Sep 2024 15:30:23 +0800 Subject: [PATCH 048/135] add random tree search --- expo/Greedy.py | 10 ++++++++++ expo/data/dataset.py | 2 +- expo/experimenter/mcts.py | 10 ++++++---- expo/run_experiment.py | 8 ++++++-- 4 files changed, 23 insertions(+), 7 deletions(-) diff --git a/expo/Greedy.py b/expo/Greedy.py index f6f60db01..8c8d865cd 100644 --- a/expo/Greedy.py +++ b/expo/Greedy.py @@ -1,3 +1,5 @@ +import random + from expo.MCTS import MCTS @@ -7,3 +9,11 @@ def best_child(self): return self.root_node all_children = [child for children in self.children.values() for child in children] return max(all_children, key=lambda x: x.normalized_reward.get("dev_score", 0)) + + +class Random(MCTS): + def best_child(self): + if len(self.children) == 0: + return self.root_node + all_children = [child for children in self.children.values() for child in children] + return random.choice(all_children) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 88528eb5c..43ac8ee0d 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -23,7 +23,7 @@ 2. Test set does not have the target column. 3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). 4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. -5. You could split the training set further to make cross-validation and hyperparameter tuning. +5. You could utilize dev set to improve the model. ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 9bf7306c4..fbe2f35f1 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -1,19 +1,21 @@ from expo.evaluation.visualize_mcts import get_tree_text from expo.experimenter.experimenter import Experimenter -from expo.Greedy import Greedy +from expo.Greedy import Greedy, Random from expo.MCTS import MCTS class MCTSExperimenter(Experimenter): result_path: str = "results/mcts" - def __init__(self, args, greedy=False, **kwargs): + def __init__(self, args, tree_mode=None, **kwargs): super().__init__(args, **kwargs) - self.greedy = greedy + self.tree_mode = tree_mode async def run_experiment(self): - if self.greedy: + if self.tree_mode == "greedy": mcts = Greedy(root_node=None, max_depth=5) + elif self.tree_mode == "random": + mcts = Random(root_node=None, max_depth=5) else: mcts = MCTS(root_node=None, max_depth=5) best_nodes = await mcts.search( diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 83237741a..b68607d79 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -10,7 +10,9 @@ def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--name", type=str, default="") - parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy"]) + parser.add_argument( + "--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy", "random"] + ) get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) @@ -42,7 +44,9 @@ async def main(args): if args.exp_mode == "mcts": experimenter = MCTSExperimenter(args) elif args.exp_mode == "greedy": - experimenter = MCTSExperimenter(args, greedy=True) + experimenter = MCTSExperimenter(args, tree_mode="greedy") + elif args.exp_mode == "random": + experimenter = MCTSExperimenter(args, tree_mode="random") elif args.exp_mode == "aug": experimenter = AugExperimenter(args) elif args.exp_mode == "base": From a373e684aec59a70270c8be99c721dd71a0c4de3 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 10 Sep 2024 16:05:22 +0800 Subject: [PATCH 049/135] update di instruction --- expo/data/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 43ac8ee0d..510c39fce 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -23,7 +23,7 @@ 2. Test set does not have the target column. 3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). 4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. -5. You could utilize dev set to improve the model. +5. You could utilize dev set to improve model training. ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. @@ -46,7 +46,7 @@ training (with labels): {train_path} dev (with labels): {dev_path} testing (without labels): {test_path} -dataset description: {data_info_path} (You can use this file to get additional information about the dataset) +dataset description: {data_info_path} (During EDA, you can use this file to get additional information about the dataset) """ From 923109e88295b9185ede4c8093afd8d965e8f9a7 Mon Sep 17 00:00:00 2001 From: duyipan Date: Tue, 10 Sep 2024 16:27:29 +0800 Subject: [PATCH 050/135] add aide.py update README --- expo/README.md | 34 ++-------------------------------- expo/experimenter/aide.py | 31 +++++++++++++++++++++++++++++++ 2 files changed, 33 insertions(+), 32 deletions(-) create mode 100644 expo/experimenter/aide.py diff --git a/expo/README.md b/expo/README.md index 0d47ab3e4..55ea7eed4 100644 --- a/expo/README.md +++ b/expo/README.md @@ -169,38 +169,8 @@ pip install -e . 运行下面脚本获取运行结果，在当前目录下将生成一个 log 文件夹以及 workspace 文件夹 log 文件夹中将包含实验使用配置以及生成方案记录，workspace 文件夹下将保存 aide 最后生成的结果文件 -```python -import aide -import os -import time - -os.environ["OPENAI_API_KEY"] = "sk-xxx" -os.environ["OPENAI_BASE_URL"] = "your url" -start_time = time.time() -data_dir = "xxx/data/titanic" -goal = f""" -# User requirement -({data_dir}, 'This is a 04_titanic dataset. Your goal is to predict the target column `Survived`.\nPerform data analysis, data preprocessing, feature engineering, and modeling to predict the target. \nReport f1 on the eval data. Do not plot or make any visualizations.\n') - -# Data dir -training (with labels): train.csv -testing (without labels): test.csv -dataset description: dataset_info.json (You can use this file to get additional information about the dataset)""" - -exp = aide.Experiment( - data_dir=data_dir, # replace this with your own directory - goal=goal, - eval="f1", # replace with your own evaluation metric -) - -best_solution = exp.run(steps=10) - -print(f"Best solution has validation metric: {best_solution.valid_metric}") -print(f"Best solution code: {best_solution.code}") -end_time = time.time() -execution_time = end_time - start_time - -print(f"run time : {execution_time} seconds") +``` +python experimenter/aide.py ``` ### Autogluon diff --git a/expo/experimenter/aide.py b/expo/experimenter/aide.py new file mode 100644 index 000000000..fb71dbdab --- /dev/null +++ b/expo/experimenter/aide.py @@ -0,0 +1,31 @@ +import aide +import os +import time + +os.environ["OPENAI_API_KEY"] = "sk-xxx" +os.environ["OPENAI_BASE_URL"] = "your url" +start_time = time.time() +data_dir = "xxx/data/titanic" +goal = f""" +# User requirement +({data_dir}, 'This is a 04_titanic dataset. Your goal is to predict the target column `Survived`.\nPerform data analysis, data preprocessing, feature engineering, and modeling to predict the target. \nReport f1 on the eval data. Do not plot or make any visualizations.\n') + +# Data dir +training (with labels): train.csv +testing (without labels): test.csv +dataset description: dataset_info.json (You can use this file to get additional information about the dataset)""" + +exp = aide.Experiment( + data_dir=data_dir, # replace this with your own directory + goal=goal, + eval="f1", # replace with your own evaluation metric +) + +best_solution = exp.run(steps=10) + +print(f"Best solution has validation metric: {best_solution.valid_metric}") +print(f"Best solution code: {best_solution.code}") +end_time = time.time() +execution_time = end_time - start_time + +print(f"run time : {execution_time} seconds") \ No newline at end of file From a8ee20843b7492743c3fcb434c3423b130447ead Mon Sep 17 00:00:00 2001 From: Rayhao Date: Tue, 10 Sep 2024 19:20:24 -0700 Subject: [PATCH 051/135] autogluon small fix --- expo/experimenter/autogluon.py | 5 ++--- expo/run_experiment.py | 4 ++-- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index b33411773..478ecfc01 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -34,7 +34,7 @@ def __init__(self, args, **kwargs): super().__init__(args, **kwargs) self.framework = AGRunner(self.state) - def run_experiment(self): + async def run_experiment(self): result = self.framework.run() user_requirement = self.state["requirement"] dev_preds = result["dev_preds"] @@ -44,5 +44,4 @@ def run_experiment(self): "test_score": self.evaluate_predictions(test_preds, "test"), } results = [0, {"score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)}] - self.save_result(results) - return results \ No newline at end of file + self.save_result(results) \ No newline at end of file diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 74f9c6e57..cfdd295b2 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -11,7 +11,7 @@ def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--name", type=str, default="") - parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy", "autoglu"]) + parser.add_argument("--exp_mode", type=str, default="mcts", choices=["mcts", "aug", "base", "custom", "greedy", "autogluon"]) get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) @@ -48,7 +48,7 @@ async def main(args): experimenter = AugExperimenter(args) elif args.exp_mode == "base": experimenter = Experimenter(args) - elif args.exp_mode == "autoglu": + elif args.exp_mode == "autogluon": experimenter = GluonExperimenter(args) elif args.exp_mode == "custom": experimenter = CustomExperimenter(args) From 35b9ea097e26784c16c638e5f839c754739ebca1 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 11 Sep 2024 11:59:52 +0800 Subject: [PATCH 052/135] update di instruction --- expo/data/dataset.py | 1 + 1 file changed, 1 insertion(+) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 510c39fce..c83f7b926 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -24,6 +24,7 @@ 3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). 4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. 5. You could utilize dev set to improve model training. +6. Use techniques to avoid overfitting. ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. From c91b2ada88036f8d2575e023e53fa895467478ad Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 11 Sep 2024 13:54:41 +0800 Subject: [PATCH 053/135] remove unnecessary prompt instruction --- expo/data/dataset.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index c83f7b926..1494eb267 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -23,7 +23,7 @@ 2. Test set does not have the target column. 3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). 4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. -5. You could utilize dev set to improve model training. +5. You could utilize dev set to validate and improve model training. 6. Use techniques to avoid overfitting. ## Saving Dev and Test Predictions @@ -32,8 +32,7 @@ 2. Make sure the prediction results are in the same format as the target column in the training set. ## Output Performance -Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number. -Print the training set performance in the last step. +Print the train and dev set performance in the last step. # Output dir {output_dir} @@ -44,9 +43,9 @@ {user_requirement} {additional_instruction} # Data dir -training (with labels): {train_path} -dev (with labels): {dev_path} -testing (without labels): {test_path} +train set (with labels): {train_path} +dev set (with labels): {dev_path} +test set (without labels): {test_path} dataset description: {data_info_path} (During EDA, you can use this file to get additional information about the dataset) """ From eaf1b62343f1aade858ddc82255b7d9380ee51ef Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 11 Sep 2024 16:14:50 +0800 Subject: [PATCH 054/135] add timeout to client --- expo/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/utils.py b/expo/utils.py index 44de8cf9b..f3c0c392d 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -111,7 +111,7 @@ async def load_execute_notebook(role): codes = [task.code for task in tasks if task.code] executor = role.execute_code executor.nb = nbformat.v4.new_notebook() - executor.nb_client = NotebookClient(executor.nb) + executor.nb_client = NotebookClient(executor.nb, timeout=executor.timeout) # await executor.build() for code in codes: outputs, success = await executor.run(code) From 318db0785c3063b8a39a8f10a15d808cc3968277 Mon Sep 17 00:00:00 2001 From: charles_zhang <26547-charles_zhang@users.noreply.gitlab.aicrowd.com> Date: Wed, 11 Sep 2024 21:12:36 +0800 Subject: [PATCH 055/135] add: ds-agent setup --- expo/README.md | 38 +++++++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/expo/README.md b/expo/README.md index 55ea7eed4..4dc1341e6 100644 --- a/expo/README.md +++ b/expo/README.md @@ -53,8 +53,44 @@ score_dict = experimenter.evaluate_pred_files(dev_pred_path, test_pred_path) ## 4. Baselines ### DS Agent -提供github链接，并说明使用的命令以及参数设置 +``` +git clone https://github.com/guosyjlu/DS-Agent.git +``` +将其deployment/generate.py line46-48行部分修改如下（目的是用deepseek而非GPT的API）： +```python +messages = [{"role": "user", "content": prompt}] + +if 'gpt' in llm: + response = openai.ChatCompletion.create(**{"messages": messages,**raw_request}) + raw_completion = response["choices"][0]["message"]["content"] + +elif llm == 'deepseek-coder': + from openai import OpenAI + client = OpenAI( + api_key="yours", + base_url="https://oneapi.deepwisdom.ai/v1" + ) + response = client.chat.completions.create( + model="deepseek-coder", + messages=[ + # {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": prompt}, + ], + temperature=temperature, + stream=False + ) + raw_completion = response.choices[0].message.content + +completion = raw_completion.split("```python")[1].split("```")[0] +``` + +修改完后在新建一个`deployment/test.sh` 分别运行下列两行，`$TASK` 是你要测试的task name +``` +python -u generate.py --llm deepseek-coder --task $TASK --shot 1 --retrieval > "$TASK".txt 2>&1 + +python -u evaluation.py --path "deepseek-coder_True_1" --task $TASK --device 0 > "$TASK"_eval.txt 2>&1 +``` ### AIDE From 9ba9371656f04af5553ddeb95affd35ebe66ae53 Mon Sep 17 00:00:00 2001 From: limafang Date: Thu, 12 Sep 2024 20:26:11 +0800 Subject: [PATCH 056/135] add autosklearn setup run --- expo/README.md | 9 +++ expo/experimenter/autosklearn.py | 110 +++++++++++++++++++++++++++++++ expo/run_experiment.py | 5 +- 3 files changed, 123 insertions(+), 1 deletion(-) create mode 100644 expo/experimenter/autosklearn.py diff --git a/expo/README.md b/expo/README.md index 55ea7eed4..e824312f2 100644 --- a/expo/README.md +++ b/expo/README.md @@ -182,6 +182,15 @@ pip install autogluon ``` 提供github链接，并说明使用的命令以及参数设置 +### AutoSklearn +#### Setup +``` +pip install autosklearn +``` +#### Run +``` +python run_experiment.py --exp_mode autosklearn --task titanic +``` ### Base DI For setup, check 5. diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py new file mode 100644 index 000000000..5786a3790 --- /dev/null +++ b/expo/experimenter/autosklearn.py @@ -0,0 +1,110 @@ +from datetime import datetime +import autosklearn.classification +import autosklearn.regression +import pandas as pd +from expo.experimenter.custom import CustomExperimenter +from expo.evaluation.evaluation import evaluate_score +from autosklearn.metrics import make_scorer +from functools import partial + + +def custom_scorer(y_true, y_pred, metric_name): + return evaluate_score(y_pred, y_true, metric_name) + + +def create_autosklearn_scorer(metric_name): + return make_scorer( + name=metric_name, score_func=partial(custom_scorer, metric_name=metric_name) + ) + + +class ASRunner: + time_limit = 300 + + def __init__(self, state=None): + self.state = state + self.datasets = self.state["datasets_dir"] + + def run(self): + train_path = self.datasets["train"] + dev_wo_target_path = self.datasets["dev_wo_target"] + test_wo_target_path = self.datasets["test_wo_target"] + target_col = self.state["dataset_config"]["target_col"] + + train_data = pd.read_csv(train_path) + dev_data = pd.read_csv(dev_wo_target_path) + test_data = pd.read_csv(test_wo_target_path) + eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_") + X_train = train_data.drop(columns=[target_col]) + y_train = train_data[target_col] + + if eval_metric == "rmse": + automl = autosklearn.regression.AutoSklearnRegressor( + time_left_for_this_task=self.time_limit, + per_run_time_limit=60, + metric=create_autosklearn_scorer("rmse"), # 使用新的函数创建评分器 + memory_limit=8192, + tmp_folder="AutosklearnModels/as-{}-{}".format( + self.state["task"], datetime.now().strftime("%y%m%d_%H%M") + ), + n_jobs=-1, + ) + elif eval_metric == "f1": + automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=self.time_limit, + per_run_time_limit=60, + metric=create_autosklearn_scorer("f1"), # 使用新的函数创建评分器 + memory_limit=8192, + tmp_folder="AutosklearnModels/as-{}-{}".format( + self.state["task"], datetime.now().strftime("%y%m%d_%H%M") + ), + n_jobs=-1, + ) + elif eval_metric == "f1_weighted": + automl = autosklearn.classification.AutoSklearnClassifier( + time_left_for_this_task=self.time_limit, + per_run_time_limit=60, + metric=create_autosklearn_scorer( + "f1 weighted" + ), # 使用新的函数创建评分器 + memory_limit=8192, + tmp_folder="AutosklearnModels/as-{}-{}".format( + self.state["task"], datetime.now().strftime("%y%m%d_%H%M") + ), + n_jobs=-1, + ) + else: + raise ValueError(f"Unsupported metric: {eval_metric}") + automl.fit(X_train, y_train) + + dev_preds = automl.predict(dev_data) + test_preds = automl.predict(test_data) + + return {"test_preds": test_preds, "dev_preds": dev_preds} + + +class AutoSklearnExperimenter(CustomExperimenter): + result_path: str = "results/autosklearn" + + def __init__(self, args, **kwargs): + super().__init__(args, **kwargs) + self.framework = ASRunner(self.state) + + async def run_experiment(self): + result = self.framework.run() + user_requirement = self.state["requirement"] + dev_preds = result["dev_preds"] + test_preds = result["test_preds"] + score_dict = { + "dev_score": self.evaluate_predictions(dev_preds, "dev"), + "test_score": self.evaluate_predictions(test_preds, "test"), + } + results = [ + 0, + { + "score_dict": score_dict, + "user_requirement": user_requirement, + "args": vars(self.args), + }, + ] + self.save_result(results) diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 2123fade3..2c996a737 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -6,6 +6,7 @@ from expo.experimenter.custom import CustomExperimenter from expo.experimenter.experimenter import Experimenter from expo.experimenter.mcts import MCTSExperimenter +from expo.experimenter.autosklearn import AutoSklearnExperimenter def get_args(): @@ -15,7 +16,7 @@ def get_args(): "--exp_mode", type=str, default="mcts", - choices=["mcts", "aug", "base", "custom", "greedy", "autogluon", "random"], + choices=["mcts", "aug", "base", "custom", "greedy", "autogluon", "random", "autosklearn"], ) get_di_args(parser) get_mcts_args(parser) @@ -59,6 +60,8 @@ async def main(args): experimenter = GluonExperimenter(args) elif args.exp_mode == "custom": experimenter = CustomExperimenter(args) + elif args.exp_mode == "autosklearn": + experimenter = AutoSklearnExperimenter(args) else: raise ValueError(f"Invalid exp_mode: {args.exp_mode}") await experimenter.run_experiment() From 562af8c4e4b2a54275d0501420d4e9b4cd09a612 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 13 Sep 2024 11:09:02 +0800 Subject: [PATCH 057/135] update text dataset --- expo/data/dataset.py | 5 ++-- expo/data/hf_data.py | 61 +++++++++++++++++++++++++++++---------- expo/datasets.yaml | 69 +++++++++++++++++++++++++++++--------------- 3 files changed, 94 insertions(+), 41 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 1494eb267..3b2017d1a 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -268,8 +268,9 @@ def save_dataset(self, target_col): print(f"Dataset info for {self.name} already exists") def save_datasetinfo(self, dataset_info): - with open(Path(self.dataset_dir, self.name, "dataset_info.json"), "w") as file: - json.dump(dataset_info, file, indent=4) + with open(Path(self.dataset_dir, self.name, "dataset_info.json"), "w", encoding="utf-8") as file: + # utf-8 encoding is required + json.dump(dataset_info, file, indent=4, ensure_ascii=False) def save_split_datasets(self, df, split, target_col=None): path = Path(self.dataset_dir, self.name) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index 9ed2b2c48..6f1f55f6d 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -9,12 +9,22 @@ from expo.insights.solution_designer import SolutionDesigner HFDATSETS = [ - {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label"}, - {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label"}, - {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label"}, - {"name": "oxford-iiit-pet", "dataset_name": "timm/oxford-iiit-pet", "target_col": "label"}, - {"name": "stanford_cars", "dataset_name": "tanganke/stanford_cars", "target_col": "label"}, - {"name": "fashion_mnist", "dataset_name": "zalando-datasets/fashion_mnist", "target_col": "label"}, + # {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, + # {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"}, + # {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"}, + { + "name": "oxford-iiit-pet", + "dataset_name": "timm/oxford-iiit-pet", + "target_col": "label_cat_dog", + "modality": "image", + }, + {"name": "stanford_cars", "dataset_name": "tanganke/stanford_cars", "target_col": "label", "modality": "image"}, + { + "name": "fashion_mnist", + "dataset_name": "zalando-datasets/fashion_mnist", + "target_col": "label", + "modality": "image", + }, ] @@ -27,41 +37,60 @@ def __init__(self, name, dataset_dir, dataset_name, **kwargs): self.name = name self.dataset_dir = dataset_dir self.dataset_name = dataset_name + self.modality = kwargs.get("modality", "") self.target_col = kwargs.get("target_col", "label") - self.dataset = load_dataset(dataset_name) + self.dataset = load_dataset(self.dataset_name, trust_remote_code=True) super().__init__(self.name, dataset_dir, **kwargs) def get_raw_dataset(self): raw_dir = Path(self.dataset_dir, self.name, "raw") raw_dir.mkdir(parents=True, exist_ok=True) if os.path.exists(Path(raw_dir, "train.csv")): - df = pd.read_csv(Path(raw_dir, "train.csv")) + df = pd.read_csv(Path(raw_dir, "train.csv"), encoding="utf-8") else: df = self.dataset["train"].to_pandas() - df.to_csv(Path(raw_dir, "train.csv"), index=False) + df.to_csv(Path(raw_dir, "train.csv"), index=False, encoding="utf-8") if os.path.exists(Path(raw_dir, "test.csv")): - test_df = pd.read_csv(Path(raw_dir, "test.csv"), index=False) + test_df = pd.read_csv(Path(raw_dir, "test.csv"), encoding="utf-8") else: - if "test" in self.dataset: + if self.dataset and "test" in self.dataset: test_df = self.dataset["test"].to_pandas() - test_df.to_csv(Path(raw_dir, "test.csv"), index=False) + test_df.to_csv(Path(raw_dir, "test.csv"), index=False, encoding="utf-8") else: test_df = None return df, test_df - # def get_df_head(self, raw_df): - # return raw_df.head() + def get_df_head(self, raw_df): + if self.modality == "text": + examples = [] + for i in range(5): + examples.append(raw_df.iloc[i].to_dict()) + return examples + elif self.modality == "image": + return "" + + def get_dataset_info(self): + dataset_info = super().get_dataset_info() + dataset = self.dataset + dataset_info["description"] = dataset["train"].info.description + return dataset_info if __name__ == "__main__": dataset_dir = "D:/work/automl/datasets" - save_analysis_pool = True + save_analysis_pool = False + force_update = False datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() for dataset_meta in HFDATSETS: hf_dataset = HFExpDataset( - dataset_meta["name"], dataset_dir, dataset_meta["dataset_name"], target_col=dataset_meta["target_col"] + dataset_meta["name"], + dataset_dir, + dataset_meta["dataset_name"], + target_col=dataset_meta["target_col"], + force_update=force_update, + modality=dataset_meta["modality"], ) asyncio.run(process_dataset(hf_dataset, solution_designer, save_analysis_pool, datasets_dict)) save_datasets_dict_to_yaml(datasets_dict, "hf_datasets.yaml") diff --git a/expo/datasets.yaml b/expo/datasets.yaml index 512cbc292..051e8232d 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -79,6 +79,14 @@ datasets: \ column `percent_pell_grant`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport rmse on the eval\ \ data. Do not plot or make any visualizations.\n" + concrete-strength: + dataset: concrete-strength + metric: rmse + target_col: Strength + user_requirement: "This is a concrete-strength dataset. Your goal is to predict\ + \ the target column `Strength`.\nPerform data analysis, data preprocessing,\ + \ feature engineering, and modeling to predict the target. \nReport rmse on\ + \ the eval data. Do not plot or make any visualizations.\n" credit-g: dataset: credit-g metric: f1 @@ -135,6 +143,22 @@ datasets: \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ \ Do not plot or make any visualizations.\n" + smoker-status: + dataset: smoker-status + metric: f1 + target_col: smoking + user_requirement: "This is a smoker-status dataset. Your goal is to predict the\ + \ target column `smoking`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + \ Do not plot or make any visualizations.\n" + software-defects: + dataset: software-defects + metric: f1 + target_col: defects + user_requirement: "This is a software-defects dataset. Your goal is to predict\ + \ the target column `defects`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + \ Do not plot or make any visualizations.\n" steel-plates-fault: dataset: steel-plates-fault metric: f1 weighted @@ -151,28 +175,27 @@ datasets: \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ \ eval data. Do not plot or make any visualizations.\n" - concrete-strength: - dataset: concrete-strength - metric: rmse - target_col: Strength - user_requirement: "This is a concrete-strength dataset. Your goal is to predict\ - \ the target column `Strength`.\nPerform data analysis, data preprocessing,\ - \ feature engineering, and modeling to predict the target. \nReport rmse on\ - \ the eval data. Do not plot or make any visualizations.\n" - smoker-status: - dataset: smoker-status - metric: f1 - target_col: smoking - user_requirement: "This is a smoker-status dataset. Your goal is to predict the\ - \ target column `smoking`.\nPerform data analysis, data preprocessing, feature\ - \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + banking77: + dataset: banking77 + metric: f1 weighted + target_col: label + user_requirement: "This is a banking77 dataset. Your goal is to predict the target\ + \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ \ Do not plot or make any visualizations.\n" - software-defects: - dataset: software-defects - metric: f1 - target_col: defects - user_requirement: "This is a software-defects dataset. Your goal is to predict\ - \ the target column `defects`.\nPerform data analysis, data preprocessing, feature\ - \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ + gnad10: + dataset: gnad10 + metric: f1 weighted + target_col: label + user_requirement: "This is a gnad10 dataset. Your goal is to predict the target\ + \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ \ Do not plot or make any visualizations.\n" - + sms_spam: + dataset: sms_spam + metric: f1 + target_col: label + user_requirement: "This is a sms_spam dataset. Your goal is to predict the target\ + \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\ + \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ + \ or make any visualizations.\n" From 9f0427838324f90e4bc9ed62c6eba9e6a2ad4465 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 13 Sep 2024 11:18:15 +0800 Subject: [PATCH 058/135] comment --- expo/data/hf_data.py | 35 +++++++++++++++++++---------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index 6f1f55f6d..952ab5c73 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -9,22 +9,25 @@ from expo.insights.solution_designer import SolutionDesigner HFDATSETS = [ - # {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, - # {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"}, - # {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"}, - { - "name": "oxford-iiit-pet", - "dataset_name": "timm/oxford-iiit-pet", - "target_col": "label_cat_dog", - "modality": "image", - }, - {"name": "stanford_cars", "dataset_name": "tanganke/stanford_cars", "target_col": "label", "modality": "image"}, - { - "name": "fashion_mnist", - "dataset_name": "zalando-datasets/fashion_mnist", - "target_col": "label", - "modality": "image", - }, + {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, + {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"}, + {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"}, + # { + # "name": "oxford-iiit-pet", + # "dataset_name": "timm/oxford-iiit-pet", + # "target_col": "label_cat_dog", + # "modality": "image", + # }, + # { "name": "stanford_cars", + # "dataset_name": "tanganke/stanford_cars", + # "target_col": "label", + # "modality": "image"}, + # { + # "name": "fashion_mnist", + # "dataset_name": "zalando-datasets/fashion_mnist", + # "target_col": "label", + # "modality": "image", + # }, ] From c54e4121c611473ef7ef874d19cfb5891280d091 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 13 Sep 2024 16:57:37 +0800 Subject: [PATCH 059/135] update di prompt --- expo/data/dataset.py | 16 ++++++++++++---- expo/insights/instruction_generator.py | 8 +++++--- 2 files changed, 17 insertions(+), 7 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 3b2017d1a..d2ec48326 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -16,15 +16,22 @@ Report {metric} on the eval data. Do not plot or make any visualizations. """ +RECOMMENDATION = """\ +## Base Models and Ensemble +You can consider using the following base models: +’GBM’ (LightGBM) ‘CAT’ (CatBoost) ‘XGB’ (XGBoost) ‘RF’ (random forest) ‘XT’ (extremely randomized trees) ‘KNN’ (k-nearest neighbors) ‘LR’ (linear regression) +""" -DI_INSTRUCTION = """\ -**Attention** +DI_INSTRUCTION = ( + RECOMMENDATION + + """**Attention** 1. Please do not leak the target label in any form during training. 2. Test set does not have the target column. 3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). -4. If labels are transformed during training, they should be transformed back to the original format before saving the predictions. +4. When scaling or transforming features, make sure the target column is not included. 5. You could utilize dev set to validate and improve model training. -6. Use techniques to avoid overfitting. +6. To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor using **dev set** after base models being trained +7. Make sure the model prototyping is fast. ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. @@ -37,6 +44,7 @@ # Output dir {output_dir} """ +) TASK_PROMPT = """\ # User requirement diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index c9ff7ec6e..a800f4507 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -79,7 +79,7 @@ def load_analysis_pool(file_path, task_id=None): return data @staticmethod - async def generate_new_instructions(task_id, original_instruction, max_num, file_path): + async def generate_new_instructions(task_id, original_instruction, max_num, file_path, ext_info=None): data = InstructionGenerator.load_analysis_pool(file_path, task_id) new_instructions = [] if len(data) == 0: @@ -91,12 +91,14 @@ async def generate_new_instructions(task_id, original_instruction, max_num, file else: item = data[i] insights = item["Analysis"] - new_instruction = await InstructionGenerator.generate_new_instruction(original_instruction, insights) + new_instruction = await InstructionGenerator.generate_new_instruction( + original_instruction, insights, ext_info + ) new_instructions.append(new_instruction) return new_instructions @staticmethod - async def generate_new_instruction(original_instruction, insights): + async def generate_new_instruction(original_instruction, insights, ext_info): prompt = CHANGE_INSTRUCTION.format(instruction=original_instruction, insights=insights) llm = LLM() context = llm.format_msg([Message(content=prompt, role="user")]) From a6b066a127f7d2fcfa8e6b04dc4bfaed5b20c50a Mon Sep 17 00:00:00 2001 From: limafang Date: Fri, 13 Sep 2024 17:52:22 +0800 Subject: [PATCH 060/135] support image dataset --- expo/data/hf_data.py | 72 ++++++++++++++++++++++++++++++++------------ 1 file changed, 53 insertions(+), 19 deletions(-) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index 952ab5c73..45ff6330b 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -1,7 +1,9 @@ import asyncio import os from pathlib import Path - +import numpy as np +from PIL import Image +import io import pandas as pd from datasets import load_dataset @@ -9,22 +11,25 @@ from expo.insights.solution_designer import SolutionDesigner HFDATSETS = [ - {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, - {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"}, - {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"}, - # { - # "name": "oxford-iiit-pet", - # "dataset_name": "timm/oxford-iiit-pet", - # "target_col": "label_cat_dog", - # "modality": "image", - # }, + # {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, + # {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"}, + # {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"}, + { + "name": "oxford-iiit-pet", + "dataset_name": "timm/oxford-iiit-pet", + "image_col": "image", + "target_col": "label_cat_dog", + "modality": "image", + }, # { "name": "stanford_cars", # "dataset_name": "tanganke/stanford_cars", + # "image_col": "image", # "target_col": "label", # "modality": "image"}, # { # "name": "fashion_mnist", # "dataset_name": "zalando-datasets/fashion_mnist", + # "image_col": "image", # "target_col": "label", # "modality": "image", # }, @@ -42,16 +47,22 @@ def __init__(self, name, dataset_dir, dataset_name, **kwargs): self.dataset_name = dataset_name self.modality = kwargs.get("modality", "") self.target_col = kwargs.get("target_col", "label") + self.image_col = kwargs.get("image_col", "image") self.dataset = load_dataset(self.dataset_name, trust_remote_code=True) super().__init__(self.name, dataset_dir, **kwargs) def get_raw_dataset(self): raw_dir = Path(self.dataset_dir, self.name, "raw") raw_dir.mkdir(parents=True, exist_ok=True) + if os.path.exists(Path(raw_dir, "train.csv")): df = pd.read_csv(Path(raw_dir, "train.csv"), encoding="utf-8") else: df = self.dataset["train"].to_pandas() + + if self.modality == "image": + df = self.save_images_and_update_df(df, raw_dir, "train") + df.to_csv(Path(raw_dir, "train.csv"), index=False, encoding="utf-8") if os.path.exists(Path(raw_dir, "test.csv")): @@ -59,19 +70,37 @@ def get_raw_dataset(self): else: if self.dataset and "test" in self.dataset: test_df = self.dataset["test"].to_pandas() + + if self.modality == "image": + test_df = self.save_images_and_update_df(test_df, raw_dir, "test") + test_df.to_csv(Path(raw_dir, "test.csv"), index=False, encoding="utf-8") else: test_df = None + return df, test_df + def save_images_and_update_df(self, df, raw_dir, split): + image_dir = Path(raw_dir, f"{split}_images") + image_dir.mkdir(parents=True, exist_ok=True) + + def process_image(idx, row): + image_bytes = row[self.image_col]["bytes"] + image = Image.open(io.BytesIO(image_bytes)) + if image.mode == "RGBA": + image = image.convert("RGB") + img_path = Path(image_dir, f"{idx}.jpg") + image.save(img_path) + return str(img_path) + + df["image"] = df.apply(lambda row: process_image(row.name, row), axis=1) + return df + def get_df_head(self, raw_df): - if self.modality == "text": - examples = [] - for i in range(5): - examples.append(raw_df.iloc[i].to_dict()) - return examples - elif self.modality == "image": - return "" + examples = [] + for i in range(5): + examples.append(raw_df.iloc[i].to_dict()) + return examples def get_dataset_info(self): dataset_info = super().get_dataset_info() @@ -82,7 +111,7 @@ def get_dataset_info(self): if __name__ == "__main__": dataset_dir = "D:/work/automl/datasets" - save_analysis_pool = False + save_analysis_pool = True force_update = False datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() @@ -92,8 +121,13 @@ def get_dataset_info(self): dataset_dir, dataset_meta["dataset_name"], target_col=dataset_meta["target_col"], + image_col=dataset_meta["image_col"], force_update=force_update, modality=dataset_meta["modality"], ) - asyncio.run(process_dataset(hf_dataset, solution_designer, save_analysis_pool, datasets_dict)) + asyncio.run( + process_dataset( + hf_dataset, solution_designer, save_analysis_pool, datasets_dict + ) + ) save_datasets_dict_to_yaml(datasets_dict, "hf_datasets.yaml") From cfa21ba27e6748229ff61636549c444e25b21904 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 13 Sep 2024 19:04:48 +0800 Subject: [PATCH 061/135] change img path from abs to rel --- expo/data/hf_data.py | 56 ++++++++++++++++++++++---------------------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index 45ff6330b..6f615c8cb 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -1,19 +1,19 @@ import asyncio +import io import os from pathlib import Path -import numpy as np -from PIL import Image -import io + import pandas as pd from datasets import load_dataset +from PIL import Image from expo.data.dataset import ExpDataset, process_dataset, save_datasets_dict_to_yaml from expo.insights.solution_designer import SolutionDesigner HFDATSETS = [ - # {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, - # {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"}, - # {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"}, + {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, + {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"}, + {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"}, { "name": "oxford-iiit-pet", "dataset_name": "timm/oxford-iiit-pet", @@ -21,18 +21,20 @@ "target_col": "label_cat_dog", "modality": "image", }, - # { "name": "stanford_cars", - # "dataset_name": "tanganke/stanford_cars", - # "image_col": "image", - # "target_col": "label", - # "modality": "image"}, - # { - # "name": "fashion_mnist", - # "dataset_name": "zalando-datasets/fashion_mnist", - # "image_col": "image", - # "target_col": "label", - # "modality": "image", - # }, + { + "name": "stanford_cars", + "dataset_name": "tanganke/stanford_cars", + "image_col": "image", + "target_col": "label", + "modality": "image", + }, + { + "name": "fashion_mnist", + "dataset_name": "zalando-datasets/fashion_mnist", + "image_col": "image", + "target_col": "label", + "modality": "image", + }, ] @@ -81,17 +83,19 @@ def get_raw_dataset(self): return df, test_df def save_images_and_update_df(self, df, raw_dir, split): - image_dir = Path(raw_dir, f"{split}_images") - image_dir.mkdir(parents=True, exist_ok=True) + abs_image_dir = Path(raw_dir, f"{split}_images") + rel_image_dir = f"raw/{split}_images" + abs_image_dir.mkdir(parents=True, exist_ok=True) def process_image(idx, row): image_bytes = row[self.image_col]["bytes"] image = Image.open(io.BytesIO(image_bytes)) if image.mode == "RGBA": image = image.convert("RGB") - img_path = Path(image_dir, f"{idx}.jpg") + img_path = Path(abs_image_dir, f"{idx}.jpg") + rel_img_path = f"{rel_image_dir}/{idx}.jpg" image.save(img_path) - return str(img_path) + return rel_img_path df["image"] = df.apply(lambda row: process_image(row.name, row), axis=1) return df @@ -112,7 +116,7 @@ def get_dataset_info(self): if __name__ == "__main__": dataset_dir = "D:/work/automl/datasets" save_analysis_pool = True - force_update = False + force_update = True datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() for dataset_meta in HFDATSETS: @@ -125,9 +129,5 @@ def get_dataset_info(self): force_update=force_update, modality=dataset_meta["modality"], ) - asyncio.run( - process_dataset( - hf_dataset, solution_designer, save_analysis_pool, datasets_dict - ) - ) + asyncio.run(process_dataset(hf_dataset, solution_designer, save_analysis_pool, datasets_dict)) save_datasets_dict_to_yaml(datasets_dict, "hf_datasets.yaml") From 7d8cb9afec8017fa1006df377028f9648c40d3b5 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 13 Sep 2024 19:10:45 +0800 Subject: [PATCH 062/135] add image datasets config --- expo/data/hf_data.py | 6 +++--- expo/datasets.yaml | 24 ++++++++++++++++++++++++ 2 files changed, 27 insertions(+), 3 deletions(-) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index 6f615c8cb..df3a6ed20 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -115,8 +115,8 @@ def get_dataset_info(self): if __name__ == "__main__": dataset_dir = "D:/work/automl/datasets" - save_analysis_pool = True - force_update = True + save_analysis_pool = False + force_update = False datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() for dataset_meta in HFDATSETS: @@ -125,7 +125,7 @@ def get_dataset_info(self): dataset_dir, dataset_meta["dataset_name"], target_col=dataset_meta["target_col"], - image_col=dataset_meta["image_col"], + image_col=dataset_meta.get("image_col", ""), force_update=force_update, modality=dataset_meta["modality"], ) diff --git a/expo/datasets.yaml b/expo/datasets.yaml index 051e8232d..92e004c6d 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -183,6 +183,14 @@ datasets: \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ \ Do not plot or make any visualizations.\n" + fashion_mnist: + dataset: fashion_mnist + metric: f1 weighted + target_col: label + user_requirement: "This is a fashion_mnist dataset. Your goal is to predict the\ + \ target column `label`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" gnad10: dataset: gnad10 metric: f1 weighted @@ -191,6 +199,14 @@ datasets: \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ \ Do not plot or make any visualizations.\n" + oxford-iiit-pet: + dataset: oxford-iiit-pet + metric: f1 + target_col: label_cat_dog + user_requirement: "This is a oxford-iiit-pet dataset. Your goal is to predict\ + \ the target column `label_cat_dog`.\nPerform data analysis, data preprocessing,\ + \ feature engineering, and modeling to predict the target. \nReport f1 on the\ + \ eval data. Do not plot or make any visualizations.\n" sms_spam: dataset: sms_spam metric: f1 @@ -199,3 +215,11 @@ datasets: \ column `label`.\nPerform data analysis, data preprocessing, feature engineering,\ \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ \ or make any visualizations.\n" + stanford_cars: + dataset: stanford_cars + metric: f1 weighted + target_col: label + user_requirement: "This is a stanford_cars dataset. Your goal is to predict the\ + \ target column `label`.\nPerform data analysis, data preprocessing, feature\ + \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ + \ eval data. Do not plot or make any visualizations.\n" From b49334b16c4c61bff89780c522c6f60f25d89329 Mon Sep 17 00:00:00 2001 From: limafang Date: Fri, 13 Sep 2024 21:07:10 +0800 Subject: [PATCH 063/135] fix import and update readme --- expo/README.md | 18 +++++++++++++++++- expo/experimenter/autosklearn.py | 32 ++++++++++++-------------------- 2 files changed, 29 insertions(+), 21 deletions(-) diff --git a/expo/README.md b/expo/README.md index e824312f2..707e6415e 100644 --- a/expo/README.md +++ b/expo/README.md @@ -183,10 +183,26 @@ pip install autogluon 提供github链接，并说明使用的命令以及参数设置 ### AutoSklearn +#### System requirements +auto-sklearn has the following system requirements: + +- Linux operating system (for example Ubuntu) + +- Python (>=3.7) + +- C++ compiler (with C++11 supports) + +In case you try to install Auto-sklearn on a system where no wheel files for the pyrfr package are provided (see here for available wheels) you also need: + +- SWIG [(get SWIG here).](https://www.swig.org/survey.html) + +For an explanation of missing Microsoft Windows and macOS support please check the Section [Windows/macOS compatibility](https://automl.github.io/auto-sklearn/master/installation.html#windows-macos-compatibility). + #### Setup ``` -pip install autosklearn +pip install auto-sklearn ``` + #### Run ``` python run_experiment.py --exp_mode autosklearn --task titanic diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py index 5786a3790..7340edafa 100644 --- a/expo/experimenter/autosklearn.py +++ b/expo/experimenter/autosklearn.py @@ -1,10 +1,7 @@ from datetime import datetime -import autosklearn.classification -import autosklearn.regression import pandas as pd from expo.experimenter.custom import CustomExperimenter from expo.evaluation.evaluation import evaluate_score -from autosklearn.metrics import make_scorer from functools import partial @@ -24,6 +21,14 @@ class ASRunner: def __init__(self, state=None): self.state = state self.datasets = self.state["datasets_dir"] + try: + import autosklearn.classification + import autosklearn.regression + from autosklearn.metrics import make_scorer + except ImportError: + raise ImportError( + "autosklearn not found or system not supported, please check it first" + ) def run(self): train_path = self.datasets["train"] @@ -34,7 +39,7 @@ def run(self): train_data = pd.read_csv(train_path) dev_data = pd.read_csv(dev_wo_target_path) test_data = pd.read_csv(test_wo_target_path) - eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_") + eval_metric = self.state["dataset_config"]["metric"] X_train = train_data.drop(columns=[target_col]) y_train = train_data[target_col] @@ -42,31 +47,18 @@ def run(self): automl = autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=self.time_limit, per_run_time_limit=60, - metric=create_autosklearn_scorer("rmse"), # 使用新的函数创建评分器 - memory_limit=8192, - tmp_folder="AutosklearnModels/as-{}-{}".format( - self.state["task"], datetime.now().strftime("%y%m%d_%H%M") - ), - n_jobs=-1, - ) - elif eval_metric == "f1": - automl = autosklearn.classification.AutoSklearnClassifier( - time_left_for_this_task=self.time_limit, - per_run_time_limit=60, - metric=create_autosklearn_scorer("f1"), # 使用新的函数创建评分器 + metric=create_autosklearn_scorer(eval_metric), memory_limit=8192, tmp_folder="AutosklearnModels/as-{}-{}".format( self.state["task"], datetime.now().strftime("%y%m%d_%H%M") ), n_jobs=-1, ) - elif eval_metric == "f1_weighted": + elif eval_metric in ["f1", "f1 weighted"]: automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=self.time_limit, per_run_time_limit=60, - metric=create_autosklearn_scorer( - "f1 weighted" - ), # 使用新的函数创建评分器 + metric=create_autosklearn_scorer(eval_metric), memory_limit=8192, tmp_folder="AutosklearnModels/as-{}-{}".format( self.state["task"], datetime.now().strftime("%y%m%d_%H%M") From 5d2de4d0ec008b3e5737ad8b2ee531a9dc88e1c3 Mon Sep 17 00:00:00 2001 From: duiyipan Date: Sat, 14 Sep 2024 11:37:08 +0800 Subject: [PATCH 064/135] add random seed --- expo/experimenter/autosklearn.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py index 7340edafa..22aa2a132 100644 --- a/expo/experimenter/autosklearn.py +++ b/expo/experimenter/autosklearn.py @@ -17,6 +17,7 @@ def create_autosklearn_scorer(metric_name): class ASRunner: time_limit = 300 + seed = 42 def __init__(self, state=None): self.state = state @@ -49,6 +50,7 @@ def run(self): per_run_time_limit=60, metric=create_autosklearn_scorer(eval_metric), memory_limit=8192, + seed=self.seed, tmp_folder="AutosklearnModels/as-{}-{}".format( self.state["task"], datetime.now().strftime("%y%m%d_%H%M") ), @@ -60,6 +62,7 @@ def run(self): per_run_time_limit=60, metric=create_autosklearn_scorer(eval_metric), memory_limit=8192, + seed=self.seed, tmp_folder="AutosklearnModels/as-{}-{}".format( self.state["task"], datetime.now().strftime("%y%m%d_%H%M") ), From b32b28eb125136ca404200ff7e20831442d36f90 Mon Sep 17 00:00:00 2001 From: Bangbang Date: Sat, 14 Sep 2024 12:13:55 +0800 Subject: [PATCH 065/135] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E9=A2=84=E6=B5=8B?= =?UTF-8?q?=E7=BB=93=E6=9E=9C=E6=B2=A1=E6=9C=89target=E5=88=97=E5=90=8D?= =?UTF-8?q?=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/experimenter/experimenter.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index b1b5a93c0..418e0089a 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -85,7 +85,8 @@ def evaluate_prediction(self, split, state): os.makedirs(state["node_dir"], exist_ok=True) pred_node_path = os.path.join(state["node_dir"], f"{self.start_time}-{split}_predictions.csv") gt_path = os.path.join(state["datasets_dir"][f"{split}_target"]) - preds = pd.read_csv(pred_path)["target"] + preds = pd.read_csv(pred_path) + preds = preds[preds.columns.tolist()[0]] preds.to_csv(pred_node_path, index=False) gt = pd.read_csv(gt_path)["target"] metric = state["dataset_config"]["metric"] From c4fe056bcaa2d06abec4c2328c01994d09fce031 Mon Sep 17 00:00:00 2001 From: duiyipan Date: Sat, 14 Sep 2024 14:58:22 +0800 Subject: [PATCH 066/135] fix import error delete seed --- expo/experimenter/autosklearn.py | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py index 22aa2a132..602b8385a 100644 --- a/expo/experimenter/autosklearn.py +++ b/expo/experimenter/autosklearn.py @@ -9,15 +9,8 @@ def custom_scorer(y_true, y_pred, metric_name): return evaluate_score(y_pred, y_true, metric_name) -def create_autosklearn_scorer(metric_name): - return make_scorer( - name=metric_name, score_func=partial(custom_scorer, metric_name=metric_name) - ) - - class ASRunner: time_limit = 300 - seed = 42 def __init__(self, state=None): self.state = state @@ -25,12 +18,19 @@ def __init__(self, state=None): try: import autosklearn.classification import autosklearn.regression - from autosklearn.metrics import make_scorer + import autosklearn.metrics + + self.autosklearn = autosklearn except ImportError: raise ImportError( "autosklearn not found or system not supported, please check it first" ) + def create_autosklearn_scorer(self, metric_name): + return self.autosklearn.metrics.make_scorer( + name=metric_name, score_func=partial(custom_scorer, metric_name=metric_name) + ) + def run(self): train_path = self.datasets["train"] dev_wo_target_path = self.datasets["dev_wo_target"] @@ -45,24 +45,22 @@ def run(self): y_train = train_data[target_col] if eval_metric == "rmse": - automl = autosklearn.regression.AutoSklearnRegressor( + automl = self.autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=self.time_limit, per_run_time_limit=60, - metric=create_autosklearn_scorer(eval_metric), + metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, - seed=self.seed, tmp_folder="AutosklearnModels/as-{}-{}".format( self.state["task"], datetime.now().strftime("%y%m%d_%H%M") ), n_jobs=-1, ) elif eval_metric in ["f1", "f1 weighted"]: - automl = autosklearn.classification.AutoSklearnClassifier( + automl = self.autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=self.time_limit, per_run_time_limit=60, - metric=create_autosklearn_scorer(eval_metric), + metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, - seed=self.seed, tmp_folder="AutosklearnModels/as-{}-{}".format( self.state["task"], datetime.now().strftime("%y%m%d_%H%M") ), From 8beca0faddd33b981d23d875c1a59df0b71947f0 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 15:17:42 +0800 Subject: [PATCH 067/135] 1. add special instruction 2. add fixed insights --- expo/MCTS.py | 24 ++++++++------- expo/README.md | 24 ++++++++++----- expo/data/dataset.py | 42 ++++++++++++++++++-------- expo/experimenter/aug.py | 4 ++- expo/experimenter/custom.py | 7 ++++- expo/experimenter/experimenter.py | 3 +- expo/experimenter/mcts.py | 12 +++----- expo/insights/fixed_insights.json | 22 ++++++++++++++ expo/insights/instruction_generator.py | 15 +++++++-- expo/requirements.txt | 1 + expo/run_experiment.py | 4 ++- 11 files changed, 111 insertions(+), 47 deletions(-) create mode 100644 expo/insights/fixed_insights.json diff --git a/expo/MCTS.py b/expo/MCTS.py index 360baac8d..265356f65 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -15,18 +15,18 @@ from metagpt.utils.common import read_json_file -def initialize_di_root_node(task, data_config, low_is_better=False, reflection=True, name=""): +def initialize_di_root_node(state, reflection: bool = True): start_task_id = 2 - state = create_initial_state( - task, start_task_id=start_task_id, data_config=data_config, low_is_better=low_is_better, name=name - ) + # state = create_initial_state( + # task, start_task_id=start_task_id, data_config=data_config, low_is_better=low_is_better, name=name + # ) role = ResearchAssistant( node_id="0", start_task_id=start_task_id, use_reflection=reflection, role_dir=state["node_dir"] ) return role, Node(parent=None, state=state, action=None, value=0) -def create_initial_state(task, start_task_id, data_config, low_is_better, name): +def create_initial_state(task, start_task_id, data_config, low_is_better: bool, name: str, special_instruction: str): initial_state = { "task": task, "work_dir": data_config["work_dir"], @@ -34,7 +34,9 @@ def create_initial_state(task, start_task_id, data_config, low_is_better, name): "dataset_config": data_config["datasets"][task], "datasets_dir": get_split_dataset_path(task, data_config), "exp_pool_path": get_exp_pool_path(task, data_config, pool_name="ds_analysis_pool"), - "requirement": generate_task_requirement(task, data_config), + "requirement": generate_task_requirement( + task, data_config, is_di=True, special_instruction=special_instruction + ), "has_run": False, "start_task_id": start_task_id, "low_is_better": low_is_better, @@ -157,6 +159,7 @@ async def expand(self, max_children): original_instruction=original_instruction, max_num=max_children, file_path=self.state["exp_pool_path"], + use_fixed_insights=self.use_fixed_insights, ) new_state = self.state.copy() new_state["start_task_id"] += 1 @@ -234,9 +237,10 @@ class MCTS: c_explore: float = 1.4 c_unvisited: float = 0.8 - def __init__(self, root_node, max_depth): + def __init__(self, root_node, max_depth, use_fixed_insights): self.root_node = root_node self.max_depth = max_depth + self.use_fixed_insights = use_fixed_insights def select(self, node: Node): node = self.best_child() @@ -303,10 +307,8 @@ def bfs(node: Node, best_score, best_child: Node, split): def get_num_simulations(self): return self.root_node.visited - async def search(self, task, data_config, name, rollouts, load_tree=False, low_is_better=False, reflection=False): - role, root = initialize_di_root_node( - task, data_config, low_is_better=low_is_better, reflection=reflection, name=name - ) + async def search(self, state, rollouts, load_tree=False, reflection=False): + role, root = initialize_di_root_node(state, reflection=reflection) self.root_node = root tree_loaded = False if load_tree: diff --git a/expo/README.md b/expo/README.md index 55ea7eed4..00d1cae50 100644 --- a/expo/README.md +++ b/expo/README.md @@ -187,16 +187,10 @@ pip install autogluon For setup, check 5. - `python run_experiment.py --exp_mode base --task titanic --num_experiments 10` +- Ask DI to use AutoGluon: `--special_instruction ag` +- Ask DI to use the stacking ensemble method: `--special_instruction stacking` -### DI RandomSearch -For setup, check 5. - -- Single insight -`python run_experiment.py --exp_mode aug --task titanic --aug_mode single` - -- Set insight -`python run_experiment.py --exp_mode aug --task titanic --aug_mode set` ## 5. DI MCTS @@ -223,6 +217,20 @@ If the dataset has reg metric, remember to use `--low_is_better`: - `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 10 --low_is_better` +In addition to the generated insights, include the fixed insights saved in `insights/fixed_insights.json` +- `--use_fixed_insights` + + + +#### Ablation Study + +**DI RandomSearch** + +- Single insight +`python run_experiment.py --exp_mode aug --task titanic --aug_mode single` + +- Set insight +`python run_experiment.py --exp_mode aug --task titanic --aug_mode set` diff --git a/expo/data/dataset.py b/expo/data/dataset.py index d2ec48326..03b80985a 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -10,16 +10,27 @@ from expo.insights.solution_designer import SolutionDesigner -BASE_USER_REQUIREMENT = """\ +BASE_USER_REQUIREMENT = """ This is a {datasetname} dataset. Your goal is to predict the target column `{target_col}`. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report {metric} on the eval data. Do not plot or make any visualizations. """ -RECOMMENDATION = """\ +USE_AG = """ +7. Please use autogluon for model training with presets='medium_quality', time_limit=None, give dev dataset to tuning_data, and use right eval_metric. +""" + +STACKING = """ +7. To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor. +8. You could do some quick model prototyping to see which models work best and then use them in the ensemble. +""" + +SPECIAL_INSTRUCTIONS = {"ag": USE_AG, "stacking": STACKING} + +RECOMMENDATION = """ ## Base Models and Ensemble You can consider using the following base models: -’GBM’ (LightGBM) ‘CAT’ (CatBoost) ‘XGB’ (XGBoost) ‘RF’ (random forest) ‘XT’ (extremely randomized trees) ‘KNN’ (k-nearest neighbors) ‘LR’ (linear regression) +`GBM` (LightGBM) `CAT` (CatBoost) `XGB` (XGBoost) `RF` (random forest) `XT` (extremely randomized trees) `KNN` (k-nearest neighbors) ‘LR’ (linear regression) """ DI_INSTRUCTION = ( @@ -27,11 +38,10 @@ + """**Attention** 1. Please do not leak the target label in any form during training. 2. Test set does not have the target column. -3. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). -4. When scaling or transforming features, make sure the target column is not included. -5. You could utilize dev set to validate and improve model training. -6. To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor using **dev set** after base models being trained -7. Make sure the model prototyping is fast. +3. When conducting data exploration or analysis, print out the results of your findings. +4. You should perform transformations on train, dev, and test sets at the same time (it's a good idea to define functions for this and avoid code repetition). +5. When scaling or transforming features, make sure the target column is not included. +6. You could utilize dev set to validate and improve model training. {special_instruction} ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. @@ -46,7 +56,7 @@ """ ) -TASK_PROMPT = """\ +TASK_PROMPT = """ # User requirement {user_requirement} {additional_instruction} @@ -142,12 +152,18 @@ def create_dataset_dict(dataset): return dataset_dict -def generate_di_instruction(output_dir): - additional_instruction = DI_INSTRUCTION.format(output_dir=output_dir) +def generate_di_instruction(output_dir, special_instruction): + if special_instruction: + special_instruction_prompt = SPECIAL_INSTRUCTIONS[special_instruction] + else: + special_instruction_prompt = "" + additional_instruction = DI_INSTRUCTION.format( + output_dir=output_dir, special_instruction=special_instruction_prompt + ) return additional_instruction -def generate_task_requirement(task_name, data_config, is_di=True): +def generate_task_requirement(task_name, data_config, is_di=True, special_instruction=None): user_requirement = get_user_requirement(task_name, data_config) split_dataset_path = get_split_dataset_path(task_name, data_config) train_path = split_dataset_path["train"] @@ -158,7 +174,7 @@ def generate_task_requirement(task_name, data_config, is_di=True): datasets_dir = data_config["datasets_dir"] data_info_path = f"{datasets_dir}/{task_name}/dataset_info.json" if is_di: - additional_instruction = generate_di_instruction(output_dir) + additional_instruction = generate_di_instruction(output_dir, special_instruction) else: additional_instruction = "" user_requirement = TASK_PROMPT.format( diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 8312f57fc..e57d024bd 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -17,7 +17,9 @@ async def run_experiment(self): # state = create_initial_state(self.args.task, start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, name="") user_requirement = self.state["requirement"] exp_pool_path = get_exp_pool_path(self.args.task, self.data_config, pool_name="ds_analysis_pool") - exp_pool = InstructionGenerator.load_analysis_pool(exp_pool_path) + exp_pool = InstructionGenerator.load_analysis_pool( + exp_pool_path, use_fixed_insights=self.args.use_fixed_insights + ) if self.args.aug_mode == "single": exps = InstructionGenerator._random_sample(exp_pool, self.args.num_experiments) exps = [exp["Analysis"] for exp in exps] diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py index df090fb58..92b7dafa2 100644 --- a/expo/experimenter/custom.py +++ b/expo/experimenter/custom.py @@ -18,7 +18,12 @@ def __init__(self, args, **kwargs): self.name = kwargs.get("name", "") self.result_path = f"results/custom_{self.name}" self.state = create_initial_state( - self.task, start_task_id=1, data_config=self.data_config, low_is_better=self.low_is_better, name=self.name + self.task, + start_task_id=1, + data_config=self.data_config, + low_is_better=self.low_is_better, + name=self.name, + special_instruction=self.args.special_instruction, ) def run_experiment(self): diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 418e0089a..89d589d7d 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -23,7 +23,8 @@ def __init__(self, args, **kwargs): start_task_id=1, data_config=self.data_config, low_is_better=self.args.low_is_better, - name="", + name=self.args.name, + special_instruction=self.args.special_instruction, ) async def run_di(self, di, user_requirement, run_idx): diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index fbe2f35f1..e06169a70 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -13,19 +13,15 @@ def __init__(self, args, tree_mode=None, **kwargs): async def run_experiment(self): if self.tree_mode == "greedy": - mcts = Greedy(root_node=None, max_depth=5) + mcts = Greedy(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) elif self.tree_mode == "random": - mcts = Random(root_node=None, max_depth=5) + mcts = Random(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) else: - mcts = MCTS(root_node=None, max_depth=5) + mcts = MCTS(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) best_nodes = await mcts.search( - self.args.task, - self.data_config, - low_is_better=self.args.low_is_better, - load_tree=self.args.load_tree, + state=self.state, reflection=self.args.reflection, rollouts=self.args.rollouts, - name=self.args.name, ) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] diff --git a/expo/insights/fixed_insights.json b/expo/insights/fixed_insights.json new file mode 100644 index 000000000..e52745707 --- /dev/null +++ b/expo/insights/fixed_insights.json @@ -0,0 +1,22 @@ +[ +{ + "Analysis": "Use early stopping, hyperparameter tuning, and cross-validation to avoid overfitting and improve robustness of the model.", + "Category": "Model Training", + "task_id": 4 +}, +{ + "Analysis": "use k-fold bagging and early stopping", + "Category": "Model Training", + "task_id": 4 +}, +{ + "Analysis": "To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor using **dev set** after base models being trained.", + "Category": "Model Training", + "task_id": 4 +}, +{ + "Analysis": "Please use autogluon for model training with presets='medium_quality', time_limit=None, give dev dataset to tuning_data, and use right eval_metric.", + "Category": "Model Training", + "task_id": 4 +} +] \ No newline at end of file diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index a800f4507..07e5fb655 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -1,4 +1,5 @@ import json +import os import random from expo.utils import clean_json_from_rsp, load_data_config, mcts_logger @@ -68,8 +69,12 @@ def format_output(rsp): return new_data @staticmethod - def load_analysis_pool(file_path, task_id=None): + def load_analysis_pool(file_path, use_fixed_insights, task_id=None): data = InstructionGenerator.load_json_data(file_path) + if use_fixed_insights: + current_directory = os.path.dirname(__file__) + fixed_insights = InstructionGenerator.load_json_data(f"{current_directory}/fixed_insights.json") + data.extend(fixed_insights) for item in data: if "task_id" not in item: raise ValueError("task_id is not found in the analysis pool") @@ -79,8 +84,12 @@ def load_analysis_pool(file_path, task_id=None): return data @staticmethod - async def generate_new_instructions(task_id, original_instruction, max_num, file_path, ext_info=None): - data = InstructionGenerator.load_analysis_pool(file_path, task_id) + async def generate_new_instructions( + task_id, original_instruction, max_num, file_path, ext_info=None, use_fixed_insights=False + ): + data = InstructionGenerator.load_analysis_pool( + file_path, task_id=task_id, use_fixed_insights=use_fixed_insights + ) new_instructions = [] if len(data) == 0: mcts_logger.log("MCTS", f"No insights available for task {task_id}") diff --git a/expo/requirements.txt b/expo/requirements.txt index 04de1a8bb..e85818bbe 100644 --- a/expo/requirements.txt +++ b/expo/requirements.txt @@ -3,3 +3,4 @@ openml==0.14.2 # ml module to run in DI xgboost catboost +lightgbm diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 2123fade3..f1b5b2d80 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -28,11 +28,11 @@ def get_mcts_args(parser): parser.add_argument("--no_load_tree", dest="load_tree", action="store_false") parser.set_defaults(load_tree=False) parser.add_argument("--rollouts", type=int, default=5) + parser.add_argument("--use_fixed_insights", dest="use_fixed_insights", action="store_true") def get_aug_exp_args(parser): parser.add_argument("--aug_mode", type=str, default="single", choices=["single", "set"]) - parser.add_argument("--num_experiments", type=int, default=1) def get_di_args(parser): @@ -41,6 +41,8 @@ def get_di_args(parser): parser.set_defaults(low_is_better=False) parser.add_argument("--reflection", dest="reflection", action="store_true") parser.add_argument("--no_reflection", dest="reflection", action="store_false") + parser.add_argument("--num_experiments", type=int, default=1) + parser.add_argument("--special_instruction", type=str, default=None, choices=["ag", "stacking"]) parser.set_defaults(reflection=True) From 9089ecf7d6f031d069623381006b14615788ecde Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 15:21:21 +0800 Subject: [PATCH 068/135] update readme --- expo/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/README.md b/expo/README.md index 00d1cae50..0af20388e 100644 --- a/expo/README.md +++ b/expo/README.md @@ -217,7 +217,7 @@ If the dataset has reg metric, remember to use `--low_is_better`: - `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 10 --low_is_better` -In addition to the generated insights, include the fixed insights saved in `insights/fixed_insights.json` +In addition to the generated insights, include the fixed insights saved in `expo/insights/fixed_insights.json` - `--use_fixed_insights` From ed6ce14838861dec0385e5cdfb131ab3664948b9 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 15:24:17 +0800 Subject: [PATCH 069/135] update fixed insights --- expo/insights/fixed_insights.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/insights/fixed_insights.json b/expo/insights/fixed_insights.json index e52745707..4f42b9db1 100644 --- a/expo/insights/fixed_insights.json +++ b/expo/insights/fixed_insights.json @@ -10,7 +10,7 @@ "task_id": 4 }, { - "Analysis": "To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor using **dev set** after base models being trained.", + "Analysis": "To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor; You could do some quick model prototyping to see which models work best and then use them in the ensemble.", "Category": "Model Training", "task_id": 4 }, From 8a5b6d6e7794c9b1fb795e654b9a1655c4dd83da Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 15:53:38 +0800 Subject: [PATCH 070/135] update recommendation prompt --- expo/data/dataset.py | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 03b80985a..8af0c485e 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -28,14 +28,29 @@ SPECIAL_INSTRUCTIONS = {"ag": USE_AG, "stacking": STACKING} RECOMMENDATION = """ -## Base Models and Ensemble -You can consider using the following base models: -`GBM` (LightGBM) `CAT` (CatBoost) `XGB` (XGBoost) `RF` (random forest) `XT` (extremely randomized trees) `KNN` (k-nearest neighbors) ‘LR’ (linear regression) +## Base Models +You have access to the following base models: +Tabular: +LightGBM, CatBoost, XGBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression + +Image: +ResNet, DenseNet, VGG, Inception, MobileNet, EfficientNet + +Text: +BERT, RoBERTa, DistilBERT, GPT-2 +""" + +# The RECOMMENDATION above is not tested but might be needed for multi-modal datasets + +RECOMMENDATION = """ +## Base Models +You have access to the following base models: +LightGBM, CatBoost, XGBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression """ DI_INSTRUCTION = ( RECOMMENDATION - + """**Attention** + + """## Attention 1. Please do not leak the target label in any form during training. 2. Test set does not have the target column. 3. When conducting data exploration or analysis, print out the results of your findings. From 743c67aef8c6b0aed81d4334546a543cc2187832 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 17:34:17 +0800 Subject: [PATCH 071/135] change task type prompt to prevent unwanted label transformation --- metagpt/prompts/task_type.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/metagpt/prompts/task_type.py b/metagpt/prompts/task_type.py index 116756edc..ca0aae572 100644 --- a/metagpt/prompts/task_type.py +++ b/metagpt/prompts/task_type.py @@ -11,7 +11,7 @@ - Monitor data types per column, applying appropriate methods. - Ensure operations are on existing dataset columns. - Avoid writing processed data to files. -- Avoid any change to label column, such as standardization, etc. +- **ATTENTION** Do NOT make any changes to the label column, such as standardization, etc. - Prefer alternatives to one-hot encoding for categorical data. - Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later. - Each step do data preprocessing to train, must do same for test separately at the same time. @@ -26,7 +26,7 @@ - Avoid creating redundant or excessively numerous features in one step. - Exclude ID columns from feature generation and remove them. - Each feature engineering operation performed on the train set must also applies to the dev/test separately at the same time. -- Avoid using the label column to create features, except for cat encoding. +- **ATTENTION** Do NOT use the label column to create features or make any changes to the label column, except for cat encoding. - Use the data from previous task result if exist, do not mock or reload data yourself. - Always copy the DataFrame before processing it and use the copy to process. """ From 5e7cac7e6e3f53a98cddc62c6453e19cfb7b3bf8 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 17:51:32 +0800 Subject: [PATCH 072/135] fix fixed_insights bug --- expo/MCTS.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 265356f65..ef408b2dd 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -148,7 +148,7 @@ def save_new_role(self, role: ResearchAssistant): role = role.model_copy() role.save_state(static_save=True) - async def expand(self, max_children): + async def expand(self, max_children, use_fixed_insights): if self.is_fully_expanded(): return insight_geneartor = InstructionGenerator() @@ -159,7 +159,7 @@ async def expand(self, max_children): original_instruction=original_instruction, max_num=max_children, file_path=self.state["exp_pool_path"], - use_fixed_insights=self.use_fixed_insights, + use_fixed_insights=use_fixed_insights, ) new_state = self.state.copy() new_state["start_task_id"] += 1 @@ -259,7 +259,7 @@ def uct(node: Node): return max(all_children, key=uct) async def expand(self, node: Node, max_children=5): - await node.expand(max_children) + await node.expand(max_children, self.use_fixed_insights) if node not in self.children or not self.children[node]: self.children[node] = node.children return node.children From f856d768fe2630c1482cfb8b568f48e97978acc2 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 18:05:02 +0800 Subject: [PATCH 073/135] remove recommendation from di initial prompt, add recommendation to task type prompt --- expo/data/dataset.py | 27 ++------------------------- metagpt/prompts/task_type.py | 3 +++ 2 files changed, 5 insertions(+), 25 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 8af0c485e..9748cb8c2 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -27,30 +27,8 @@ SPECIAL_INSTRUCTIONS = {"ag": USE_AG, "stacking": STACKING} -RECOMMENDATION = """ -## Base Models -You have access to the following base models: -Tabular: -LightGBM, CatBoost, XGBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression - -Image: -ResNet, DenseNet, VGG, Inception, MobileNet, EfficientNet - -Text: -BERT, RoBERTa, DistilBERT, GPT-2 -""" - -# The RECOMMENDATION above is not tested but might be needed for multi-modal datasets - -RECOMMENDATION = """ -## Base Models -You have access to the following base models: -LightGBM, CatBoost, XGBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression -""" - -DI_INSTRUCTION = ( - RECOMMENDATION - + """## Attention +DI_INSTRUCTION = """ +## Attention 1. Please do not leak the target label in any form during training. 2. Test set does not have the target column. 3. When conducting data exploration or analysis, print out the results of your findings. @@ -69,7 +47,6 @@ # Output dir {output_dir} """ -) TASK_PROMPT = """ # User requirement diff --git a/metagpt/prompts/task_type.py b/metagpt/prompts/task_type.py index ca0aae572..6b230fc9e 100644 --- a/metagpt/prompts/task_type.py +++ b/metagpt/prompts/task_type.py @@ -34,6 +34,9 @@ # Prompt for taking on "model_train" tasks MODEL_TRAIN_PROMPT = """ The current task is about training a model, please ensure high performance: +- For tabular datasets - you have access to LightGBM, CatBoost, XGBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression, etc. +- For image datasets - you have access to ResNet, VGG, Inception, MobileNet, DenseNet, EfficientNet, etc. +- For text datasets - you have access to BERT, GPT-2, RoBERTa, DistilBERT, T5, etc. - Keep in mind that your user prioritizes results and is highly focused on model performance. So, when needed, feel free to use models of any complexity to improve effectiveness, such as XGBoost, CatBoost, etc. - If non-numeric columns exist, perform label encode together with all steps. - Use the data from previous task result directly, do not mock or reload data yourself. From 9d2c81a127de86b7ddcdceba6ad0f6db0eac6073 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 20:04:09 +0800 Subject: [PATCH 074/135] fix evaluation bug --- expo/experimenter/experimenter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 89d589d7d..b7a0e0b2f 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -87,7 +87,7 @@ def evaluate_prediction(self, split, state): pred_node_path = os.path.join(state["node_dir"], f"{self.start_time}-{split}_predictions.csv") gt_path = os.path.join(state["datasets_dir"][f"{split}_target"]) preds = pd.read_csv(pred_path) - preds = preds[preds.columns.tolist()[0]] + preds = preds[preds.columns.tolist()[-1]] preds.to_csv(pred_node_path, index=False) gt = pd.read_csv(gt_path)["target"] metric = state["dataset_config"]["metric"] From 9ff9d27ab0c388d1c37af00751181976358433d1 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 20:08:02 +0800 Subject: [PATCH 075/135] include load tree --- expo/experimenter/mcts.py | 1 + 1 file changed, 1 insertion(+) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index e06169a70..f0db72841 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -22,6 +22,7 @@ async def run_experiment(self): state=self.state, reflection=self.args.reflection, rollouts=self.args.rollouts, + load_tree=self.args.load_tree, ) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] From 24db19fa13c70f43297de44330d8b7fe3f702ef7 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 20:33:40 +0800 Subject: [PATCH 076/135] fix start task id consistency --- expo/MCTS.py | 7 +++++-- expo/experimenter/experimenter.py | 3 ++- expo/experimenter/mcts.py | 1 + 3 files changed, 8 insertions(+), 3 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index ef408b2dd..c96c57b47 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -16,12 +16,11 @@ def initialize_di_root_node(state, reflection: bool = True): - start_task_id = 2 # state = create_initial_state( # task, start_task_id=start_task_id, data_config=data_config, low_is_better=low_is_better, name=name # ) role = ResearchAssistant( - node_id="0", start_task_id=start_task_id, use_reflection=reflection, role_dir=state["node_dir"] + node_id="0", start_task_id=state["start_task_id"], use_reflection=reflection, role_dir=state["node_dir"] ) return role, Node(parent=None, state=state, action=None, value=0) @@ -208,6 +207,10 @@ async def run_node(self, role=None): self.raw_reward = score_dict run_finished = True except Exception as e: + print(f"Error: {e}") + import pdb + + pdb.set_trace() mcts_logger.log("MCTS", f"Error in running the role: {e}") num_runs += 1 if not run_finished: diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index b7a0e0b2f..155108f8d 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -13,6 +13,7 @@ class Experimenter: result_path: str = "results/base" data_config = DATA_CONFIG + start_task_id = 1 def __init__(self, args, **kwargs): self.args = args @@ -20,7 +21,7 @@ def __init__(self, args, **kwargs): self.start_time = self.start_time_raw.strftime("%Y%m%d%H%M") self.state = create_initial_state( self.args.task, - start_task_id=1, + start_task_id=self.start_task_id, data_config=self.data_config, low_is_better=self.args.low_is_better, name=self.args.name, diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index f0db72841..89f362b6b 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -6,6 +6,7 @@ class MCTSExperimenter(Experimenter): result_path: str = "results/mcts" + start_task_id = 2 def __init__(self, args, tree_mode=None, **kwargs): super().__init__(args, **kwargs) From 1cdffc3d8550538195fce30cea4d913a24be9c04 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 20:49:49 +0800 Subject: [PATCH 077/135] =?UTF-8?q?FE=20prompt:=20FE=E9=80=9A=E5=B8=B8?= =?UTF-8?q?=E4=B8=8D=E4=BC=9Amake=20changes=E8=80=8C=E6=98=AF=E5=8A=A0?= =?UTF-8?q?=E6=96=B0=E7=9A=84=E7=89=B9=E5=BE=81=20DI=20prompt:=20=E8=A6=81?= =?UTF-8?q?=E6=B1=82=E8=AE=A9predictions=E6=9C=80=E7=BB=88=E7=BB=93?= =?UTF-8?q?=E6=9E=9C=E4=B8=80=E8=87=B4=EF=BC=8C=E5=B9=B6=E6=8F=90=E4=BE=9B?= =?UTF-8?q?=E4=BE=8B=E5=AD=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/data/dataset.py | 1 + metagpt/prompts/task_type.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 9748cb8c2..28bd26d2e 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -40,6 +40,7 @@ 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. - Both files should contain a single column named `target` with the predicted values. 2. Make sure the prediction results are in the same format as the target column in the training set. +- For instance, if the target column is categorical, the prediction results should be categorical as well. ## Output Performance Print the train and dev set performance in the last step. diff --git a/metagpt/prompts/task_type.py b/metagpt/prompts/task_type.py index 6b230fc9e..599d437c5 100644 --- a/metagpt/prompts/task_type.py +++ b/metagpt/prompts/task_type.py @@ -26,7 +26,7 @@ - Avoid creating redundant or excessively numerous features in one step. - Exclude ID columns from feature generation and remove them. - Each feature engineering operation performed on the train set must also applies to the dev/test separately at the same time. -- **ATTENTION** Do NOT use the label column to create features or make any changes to the label column, except for cat encoding. +- **ATTENTION** Do NOT use the label column to create features, except for cat encoding. - Use the data from previous task result if exist, do not mock or reload data yourself. - Always copy the DataFrame before processing it and use the copy to process. """ From 8c6dd480dcddae7bb123f2a2e9dc833db11b7fe7 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 21:10:37 +0800 Subject: [PATCH 078/135] remove pdb --- expo/MCTS.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index c96c57b47..5cd357989 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -208,9 +208,6 @@ async def run_node(self, role=None): run_finished = True except Exception as e: print(f"Error: {e}") - import pdb - - pdb.set_trace() mcts_logger.log("MCTS", f"Error in running the role: {e}") num_runs += 1 if not run_finished: From 3c50575ff7902dbe79d144dee40d0383765fb060 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 21:16:40 +0800 Subject: [PATCH 079/135] make dir at start --- expo/MCTS.py | 1 + 1 file changed, 1 insertion(+) diff --git a/expo/MCTS.py b/expo/MCTS.py index 5cd357989..228671e2c 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -40,6 +40,7 @@ def create_initial_state(task, start_task_id, data_config, low_is_better: bool, "start_task_id": start_task_id, "low_is_better": low_is_better, } + os.makedirs(initial_state["node_dir"], exist_ok=True) return initial_state From ce73f4c25a7551f1c30dae719d57220ec7fb2069 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 14 Sep 2024 21:58:53 +0800 Subject: [PATCH 080/135] lazy import autogluon --- expo/experimenter/autogluon.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index 478ecfc01..93dfdb4bc 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -1,17 +1,19 @@ from datetime import datetime -from autogluon.tabular import TabularDataset, TabularPredictor + from expo.experimenter.custom import CustomExperimenter class AGRunner: preset = "best_quality" - time_limit = 1000 # 1000s + time_limit = 1000 # 1000s def __init__(self, state=None): self.state = state self.datasets = self.state["datasets_dir"] def run(self): + from autogluon.tabular import TabularDataset, TabularPredictor + train_path = self.datasets["train"] dev_wo_target_path = self.datasets["dev_wo_target"] test_wo_target_path = self.datasets["test_wo_target"] @@ -21,7 +23,11 @@ def run(self): test_data = TabularDataset(test_wo_target_path) eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_") # predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, presets=self.preset, time_limit=self.time_limit, fit_weighted_ensemble=False, num_gpus=1) - predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, num_gpus=1) + predictor = TabularPredictor( + label=target_col, + eval_metric=eval_metric, + path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")), + ).fit(train_data, num_gpus=1) dev_preds = predictor.predict(dev_data) test_preds = predictor.predict(test_data) return {"test_preds": test_preds, "dev_preds": dev_preds} @@ -44,4 +50,4 @@ async def run_experiment(self): "test_score": self.evaluate_predictions(test_preds, "test"), } results = [0, {"score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)}] - self.save_result(results) \ No newline at end of file + self.save_result(results) From 9665ebdf4d1a8c2dff63c0b991184f633c7171f7 Mon Sep 17 00:00:00 2001 From: duiyipan Date: Sat, 14 Sep 2024 23:43:57 +0800 Subject: [PATCH 081/135] autosklearn delete per_run_time_limit and change time_limit --- expo/experimenter/autosklearn.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py index 602b8385a..e8923c6bd 100644 --- a/expo/experimenter/autosklearn.py +++ b/expo/experimenter/autosklearn.py @@ -10,7 +10,7 @@ def custom_scorer(y_true, y_pred, metric_name): class ASRunner: - time_limit = 300 + time_limit = 600 def __init__(self, state=None): self.state = state @@ -47,7 +47,6 @@ def run(self): if eval_metric == "rmse": automl = self.autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=self.time_limit, - per_run_time_limit=60, metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, tmp_folder="AutosklearnModels/as-{}-{}".format( @@ -58,7 +57,6 @@ def run(self): elif eval_metric in ["f1", "f1 weighted"]: automl = self.autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=self.time_limit, - per_run_time_limit=60, metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, tmp_folder="AutosklearnModels/as-{}-{}".format( From c007d0bd5e6a89ad53bd4ab7c8504ed329ffb1a5 Mon Sep 17 00:00:00 2001 From: duiyipan Date: Sat, 14 Sep 2024 23:49:46 +0800 Subject: [PATCH 082/135] change import way --- expo/experimenter/autosklearn.py | 22 +++++++++------------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py index e8923c6bd..c6aa70920 100644 --- a/expo/experimenter/autosklearn.py +++ b/expo/experimenter/autosklearn.py @@ -15,19 +15,11 @@ class ASRunner: def __init__(self, state=None): self.state = state self.datasets = self.state["datasets_dir"] - try: - import autosklearn.classification - import autosklearn.regression - import autosklearn.metrics - - self.autosklearn = autosklearn - except ImportError: - raise ImportError( - "autosklearn not found or system not supported, please check it first" - ) def create_autosklearn_scorer(self, metric_name): - return self.autosklearn.metrics.make_scorer( + from autosklearn.metrics import make_scorer + + return make_scorer( name=metric_name, score_func=partial(custom_scorer, metric_name=metric_name) ) @@ -45,7 +37,9 @@ def run(self): y_train = train_data[target_col] if eval_metric == "rmse": - automl = self.autosklearn.regression.AutoSklearnRegressor( + from autosklearn.regression import AutoSklearnRegressor + + automl = AutoSklearnRegressor( time_left_for_this_task=self.time_limit, metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, @@ -55,7 +49,9 @@ def run(self): n_jobs=-1, ) elif eval_metric in ["f1", "f1 weighted"]: - automl = self.autosklearn.classification.AutoSklearnClassifier( + from autosklearn.classification import AutoSklearnClassifier + + automl = AutoSklearnClassifier( time_left_for_this_task=self.time_limit, metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, From 574f1b0e0d2c6b9702097287761bf64a29b8a82f Mon Sep 17 00:00:00 2001 From: duiyipan Date: Sun, 15 Sep 2024 00:01:35 +0800 Subject: [PATCH 083/135] change import way --- expo/experimenter/autosklearn.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py index c6aa70920..9d0ea2df4 100644 --- a/expo/experimenter/autosklearn.py +++ b/expo/experimenter/autosklearn.py @@ -24,6 +24,8 @@ def create_autosklearn_scorer(self, metric_name): ) def run(self): + import autosklearn + train_path = self.datasets["train"] dev_wo_target_path = self.datasets["dev_wo_target"] test_wo_target_path = self.datasets["test_wo_target"] @@ -37,9 +39,7 @@ def run(self): y_train = train_data[target_col] if eval_metric == "rmse": - from autosklearn.regression import AutoSklearnRegressor - - automl = AutoSklearnRegressor( + automl = autosklearn.regression.AutoSklearnRegressor( time_left_for_this_task=self.time_limit, metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, @@ -49,9 +49,7 @@ def run(self): n_jobs=-1, ) elif eval_metric in ["f1", "f1 weighted"]: - from autosklearn.classification import AutoSklearnClassifier - - automl = AutoSklearnClassifier( + automl = autosklearn.classification.AutoSklearnClassifier( time_left_for_this_task=self.time_limit, metric=self.create_autosklearn_scorer(eval_metric), memory_limit=8192, From 0bf2d60248899cae2d4db13a8d5372e80c3443ca Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9E=97=E4=B9=89=E7=AB=A0?= Date: Sun, 15 Sep 2024 05:33:48 +0000 Subject: [PATCH 084/135] rm lgb --- metagpt/prompts/task_type.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/metagpt/prompts/task_type.py b/metagpt/prompts/task_type.py index 599d437c5..e670fe088 100644 --- a/metagpt/prompts/task_type.py +++ b/metagpt/prompts/task_type.py @@ -34,7 +34,7 @@ # Prompt for taking on "model_train" tasks MODEL_TRAIN_PROMPT = """ The current task is about training a model, please ensure high performance: -- For tabular datasets - you have access to LightGBM, CatBoost, XGBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression, etc. +- For tabular datasets - you have access to XGBoost, CatBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression, etc. - For image datasets - you have access to ResNet, VGG, Inception, MobileNet, DenseNet, EfficientNet, etc. - For text datasets - you have access to BERT, GPT-2, RoBERTa, DistilBERT, T5, etc. - Keep in mind that your user prioritizes results and is highly focused on model performance. So, when needed, feel free to use models of any complexity to improve effectiveness, such as XGBoost, CatBoost, etc. From d95c1cb333069e06881a7ff7712ba349c584a5cb Mon Sep 17 00:00:00 2001 From: duiyipan Date: Mon, 16 Sep 2024 14:07:09 +0800 Subject: [PATCH 085/135] fix import error --- expo/experimenter/autosklearn.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/expo/experimenter/autosklearn.py b/expo/experimenter/autosklearn.py index 9d0ea2df4..02a3cc465 100644 --- a/expo/experimenter/autosklearn.py +++ b/expo/experimenter/autosklearn.py @@ -24,7 +24,8 @@ def create_autosklearn_scorer(self, metric_name): ) def run(self): - import autosklearn + import autosklearn.classification + import autosklearn.regression train_path = self.datasets["train"] dev_wo_target_path = self.datasets["dev_wo_target"] From 8dbcd46bfc6f4472dfedf49918eea84ac635b1ed Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 20 Sep 2024 15:53:10 +0800 Subject: [PATCH 086/135] copy notebook to result after mcts --- expo/MCTS.py | 3 +++ expo/experimenter/experimenter.py | 27 ++++++++++++++++++--------- expo/experimenter/mcts.py | 15 ++++++++++++++- expo/research_assistant.py | 2 ++ expo/utils.py | 16 +++++++++------- metagpt/prompts/task_type.py | 1 + 6 files changed, 47 insertions(+), 17 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 228671e2c..aa4ade944 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -87,6 +87,9 @@ def load_node(self): def get_depth(self): return self.depth + def get_node_dir(self): + return self.state["node_dir"] + def generate_depth(self): if self.parent is None: return 0 diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 155108f8d..77cb5fa45 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -2,6 +2,7 @@ import json import os +import numpy as np import pandas as pd from expo.evaluation.evaluation import evaluate_score @@ -58,17 +59,21 @@ async def run_experiment(self): {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} ) self.save_result(results) # save intermediate results - dev_scores = [ - result["score_dict"]["dev_score"] for result in results if result["score_dict"]["dev_score"] != -1 - ] - best_dev_score = max(dev_scores) if not self.args.low_is_better else min(dev_scores) + dev_scores = [result["score_dict"]["dev_score"] for result in results] + best_dev_score = ( + max(dev_scores) + if not self.args.low_is_better + else min([score for score in dev_scores if score != -1] + [np.inf]) + ) best_score_idx = dev_scores.index(best_dev_score) - test_scores = [ - result["score_dict"]["test_score"] for result in results if result["score_dict"]["dev_score"] != -1 - ] + test_scores = [result["score_dict"]["test_score"] for result in results] avg_score = sum(test_scores) / len(test_scores) - global_best_score = max(test_scores) if not self.args.low_is_better else min(test_scores) + global_best_score = ( + max(test_scores) + if not self.args.low_is_better + else min([score for i, score in enumerate(test_scores) if dev_scores[i] != -1] + [np.inf]) + ) results.insert( 0, @@ -103,6 +108,9 @@ def evaluate(self, score_dict, state): score_dict.update(scores) return score_dict + def get_save_name(self): + return f"{self.args.exp_mode}-{self.args.task}_{self.start_time}" + def save_result(self, result): end_time_raw = datetime.datetime.now() end_time = end_time_raw.strftime("%Y%m%d%H%M") @@ -113,6 +121,7 @@ def save_result(self, result): } result = result.copy() result.insert(0, time_info) + save_name = self.get_save_name() os.makedirs(self.result_path, exist_ok=True) - with open(f"{self.result_path}/{self.args.exp_mode}-{self.args.task}_{self.start_time}.json", "w") as f: + with open(f"{self.result_path}/{save_name}.json", "w") as f: json.dump(result, f, indent=4) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 89f362b6b..5fb00ca8d 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -1,3 +1,5 @@ +import shutil + from expo.evaluation.visualize_mcts import get_tree_text from expo.experimenter.experimenter import Experimenter from expo.Greedy import Greedy, Random @@ -28,6 +30,9 @@ async def run_experiment(self): best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] + self.copy_notebook(best_node, "best") + self.copy_notebook(dev_best_node, "dev_best") + text, num_generated_codes = get_tree_text(mcts.root_node) text += f"Generated {num_generated_codes} unique codes.\n" text += f"Best node: {best_node.id}, score: {best_node.raw_reward}\n" @@ -49,7 +54,15 @@ async def run_experiment(self): ] self.save_result(results) + def copy_notebook(self, node, name): + node_dir = node.get_node_dir() + node_nb_dir = f"{node_dir}/Node-{node.id}.ipynb" + save_name = self.get_save_name() + copy_nb_dir = f"{self.result_path}/{save_name}_{name}.ipynb" + shutil.copy(node_nb_dir, copy_nb_dir) + def save_tree(self, tree_text): - fpath = f"{self.result_path}/{self.args.task}_tree_{self.args.name}.txt" + save_name = self.get_save_name() + fpath = f"{self.result_path}/{save_name}_tree.txt" with open(fpath, "w") as f: f.write(tree_text) diff --git a/expo/research_assistant.py b/expo/research_assistant.py index b21fc1a55..51de188d3 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -111,6 +111,8 @@ async def _act_on_task(self, current_task: Task) -> TaskResult: if int(current_task.task_id) == self.start_task_id + 1: # fe_id = current_task.dependent_task_ids self.save_state() + save_notebook(role=self, save_dir=self.role_dir, name=self.get_node_name(), save_to_depth=True) + else: save_notebook(role=self, save_dir=self.role_dir, name=self.get_node_name()) return task_result diff --git a/expo/utils.py b/expo/utils.py index f3c0c392d..56f3c21b9 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -91,19 +91,21 @@ def process_cells(nb: NotebookNode) -> NotebookNode: return nb -def save_notebook(role: Role, save_dir: str = "", name: str = ""): +def save_notebook(role: Role, save_dir: str = "", name: str = "", save_to_depth=False): save_dir = Path(save_dir) tasks = role.planner.plan.tasks - codes = [task.code for task in tasks if task.code] - clean_nb = nbformat.v4.new_notebook() - for code in codes: - clean_nb.cells.append(nbformat.v4.new_code_cell(code)) nb = process_cells(role.execute_code.nb) os.makedirs(save_dir, exist_ok=True) file_path = save_dir / f"{name}.ipynb" - clean_file_path = save_dir / f"{name}_clean.ipynb" nbformat.write(nb, file_path) - nbformat.write(clean_nb, clean_file_path) + + if save_to_depth: + clean_file_path = save_dir / f"{name}_clean.ipynb" + codes = [task.code for task in tasks if task.code] + clean_nb = nbformat.v4.new_notebook() + for code in codes: + clean_nb.cells.append(nbformat.v4.new_code_cell(code)) + nbformat.write(clean_nb, clean_file_path) async def load_execute_notebook(role): diff --git a/metagpt/prompts/task_type.py b/metagpt/prompts/task_type.py index e670fe088..97666874d 100644 --- a/metagpt/prompts/task_type.py +++ b/metagpt/prompts/task_type.py @@ -37,6 +37,7 @@ - For tabular datasets - you have access to XGBoost, CatBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression, etc. - For image datasets - you have access to ResNet, VGG, Inception, MobileNet, DenseNet, EfficientNet, etc. - For text datasets - you have access to BERT, GPT-2, RoBERTa, DistilBERT, T5, etc. +- Avoid the use of SVM because of its high training time. - Keep in mind that your user prioritizes results and is highly focused on model performance. So, when needed, feel free to use models of any complexity to improve effectiveness, such as XGBoost, CatBoost, etc. - If non-numeric columns exist, perform label encode together with all steps. - Use the data from previous task result directly, do not mock or reload data yourself. From 2f78d57e10726d7d0a2faffd320e4df77d0ac518 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 23 Sep 2024 16:46:34 +0800 Subject: [PATCH 087/135] fix random search --- expo/experimenter/aug.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index e57d024bd..ffe0d04c5 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -24,9 +24,11 @@ async def run_experiment(self): exps = InstructionGenerator._random_sample(exp_pool, self.args.num_experiments) exps = [exp["Analysis"] for exp in exps] elif self.args.aug_mode == "set": - exp_set = InstructionGenerator.sample_instruction_set(exp_pool) - exp_set_text = "\n".join([f"{exp['task_id']}: {exp['Analysis']}" for exp in exp_set]) - exps = [exp_set_text] * self.args.num_experiments + exps = [] + for i in range(self.args.num_experiments): + exp_set = InstructionGenerator.sample_instruction_set(exp_pool) + exp_set_text = "\n".join([f"{exp['task_id']}: {exp['Analysis']}" for exp in exp_set]) + exps.append(exp_set_text) else: raise ValueError(f"Invalid mode: {self.args.aug_mode}") From 6344046c31d0d4c673ddfcd6c9b041f0dbfc3e6f Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 24 Sep 2024 20:48:44 +0800 Subject: [PATCH 088/135] update aug result summarization --- expo/experimenter/aug.py | 6 +----- expo/experimenter/experimenter.py | 29 +++++++++++++++++------------ 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index ffe0d04c5..97b819802 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -49,9 +49,5 @@ async def run_experiment(self): "args": vars(self.args), } ) - scores = [result["score_dict"]["test_score"] for result in results] - avg_score = sum(scores) / len(scores) - best_score = max(scores) if not self.args.low_is_better else min(scores) - best_score_idx = scores.index(best_score) - results.insert(0, {"avg_score": avg_score, "best_score": best_score, "best_score_idx": best_score_idx}) + results = self.summarize_results(results) self.save_result(results) diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 77cb5fa45..c6ead281b 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -47,18 +47,7 @@ async def run_di(self, di, user_requirement, run_idx): score_dict = {"train_score": -1, "dev_score": -1, "test_score": -1, "score": -1} return score_dict - async def run_experiment(self): - state = self.state - user_requirement = state["requirement"] - results = [] - - for i in range(self.args.num_experiments): - di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) - score_dict = await self.run_di(di, user_requirement, run_idx=i) - results.append( - {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} - ) - self.save_result(results) # save intermediate results + def summarize_results(self, results): dev_scores = [result["score_dict"]["dev_score"] for result in results] best_dev_score = ( max(dev_scores) @@ -85,6 +74,22 @@ async def run_experiment(self): "global_best_test_score": global_best_score, }, ) + return results + + async def run_experiment(self): + state = self.state + user_requirement = state["requirement"] + results = [] + + for i in range(self.args.num_experiments): + di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) + score_dict = await self.run_di(di, user_requirement, run_idx=i) + results.append( + {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} + ) + self.save_result(results) # save intermediate results + results = self.summarize_results(results) + self.save_result(results) def evaluate_prediction(self, split, state): From 31adaee23f919da61e8b949a1651d36a734de105 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 24 Sep 2024 20:58:41 +0800 Subject: [PATCH 089/135] add special instruction for img/text dataset --- expo/data/dataset.py | 13 ++++++++++++- expo/run_experiment.py | 4 ++-- 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 28bd26d2e..8ad6f2854 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -20,12 +20,23 @@ 7. Please use autogluon for model training with presets='medium_quality', time_limit=None, give dev dataset to tuning_data, and use right eval_metric. """ +TEXT_MODALITY = """ +7. You could use models from transformers library for this text dataset. +8. Use gpu if available for faster training. +""" + +IMAGE_MODALITY = """ +7. You could use models from torchvision library for this image dataset. +8. Use gpu if available for faster training. +""" + STACKING = """ 7. To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor. 8. You could do some quick model prototyping to see which models work best and then use them in the ensemble. """ -SPECIAL_INSTRUCTIONS = {"ag": USE_AG, "stacking": STACKING} + +SPECIAL_INSTRUCTIONS = {"ag": USE_AG, "stacking": STACKING, "text": TEXT_MODALITY, "image": IMAGE_MODALITY} DI_INSTRUCTION = """ ## Attention diff --git a/expo/run_experiment.py b/expo/run_experiment.py index be028c47e..8dd66577c 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -3,10 +3,10 @@ from expo.experimenter.aug import AugExperimenter from expo.experimenter.autogluon import GluonExperimenter +from expo.experimenter.autosklearn import AutoSklearnExperimenter from expo.experimenter.custom import CustomExperimenter from expo.experimenter.experimenter import Experimenter from expo.experimenter.mcts import MCTSExperimenter -from expo.experimenter.autosklearn import AutoSklearnExperimenter def get_args(): @@ -43,7 +43,7 @@ def get_di_args(parser): parser.add_argument("--reflection", dest="reflection", action="store_true") parser.add_argument("--no_reflection", dest="reflection", action="store_false") parser.add_argument("--num_experiments", type=int, default=1) - parser.add_argument("--special_instruction", type=str, default=None, choices=["ag", "stacking"]) + parser.add_argument("--special_instruction", type=str, default=None, choices=["ag", "stacking", "text", "image"]) parser.set_defaults(reflection=True) From 68c672d4381dfee21b15e682aba5801bdc3934a8 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 25 Sep 2024 09:59:12 +0800 Subject: [PATCH 090/135] use transformers lib instead of torchvision --- expo/data/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 8ad6f2854..f2f01f71b 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -26,7 +26,7 @@ """ IMAGE_MODALITY = """ -7. You could use models from torchvision library for this image dataset. +7. You could use models from transformers library for this image dataset. 8. Use gpu if available for faster training. """ From e2cee3905f92f661a7bacd2be102a0bf9428ffff Mon Sep 17 00:00:00 2001 From: Rayhao Date: Wed, 25 Sep 2024 22:58:04 -0700 Subject: [PATCH 091/135] add autogluon multimodal support --- expo/README.md | 2 + expo/experimenter/autogluon.py | 74 +++++++++++++++++++++++++++++++++- 2 files changed, 75 insertions(+), 1 deletion(-) diff --git a/expo/README.md b/expo/README.md index 011322897..3f9e630e5 100644 --- a/expo/README.md +++ b/expo/README.md @@ -215,6 +215,8 @@ python experimenter/aide.py pip install -U pip pip install -U setuptools wheel pip install autogluon + +python run_expriment.py --exp_mode autogluon --task fashion_mnist ``` 提供github链接，并说明使用的命令以及参数设置 diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index 93dfdb4bc..4bcba432c 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -32,6 +32,77 @@ def run(self): test_preds = predictor.predict(test_data) return {"test_preds": test_preds, "dev_preds": dev_preds} + def run_images(self): + from autogluon.multimodal import MultiModalPredictor + target_col = self.state["dataset_config"]["target_col"] + train_path = self.datasets["train"] + dev_path = self.datasets["dev"] + dev_wo_target_path = self.datasets["dev_wo_target"] # Updated variable name + test_wo_target_path = self.datasets["test_wo_target"] + eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_") + + # Load the datasets + train_data, dev_data, dev_wo_target_data, test_data = self.load_split_dataset( + train_path, dev_path, dev_wo_target_path, test_wo_target_path + ) + + # Create and fit the predictor + predictor = MultiModalPredictor( + label=target_col, + eval_metric=eval_metric, + path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")), + ).fit(train_data=train_data, tuning_data=dev_data, time_limit=self.time_limit) + + # Make predictions on dev and test datasets + dev_preds = predictor.predict(dev_wo_target_data) + test_preds = predictor.predict(test_data) + + # Return predictions for dev and test datasets + return { + "dev_preds": dev_preds, + "test_preds": test_preds + } + + def load_split_dataset(self, train_path, dev_path, dev_wo_target_path, test_wo_target_path): + import os + import pandas as pd + """ + Loads training, dev, and test datasets from given file paths + + Args: + train_path (str): Path to the training dataset. + dev_path (str): Path to the dev dataset with target labels. + dev_wo_target_path (str): Path to the dev dataset without target labels. + test_wo_target_path (str): Path to the test dataset without target labels. + + Returns: + train_data (pd.DataFrame): Loaded training dataset with updated image paths. + dev_data (pd.DataFrame): Loaded dev dataset with updated image paths. + dev_wo_target_data (pd.DataFrame): Loaded dev dataset without target labels and updated image paths. + test_data (pd.DataFrame): Loaded test dataset with updated image paths. + """ + + # Define the root path to append + root_folder = os.path.join("F:/Download/Dataset/", self.state["task"]) + + # Load the datasets + train_data = pd.read_csv(train_path) + dev_data = pd.read_csv(dev_path) # Load dev dataset with target labels + dev_wo_target_data = pd.read_csv(dev_wo_target_path) # Load dev dataset without target labels + test_data = pd.read_csv(test_wo_target_path) + + + # Get the name of the first column (assuming it's the image path column) + + image_column = train_data.columns[0] + # Append root folder path to the image column in each dataset + train_data[image_column] = train_data[image_column].apply(lambda x: os.path.join(root_folder, x)) + dev_data[image_column] = dev_data[image_column].apply(lambda x: os.path.join(root_folder, x)) + dev_wo_target_data[image_column] = dev_wo_target_data[image_column].apply( + lambda x: os.path.join(root_folder, x)) + test_data[image_column] = test_data[image_column].apply(lambda x: os.path.join(root_folder, x)) + return train_data, dev_data, dev_wo_target_data, test_data + class GluonExperimenter(CustomExperimenter): result_path: str = "results/autogluon" @@ -41,7 +112,8 @@ def __init__(self, args, **kwargs): self.framework = AGRunner(self.state) async def run_experiment(self): - result = self.framework.run() + # result = self.framework.run() + result = self.framework.run_images() user_requirement = self.state["requirement"] dev_preds = result["dev_preds"] test_preds = result["test_preds"] From 1a1855f21a7379af2099e5a3cc01cace085ea8d8 Mon Sep 17 00:00:00 2001 From: Rayhao Date: Wed, 25 Sep 2024 23:28:16 -0700 Subject: [PATCH 092/135] add input param for autogluon --- expo/README.md | 12 +++++++++++- expo/experimenter/autogluon.py | 25 +++++++++++++------------ expo/run_experiment.py | 1 + 3 files changed, 25 insertions(+), 13 deletions(-) diff --git a/expo/README.md b/expo/README.md index 3f9e630e5..e5da96708 100644 --- a/expo/README.md +++ b/expo/README.md @@ -216,9 +216,19 @@ pip install -U pip pip install -U setuptools wheel pip install autogluon -python run_expriment.py --exp_mode autogluon --task fashion_mnist ``` +For Tabular data: +``` +python run_expriment.py --exp_mode autogluon --task {task_name} +``` +For Multimodal data: +``` +python run_expriment.py --exp_mode autogluon --task {task_name} --is_multimodal +``` +Replace {task_name} with the specific task you want to run. + + 提供github链接，并说明使用的命令以及参数设置 ### AutoSklearn #### System requirements diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index 4bcba432c..6cb3797e3 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -1,12 +1,10 @@ from datetime import datetime - from expo.experimenter.custom import CustomExperimenter +import os +import pandas as pd class AGRunner: - preset = "best_quality" - time_limit = 1000 # 1000s - def __init__(self, state=None): self.state = state self.datasets = self.state["datasets_dir"] @@ -32,7 +30,7 @@ def run(self): test_preds = predictor.predict(test_data) return {"test_preds": test_preds, "dev_preds": dev_preds} - def run_images(self): + def run_multimodal(self): from autogluon.multimodal import MultiModalPredictor target_col = self.state["dataset_config"]["target_col"] train_path = self.datasets["train"] @@ -51,7 +49,7 @@ def run_images(self): label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")), - ).fit(train_data=train_data, tuning_data=dev_data, time_limit=self.time_limit) + ).fit(train_data=train_data, tuning_data=dev_data) # Make predictions on dev and test datasets dev_preds = predictor.predict(dev_wo_target_data) @@ -64,8 +62,6 @@ def run_images(self): } def load_split_dataset(self, train_path, dev_path, dev_wo_target_path, test_wo_target_path): - import os - import pandas as pd """ Loads training, dev, and test datasets from given file paths @@ -91,16 +87,16 @@ def load_split_dataset(self, train_path, dev_path, dev_wo_target_path, test_wo_t dev_wo_target_data = pd.read_csv(dev_wo_target_path) # Load dev dataset without target labels test_data = pd.read_csv(test_wo_target_path) - # Get the name of the first column (assuming it's the image path column) - image_column = train_data.columns[0] + # Append root folder path to the image column in each dataset train_data[image_column] = train_data[image_column].apply(lambda x: os.path.join(root_folder, x)) dev_data[image_column] = dev_data[image_column].apply(lambda x: os.path.join(root_folder, x)) dev_wo_target_data[image_column] = dev_wo_target_data[image_column].apply( lambda x: os.path.join(root_folder, x)) test_data[image_column] = test_data[image_column].apply(lambda x: os.path.join(root_folder, x)) + return train_data, dev_data, dev_wo_target_data, test_data @@ -110,10 +106,15 @@ class GluonExperimenter(CustomExperimenter): def __init__(self, args, **kwargs): super().__init__(args, **kwargs) self.framework = AGRunner(self.state) + self.is_multimodal = args.is_multimodal if hasattr(args, 'is_multimodal') else False async def run_experiment(self): - # result = self.framework.run() - result = self.framework.run_images() + if not self.is_multimodal: + result = self.framework.run() + else: + result = self.framework.run_multimodal() + + assert result is not None user_requirement = self.state["requirement"] dev_preds = result["dev_preds"] test_preds = result["test_preds"] diff --git a/expo/run_experiment.py b/expo/run_experiment.py index be028c47e..038b57ad2 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -34,6 +34,7 @@ def get_mcts_args(parser): def get_aug_exp_args(parser): parser.add_argument("--aug_mode", type=str, default="single", choices=["single", "set"]) + parser.add_argument("--is_multimodal", action="store_true", help="Specify if the model is multi-modal") def get_di_args(parser): From 3c397387f9991baa04a02b8f1e41178375f1db6f Mon Sep 17 00:00:00 2001 From: Rayhao Date: Wed, 25 Sep 2024 23:32:52 -0700 Subject: [PATCH 093/135] add tuning data for tabular mode --- expo/experimenter/autogluon.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index 6cb3797e3..e5e3045f1 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -1,5 +1,6 @@ from datetime import datetime from expo.experimenter.custom import CustomExperimenter +from autogluon.tabular import TabularDataset, TabularPredictor import os import pandas as pd @@ -10,23 +11,22 @@ def __init__(self, state=None): self.datasets = self.state["datasets_dir"] def run(self): - from autogluon.tabular import TabularDataset, TabularPredictor - train_path = self.datasets["train"] + dev_path = self.datasets["dev"] dev_wo_target_path = self.datasets["dev_wo_target"] test_wo_target_path = self.datasets["test_wo_target"] target_col = self.state["dataset_config"]["target_col"] train_data = TabularDataset(train_path) - dev_data = TabularDataset(dev_wo_target_path) + dev_data = TabularDataset(dev_path) + dev_wo_target_data = TabularDataset(dev_wo_target_path) test_data = TabularDataset(test_wo_target_path) eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_") - # predictor = TabularPredictor(label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state['task'], datetime.now().strftime("%y%m%d_%H%M"))).fit(train_data, presets=self.preset, time_limit=self.time_limit, fit_weighted_ensemble=False, num_gpus=1) predictor = TabularPredictor( label=target_col, eval_metric=eval_metric, path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")), - ).fit(train_data, num_gpus=1) - dev_preds = predictor.predict(dev_data) + ).fit(train_data=train_data, tuning_data=dev_data, num_gpus=1) + dev_preds = predictor.predict(dev_wo_target_data) test_preds = predictor.predict(test_data) return {"test_preds": test_preds, "dev_preds": dev_preds} From 2b67355358a6ce9f490fd1b9b6970f7010789857 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 26 Sep 2024 20:25:36 +0800 Subject: [PATCH 094/135] add step score --- expo/MCTS.py | 31 ++++++++++++++++++++++++++++++- expo/experimenter/mcts.py | 2 ++ 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index aa4ade944..4564cd682 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -1,3 +1,4 @@ +import json import math import os import pickle @@ -240,6 +241,7 @@ class MCTS: max_depth: int = 5 c_explore: float = 1.4 c_unvisited: float = 0.8 + node_order: list = [] def __init__(self, root_node, max_depth, use_fixed_insights): self.root_node = root_node @@ -306,11 +308,32 @@ def bfs(node: Node, best_score, best_child: Node, split): _, global_best_child = bfs(root, global_best_score, best_child, "test_score") _, dev_best_child = bfs(root, dev_best_score, best_child, "dev_score") - return {"dev_best": dev_best_child, "global_best": global_best_child} + return {"dev_best": dev_best_child, "global_best": global_best_child, "scores": self.get_score_order_dict()} def get_num_simulations(self): return self.root_node.visited + def save_node_order(self, node_id): + self.node_order.append(node_id) + with open(os.path.join(self.root_node.state["node_dir"], "node_order.json"), "w") as f: + json.dump(self.node_order, f) + + def load_node_order(self): + with open(os.path.join(self.root_node.state["node_dir"], "node_order.json"), "r") as f: + self.node_order = json.load(f) + + def get_score_order_dict(self): + scores = {"dev": [], "test": [], "dev_raw": [], "test_raw": []} + for node_id in self.node_order: + node = Node(parent=None, state=self.root_node.state, action=None, value=0) + node.id = node_id + node = node.load_node() + scores["dev"].append(node.normalized_reward["dev_score"]) + scores["test"].append(node.normalized_reward["test_score"]) + scores["dev_raw"].append(node.raw_reward["dev_score"]) + scores["test_raw"].append(node.raw_reward["test_score"]) + return scores + async def search(self, state, rollouts, load_tree=False, reflection=False): role, root = initialize_di_root_node(state, reflection=reflection) self.root_node = root @@ -329,8 +352,12 @@ async def search(self, state, rollouts, load_tree=False, reflection=False): self.backpropagate(root, reward) node, reward = await self.expand_and_simulate(root) # self.backpropagate(node, reward) + self.save_node_order(root.id) + self.save_node_order(node.id) else: root = self.root_node + self.load_node_order() + for _ in range(rollouts): # number of rollouts mcts_logger.log("MCTS", f"Start the next rollout {_+1}") node = self.select(root) @@ -344,6 +371,7 @@ async def search(self, state, rollouts, load_tree=False, reflection=False): else: node, reward = await self.expand_and_simulate(node) # self.backpropagate(node, reward) + self.save_node_order(node.id) return self.best_path(root) async def expand_and_simulate(self, node): @@ -373,6 +401,7 @@ def load_children_node(node): self.root_node = pickle.load(f) self.children[self.root_node] = self.root_node.children load_children_node(self.root_node) + if self.children: return True return False diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 5fb00ca8d..bd803bff1 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -29,6 +29,7 @@ async def run_experiment(self): ) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] + score_dict = best_nodes["scores"] self.copy_notebook(best_node, "best") self.copy_notebook(dev_best_node, "dev_best") @@ -50,6 +51,7 @@ async def run_experiment(self): "user_requirement": best_node.state["requirement"], "tree_text": text, "args": vars(self.args), + "scores": score_dict, } ] self.save_result(results) From e2c82249b37e78bfe8c0cb2f34050db8c06021db Mon Sep 17 00:00:00 2001 From: Rayhao Date: Thu, 26 Sep 2024 21:13:48 -0700 Subject: [PATCH 095/135] import issue --- expo/experimenter/autogluon.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py index e5e3045f1..dabf0c138 100644 --- a/expo/experimenter/autogluon.py +++ b/expo/experimenter/autogluon.py @@ -1,6 +1,5 @@ from datetime import datetime from expo.experimenter.custom import CustomExperimenter -from autogluon.tabular import TabularDataset, TabularPredictor import os import pandas as pd @@ -11,6 +10,7 @@ def __init__(self, state=None): self.datasets = self.state["datasets_dir"] def run(self): + from autogluon.tabular import TabularDataset, TabularPredictor train_path = self.datasets["train"] dev_path = self.datasets["dev"] dev_wo_target_path = self.datasets["dev_wo_target"] From 24536df24c24b80b870c96248bfc46fdd07ec929 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 27 Sep 2024 12:54:22 +0800 Subject: [PATCH 096/135] update_save_order --- expo/experimenter/mcts.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index bd803bff1..fa42cb070 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -31,9 +31,6 @@ async def run_experiment(self): dev_best_node = best_nodes["dev_best"] score_dict = best_nodes["scores"] - self.copy_notebook(best_node, "best") - self.copy_notebook(dev_best_node, "dev_best") - text, num_generated_codes = get_tree_text(mcts.root_node) text += f"Generated {num_generated_codes} unique codes.\n" text += f"Best node: {best_node.id}, score: {best_node.raw_reward}\n" @@ -55,6 +52,8 @@ async def run_experiment(self): } ] self.save_result(results) + self.copy_notebook(best_node, "best") + self.copy_notebook(dev_best_node, "dev_best") def copy_notebook(self, node, name): node_dir = node.get_node_dir() From af844693b1510655223edc8c879cc9f14f9140e0 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 27 Sep 2024 15:13:23 +0800 Subject: [PATCH 097/135] change label column --- expo/data/hf_data.py | 4 ++-- expo/datasets.yaml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index df3a6ed20..a43fcd415 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -18,7 +18,7 @@ "name": "oxford-iiit-pet", "dataset_name": "timm/oxford-iiit-pet", "image_col": "image", - "target_col": "label_cat_dog", + "target_col": "label", "modality": "image", }, { @@ -115,7 +115,7 @@ def get_dataset_info(self): if __name__ == "__main__": dataset_dir = "D:/work/automl/datasets" - save_analysis_pool = False + save_analysis_pool = True force_update = False datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() diff --git a/expo/datasets.yaml b/expo/datasets.yaml index 92e004c6d..e58e717b5 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -202,7 +202,7 @@ datasets: oxford-iiit-pet: dataset: oxford-iiit-pet metric: f1 - target_col: label_cat_dog + target_col: label user_requirement: "This is a oxford-iiit-pet dataset. Your goal is to predict\ \ the target column `label_cat_dog`.\nPerform data analysis, data preprocessing,\ \ feature engineering, and modeling to predict the target. \nReport f1 on the\ From bd26e900e6b33c9e16c6246958e977b356dd0a5a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 28 Sep 2024 10:07:35 +0800 Subject: [PATCH 098/135] update target label --- expo/datasets.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/expo/datasets.yaml b/expo/datasets.yaml index e58e717b5..016daf7ec 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -201,10 +201,10 @@ datasets: \ Do not plot or make any visualizations.\n" oxford-iiit-pet: dataset: oxford-iiit-pet - metric: f1 + metric: f1 weighted target_col: label user_requirement: "This is a oxford-iiit-pet dataset. Your goal is to predict\ - \ the target column `label_cat_dog`.\nPerform data analysis, data preprocessing,\ + \ the target column `label`.\nPerform data analysis, data preprocessing,\ \ feature engineering, and modeling to predict the target. \nReport f1 on the\ \ eval data. Do not plot or make any visualizations.\n" sms_spam: From 06702db6d15deca1722027af9399173b9b647259 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 28 Sep 2024 10:39:12 +0800 Subject: [PATCH 099/135] update pet --- expo/datasets.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/datasets.yaml b/expo/datasets.yaml index 016daf7ec..2d02951d4 100644 --- a/expo/datasets.yaml +++ b/expo/datasets.yaml @@ -205,7 +205,7 @@ datasets: target_col: label user_requirement: "This is a oxford-iiit-pet dataset. Your goal is to predict\ \ the target column `label`.\nPerform data analysis, data preprocessing,\ - \ feature engineering, and modeling to predict the target. \nReport f1 on the\ + \ feature engineering, and modeling to predict the target. \nReport f1 weighted on the\ \ eval data. Do not plot or make any visualizations.\n" sms_spam: dataset: sms_spam From db247d9ff4dd8a10279bd8e1d232d552c8a2b324 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 28 Sep 2024 15:01:01 +0800 Subject: [PATCH 100/135] save result order --- expo/experimenter/mcts.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index fa42cb070..22b480caf 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -36,8 +36,6 @@ async def run_experiment(self): text += f"Best node: {best_node.id}, score: {best_node.raw_reward}\n" text += f"Dev best node: {dev_best_node.id}, score: {dev_best_node.raw_reward}\n" print(text) - self.save_tree(text) - results = [ { "best_node": best_node.id, @@ -54,6 +52,7 @@ async def run_experiment(self): self.save_result(results) self.copy_notebook(best_node, "best") self.copy_notebook(dev_best_node, "dev_best") + self.save_tree(text) def copy_notebook(self, node, name): node_dir = node.get_node_dir() From 1589a04cdbf14ef14549bd09141483191b85929b Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 28 Sep 2024 18:27:23 +0800 Subject: [PATCH 101/135] clarify prediction saving prompt --- expo/data/dataset.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index f2f01f71b..dd4cb4543 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -26,7 +26,7 @@ """ IMAGE_MODALITY = """ -7. You could use models from transformers library for this image dataset. +7. You could use models from transformers/torchvision library for this image dataset. 8. Use gpu if available for faster training. """ @@ -50,8 +50,8 @@ ## Saving Dev and Test Predictions 1. Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. - Both files should contain a single column named `target` with the predicted values. -2. Make sure the prediction results are in the same format as the target column in the training set. -- For instance, if the target column is categorical, the prediction results should be categorical as well. +2. Make sure the prediction results are in the same format as the target column in the original training set. +- For instance, if the original target column is a list of string, the prediction results should also be strings. ## Output Performance Print the train and dev set performance in the last step. From 788e42ea55e80682221e10b7f7cf56daa3c102fe Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 30 Sep 2024 16:06:48 +0800 Subject: [PATCH 102/135] update model list --- metagpt/prompts/task_type.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/metagpt/prompts/task_type.py b/metagpt/prompts/task_type.py index 97666874d..74286a28f 100644 --- a/metagpt/prompts/task_type.py +++ b/metagpt/prompts/task_type.py @@ -35,8 +35,8 @@ MODEL_TRAIN_PROMPT = """ The current task is about training a model, please ensure high performance: - For tabular datasets - you have access to XGBoost, CatBoost, random forest, extremely randomized trees, k-nearest neighbors, linear regression, etc. -- For image datasets - you have access to ResNet, VGG, Inception, MobileNet, DenseNet, EfficientNet, etc. -- For text datasets - you have access to BERT, GPT-2, RoBERTa, DistilBERT, T5, etc. +- For image datasets - you have access to Swin Transformer, ViT, ResNet, EfficientNet, etc. +- For text datasets - you have access to Electra, DeBERTa, GPT-2, BERT, etc. - Avoid the use of SVM because of its high training time. - Keep in mind that your user prioritizes results and is highly focused on model performance. So, when needed, feel free to use models of any complexity to improve effectiveness, such as XGBoost, CatBoost, etc. - If non-numeric columns exist, perform label encode together with all steps. From f80ebc4d67d80bf9db2caa70ebc9bdfeca468692 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 10 Oct 2024 16:30:07 +0800 Subject: [PATCH 103/135] =?UTF-8?q?1.=20add=20role=20level=20timeout=20?= =?UTF-8?q?=E9=99=90=E5=88=B6=E6=98=AF1000s=202.=20=E4=BF=AE=E6=94=B9log?= =?UTF-8?q?=E7=9A=84=E5=B1=82=E7=BA=A7=E9=80=BB=E8=BE=91=203.=20data.yaml?= =?UTF-8?q?=20=E5=8F=AA=E7=94=A8=E4=BA=8E=E5=AD=98=E8=B7=AF=E5=BE=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/MCTS.py | 6 +- expo/README.md | 115 +++++++++++++-------------- expo/data.yaml | 159 +------------------------------------ expo/research_assistant.py | 37 +++++++-- expo/utils.py | 14 ++-- 5 files changed, 97 insertions(+), 234 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 4564cd682..8e685cc0a 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -10,7 +10,7 @@ from expo.data.dataset import generate_task_requirement, get_split_dataset_path from expo.evaluation.evaluation import evaluate_score from expo.insights.instruction_generator import InstructionGenerator -from expo.research_assistant import ResearchAssistant +from expo.research_assistant import ResearchAssistant, TimeoutException from expo.utils import get_exp_pool_path, load_execute_notebook, mcts_logger from metagpt.tools.tool_recommend import ToolRecommender from metagpt.utils.common import read_json_file @@ -211,10 +211,14 @@ async def run_node(self, role=None): score_dict = self.evaluate_simulation(score_dict) self.raw_reward = score_dict run_finished = True + except TimeoutException as e: + mcts_logger.log("MCTS", f"Role-level timeout: {e}") + break except Exception as e: print(f"Error: {e}") mcts_logger.log("MCTS", f"Error in running the role: {e}") num_runs += 1 + if not run_finished: mcts_logger.log("MCTS", f"Role {role.node_id} failed to run") if self.state["low_is_better"]: diff --git a/expo/README.md b/expo/README.md index e5da96708..0a807c928 100644 --- a/expo/README.md +++ b/expo/README.md @@ -1,21 +1,21 @@ -# Expo +# SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning +![pipeline](resources/MCTS-Experimenter.jpg) ## 1. Data Preparation -- 下载数据集：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink -- 修改`data.yaml`的`datasets_dir`为数据集合集根目录存储位置 +- Download Datasets：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink ## 2. Configs ### Data Config -`datasets.yaml` 提供数据集对应的指标和基础提示词 +`datasets.yaml` Provide base prompts, metrics, target columns for respective datasets -`data.yaml` 继承了`datasets.yaml`以及一些路径信息，需要将`datasets_dir`指到数据集合集的根目录下 +- Modify `datasets_dir` to the root directory of all the datasets in `data.yaml` ### LLM Config @@ -30,28 +30,64 @@ llm: ``` ### Budget -实验轮次 k = 10, 20 +Experiment rollouts k = 5, 10, 20 ### Prompt Usage -- 通过执行`dataset.py`中的`generate_task_requirement`函数获取提示词 - - 非DI-based方法设置`is_di=False` - - `data_config`用`utils.DATA_CONFIG` -- 每一个数据集里有`dataset_info.json`，里面的内容需要提供给baselines以保证公平（`generate_task_requirement`已经默认提供） +- Use the function `generate_task_requirement` in `dataset.py` to get task requirement. + - If the method is non-DI-based, set `is_di=False`. + - Use `utils.DATA_CONFIG` as `data_config` -## 3. Evaluation +## 3. SELA -运行各个框架，运行后框架需要提供Dev和Test的`dev_predictions.csv`和`test_predictions.csv`，每个csv文件只需要单个名为target的列 +### Run SELA + +#### Setup +In the root directory, -- 使用`CustomExperimenter` ``` -experimenter = CustomExperimenter(task="titanic") -score_dict = experimenter.evaluate_pred_files(dev_pred_path, test_pred_path) +pip install -e . + +cd expo + +pip install -r requirements.txt ``` -## 4. Baselines +#### Run + +- `python run_experiment.py --exp_mode mcts --task titanic --rollouts 10` + +If the dataset has reg metric, remember to use `--low_is_better`: + +- `python run_experiment.py --exp_mode mcts --task house_prices --rollouts 10 --low_is_better` + + +In addition to the generated insights, include the fixed insights saved in `expo/insights/fixed_insights.json` +- `--use_fixed_insights` + + + +#### Ablation Study + +**DI RandomSearch** + +- Single insight +`python run_experiment.py --exp_mode aug --task titanic --aug_mode single` + +- Set insight +`python run_experiment.py --exp_mode aug --task titanic --aug_mode set` + + +## 4. Evaluation + +Each baseline needs to produce `dev_predictions.csv`和`test_predictions.csv`. Each csv file only needs a `target` column. + +- Use the function `evaluate_score` to evaluate. + + +## 5. Baselines ### DS Agent ``` git clone https://github.com/guosyjlu/DS-Agent.git @@ -257,55 +293,14 @@ python run_experiment.py --exp_mode autosklearn --task titanic ``` ### Base DI -For setup, check 5. - +For setup, check 4. - `python run_experiment.py --exp_mode base --task titanic --num_experiments 10` -- Ask DI to use AutoGluon: `--special_instruction ag` -- Ask DI to use the stacking ensemble method: `--special_instruction stacking` +- Specifically instruct DI to use AutoGluon: `--special_instruction ag` +- Specifically instruct DI to use the stacking ensemble method: `--special_instruction stacking` -## 5. DI MCTS - -### Run DI MCTS - -#### Setup -In the root directory, - -``` -pip install -e . - -cd expo - -pip install -r requirements.txt -``` - -#### Run - -- `python run_experiment.py --exp_mode mcts --task titanic --rollout 10` - -If the dataset has reg metric, remember to use `--low_is_better`: - -- `python run_experiment.py --exp_mode mcts --task househouse_prices --rollout 10 --low_is_better` - - -In addition to the generated insights, include the fixed insights saved in `expo/insights/fixed_insights.json` -- `--use_fixed_insights` - - - -#### Ablation Study - -**DI RandomSearch** - -- Single insight -`python run_experiment.py --exp_mode aug --task titanic --aug_mode single` - -- Set insight -`python run_experiment.py --exp_mode aug --task titanic --aug_mode set` - - diff --git a/expo/data.yaml b/expo/data.yaml index d62e45309..8273fecad 100644 --- a/expo/data.yaml +++ b/expo/data.yaml @@ -1,160 +1,3 @@ datasets_dir: "D:/work/automl/datasets" # path to the datasets directory - -datasets: - titanic: - dataset: 04_titanic - metric: f1 - target_col: Survived - user_requirement: "This is a 04_titanic dataset. Your goal is to predict the target\ - \ column `Survived`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ - \ or make any visualizations.\n" - house-prices: - dataset: 05_house-prices-advanced-regression-techniques - metric: rmse - target_col: SalePrice - user_requirement: "This is a 05_house-prices-advanced-regression-techniques dataset.\ - \ Your goal is to predict the target column `SalePrice`.\nPerform data analysis,\ - \ data preprocessing, feature engineering, and modeling to predict the target.\ - \ \nReport rmse on the eval data. Do not plot or make any visualizations.\n" - santander-customer: - dataset: 06_santander-customer-transaction-prediction - metric: f1 - target_col: target - user_requirement: "This is a 06_santander-customer-transaction-prediction dataset.\ - \ Your goal is to predict the target column `target`.\nPerform data analysis,\ - \ data preprocessing, feature engineering, and modeling to predict the target.\ - \ \nReport f1 on the eval data. Do not plot or make any visualizations.\n" - icr: - dataset: 07_icr-identify-age-related-conditions - metric: f1 - target_col: Class - user_requirement: "This is a 07_icr-identify-age-related-conditions dataset. Your\ - \ goal is to predict the target column `Class`.\nPerform data analysis, data\ - \ preprocessing, feature engineering, and modeling to predict the target. \n\ - Report f1 on the eval data. Do not plot or make any visualizations.\n" - Click_prediction_small: - dataset: Click_prediction_small - metric: f1 - target_col: click - user_requirement: "This is a Click_prediction_small dataset. Your goal is to predict\ - \ the target column `click`.\nPerform data analysis, data preprocessing, feature\ - \ engineering, and modeling to predict the target. \nReport f1 on the eval data.\ - \ Do not plot or make any visualizations.\n" - GesturePhaseSegmentationProcessed: - dataset: GesturePhaseSegmentationProcessed - metric: f1 weighted - target_col: Phase - user_requirement: "This is a GesturePhaseSegmentationProcessed dataset. Your goal\ - \ is to predict the target column `Phase`.\nPerform data analysis, data preprocessing,\ - \ feature engineering, and modeling to predict the target. \nReport f1 weighted\ - \ on the eval data. Do not plot or make any visualizations.\n" - Moneyball: - dataset: Moneyball - metric: rmse - target_col: RS - user_requirement: "This is a Moneyball dataset. Your goal is to predict the target\ - \ column `RS`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ - \ plot or make any visualizations.\n" - SAT11-HAND-runtime-regression: - dataset: SAT11-HAND-runtime-regression - metric: rmse - target_col: runtime - user_requirement: "This is a SAT11-HAND-runtime-regression dataset. Your goal\ - \ is to predict the target column `runtime`.\nPerform data analysis, data preprocessing,\ - \ feature engineering, and modeling to predict the target. \nReport rmse on\ - \ the eval data. Do not plot or make any visualizations.\n" - boston: - dataset: boston - metric: rmse - target_col: MEDV - user_requirement: "This is a boston dataset. Your goal is to predict the target\ - \ column `MEDV`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ - \ plot or make any visualizations.\n" - colleges: - dataset: colleges - metric: rmse - target_col: percent_pell_grant - user_requirement: "This is a colleges dataset. Your goal is to predict the target\ - \ column `percent_pell_grant`.\nPerform data analysis, data preprocessing, feature\ - \ engineering, and modeling to predict the target. \nReport rmse on the eval\ - \ data. Do not plot or make any visualizations.\n" - credit-g: - dataset: credit-g - metric: f1 - target_col: class - user_requirement: "This is a credit-g dataset. Your goal is to predict the target\ - \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ - \ or make any visualizations.\n" - diamonds: - dataset: diamonds - metric: rmse - target_col: price - user_requirement: "This is a diamonds dataset. Your goal is to predict the target\ - \ column `price`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport rmse on the eval data. Do not\ - \ plot or make any visualizations.\n" - jasmine: - dataset: jasmine - metric: f1 - target_col: class - user_requirement: "This is a jasmine dataset. Your goal is to predict the target\ - \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ - \ or make any visualizations.\n" - kc1: - dataset: kc1 - metric: f1 - target_col: defects - user_requirement: "This is a kc1 dataset. Your goal is to predict the target column\ - \ `defects`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ - \ or make any visualizations.\n" - kick: - dataset: kick - metric: f1 - target_col: IsBadBuy - user_requirement: "This is a kick dataset. Your goal is to predict the target\ - \ column `IsBadBuy`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport f1 on the eval data. Do not plot\ - \ or make any visualizations.\n" - mfeat-factors: - dataset: mfeat-factors - metric: f1 weighted - target_col: class - user_requirement: "This is a mfeat-factors dataset. Your goal is to predict the\ - \ target column `class`.\nPerform data analysis, data preprocessing, feature\ - \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ - \ eval data. Do not plot or make any visualizations.\n" - segment: - dataset: segment - metric: f1 weighted - target_col: class - user_requirement: "This is a segment dataset. Your goal is to predict the target\ - \ column `class`.\nPerform data analysis, data preprocessing, feature engineering,\ - \ and modeling to predict the target. \nReport f1 weighted on the eval data.\ - \ Do not plot or make any visualizations.\n" - steel-plates-fault: - dataset: steel-plates-fault - metric: f1 weighted - target_col: target - user_requirement: "This is a steel-plates-fault dataset. Your goal is to predict\ - \ the target column `target`.\nPerform data analysis, data preprocessing, feature\ - \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ - \ eval data. Do not plot or make any visualizations.\n" - wine-quality-white: - dataset: wine-quality-white - metric: f1 weighted - target_col: Class - user_requirement: "This is a wine-quality-white dataset. Your goal is to predict\ - \ the target column `Class`.\nPerform data analysis, data preprocessing, feature\ - \ engineering, and modeling to predict the target. \nReport f1 weighted on the\ - \ eval data. Do not plot or make any visualizations.\n" - - work_dir: ../workspace # path to the workspace directory -role_dir: storage/team/environment/roles/ResearchAssistant_David -# analysis_pool_dir: D:/work/MG-open/MetaGPT/examples/MCTS_test/analysis_pool_sample.json \ No newline at end of file +role_dir: storage/SELA # path to the role directory diff --git a/expo/research_assistant.py b/expo/research_assistant.py index 51de188d3..8ee7dc204 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -1,5 +1,6 @@ from __future__ import annotations +import asyncio import json import os @@ -10,7 +11,7 @@ from metagpt.const import SERDESER_PATH from metagpt.roles.di.data_interpreter import DataInterpreter from metagpt.schema import Message, Task, TaskResult -from metagpt.utils.common import CodeParser, write_json_file +from metagpt.utils.common import CodeParser, role_raise_decorator, write_json_file EXTRACT_SCORE_PROMPT = """ # Code: @@ -34,6 +35,27 @@ """ +class TimeoutException(Exception): + pass + + +def async_timeout(seconds): + def decorator(func): + async def wrapper(self, *args, **kwargs): + try: + result = await asyncio.wait_for(func(self, *args, **kwargs), timeout=seconds) + except asyncio.TimeoutError: + text = f"Function timed out after {seconds} seconds" + mcts_logger.error(text) + self.save_state() + raise TimeoutException(text) + return result + + return wrapper + + return decorator + + class ResearchAssistant(DataInterpreter): node_id: str = "0" start_task_id: int = 1 @@ -117,6 +139,12 @@ async def _act_on_task(self, current_task: Task) -> TaskResult: return task_result def save_state(self, static_save=False): + """ + attribute: + state_saved - the state has been saved + input: + static_save - saving the state without changing the state_saved flag - used when a new role is created + """ if self.state_saved and not static_save: return if not static_save: @@ -135,18 +163,15 @@ def remap_tasks(self): self.planner.plan.task_map[task_id] for task_id in sorted(self.planner.plan.task_map.keys()) ] + @async_timeout(1000) + @role_raise_decorator async def run(self, with_message=None) -> Message | None: """Observe, and think and act based on the results of the observation""" if with_message == "continue": - # self.set_todo(None) - # working_memory = self.working_memory - # self.remap_tasks() mcts_logger.info("Continue to run") self.rc.working_memory.clear() self.working_memory.clear() - # self.rc.todo = WriteAnalysisCode() rsp = await self.react() - # 发送响应消息给 Environment 对象，以便它将消息传递给订阅者 self.set_todo(None) self.publish_message(rsp) return rsp diff --git a/expo/utils.py b/expo/utils.py index 56f3c21b9..b022879b0 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -1,6 +1,5 @@ import os import re -import sys from datetime import datetime from pathlib import Path @@ -21,22 +20,19 @@ def load_data_config(file_path="data.yaml"): DATASET_CONFIG = load_data_config("datasets.yaml") DATA_CONFIG = load_data_config() -DATA_CONFIG["datasets"].update(DATASET_CONFIG["datasets"]) +DATA_CONFIG["datasets"] = DATASET_CONFIG["datasets"] def get_mcts_logger(): - print_level = "INFO" - print_level2 = "MCTS" - logfile_level = "MCTS" + logfile_level = "DEBUG" name: str = None current_date = datetime.now() formatted_date = current_date.strftime("%Y%m%d") log_name = f"{name}_{formatted_date}" if name else formatted_date # name a log with prefix name - _logger.remove() - _logger.level(logfile_level, color="", no=25) - _logger.add(sys.stderr, level=print_level) - _logger.add(sys.stderr, level=print_level2) + # _logger.remove() + _logger.level("MCTS", color="", no=25) + # _logger.add(sys.stderr, level=print_level) _logger.add(Path(DATA_CONFIG["work_dir"]) / DATA_CONFIG["role_dir"] / f"{log_name}.txt", level=logfile_level) _logger.propagate = False return _logger From 2fc8f20de6fb5be7cdaf30de246f336dfc62a325 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 10 Oct 2024 16:38:19 +0800 Subject: [PATCH 104/135] can change timeout through data.yaml --- expo/data.yaml | 1 + expo/research_assistant.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/expo/data.yaml b/expo/data.yaml index 8273fecad..f1556c519 100644 --- a/expo/data.yaml +++ b/expo/data.yaml @@ -1,3 +1,4 @@ datasets_dir: "D:/work/automl/datasets" # path to the datasets directory work_dir: ../workspace # path to the workspace directory role_dir: storage/SELA # path to the role directory +role_timeout: 1000 # timeout for each node/role in seconds \ No newline at end of file diff --git a/expo/research_assistant.py b/expo/research_assistant.py index 8ee7dc204..8fadeb7fb 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -6,7 +6,7 @@ from pydantic import model_validator -from expo.utils import mcts_logger, save_notebook +from expo.utils import DATA_CONFIG, mcts_logger, save_notebook from metagpt.actions.di.write_analysis_code import WriteAnalysisCode from metagpt.const import SERDESER_PATH from metagpt.roles.di.data_interpreter import DataInterpreter @@ -163,7 +163,7 @@ def remap_tasks(self): self.planner.plan.task_map[task_id] for task_id in sorted(self.planner.plan.task_map.keys()) ] - @async_timeout(1000) + @async_timeout(DATA_CONFIG["role_timeout"]) @role_raise_decorator async def run(self, with_message=None) -> Message | None: """Observe, and think and act based on the results of the observation""" From eb460d3e1908e412dd3ceaeca518cc58412ca01a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 10 Oct 2024 18:54:40 +0800 Subject: [PATCH 105/135] make timeout as argument --- expo/MCTS.py | 5 ++++- expo/data.yaml | 3 +-- expo/experimenter/aug.py | 4 +++- expo/experimenter/experimenter.py | 5 ++++- expo/research_assistant.py | 11 ++++++----- expo/resources/MCTS-Experimenter.jpg | Bin 0 -> 659150 bytes expo/run_experiment.py | 1 + 7 files changed, 19 insertions(+), 10 deletions(-) create mode 100644 expo/resources/MCTS-Experimenter.jpg diff --git a/expo/MCTS.py b/expo/MCTS.py index 8e685cc0a..7de123572 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -26,7 +26,9 @@ def initialize_di_root_node(state, reflection: bool = True): return role, Node(parent=None, state=state, action=None, value=0) -def create_initial_state(task, start_task_id, data_config, low_is_better: bool, name: str, special_instruction: str): +def create_initial_state( + task, start_task_id, data_config, low_is_better: bool, name: str, special_instruction: str, args +): initial_state = { "task": task, "work_dir": data_config["work_dir"], @@ -40,6 +42,7 @@ def create_initial_state(task, start_task_id, data_config, low_is_better: bool, "has_run": False, "start_task_id": start_task_id, "low_is_better": low_is_better, + "role_timeout": args.role_timeout, } os.makedirs(initial_state["node_dir"], exist_ok=True) return initial_state diff --git a/expo/data.yaml b/expo/data.yaml index f1556c519..4c6549490 100644 --- a/expo/data.yaml +++ b/expo/data.yaml @@ -1,4 +1,3 @@ datasets_dir: "D:/work/automl/datasets" # path to the datasets directory work_dir: ../workspace # path to the workspace directory -role_dir: storage/SELA # path to the role directory -role_timeout: 1000 # timeout for each node/role in seconds \ No newline at end of file +role_dir: storage/SELA # path to the role directory \ No newline at end of file diff --git a/expo/experimenter/aug.py b/expo/experimenter/aug.py index 97b819802..bcfa5d4ad 100644 --- a/expo/experimenter/aug.py +++ b/expo/experimenter/aug.py @@ -34,7 +34,9 @@ async def run_experiment(self): results = [] for i in range(self.args.num_experiments): - di = ResearchAssistant(node_id=str(i), use_reflection=self.args.reflection) + di = ResearchAssistant( + node_id=str(i), use_reflection=self.args.reflection, role_timeout=self.args.role_timeout + ) di.role_dir = f"{di.role_dir}_{self.args.task}" requirement = user_requirement + EXPS_PROMPT.format(experience=exps[i]) print(requirement) diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index c6ead281b..9aa879e24 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -27,6 +27,7 @@ def __init__(self, args, **kwargs): low_is_better=self.args.low_is_better, name=self.args.name, special_instruction=self.args.special_instruction, + args=self.args, ) async def run_di(self, di, user_requirement, run_idx): @@ -82,7 +83,9 @@ async def run_experiment(self): results = [] for i in range(self.args.num_experiments): - di = ResearchAssistant(node_id="0", use_reflection=self.args.reflection) + di = ResearchAssistant( + node_id="0", use_reflection=self.args.reflection, role_timeout=self.args.role_timeout + ) score_dict = await self.run_di(di, user_requirement, run_idx=i) results.append( {"idx": i, "score_dict": score_dict, "user_requirement": user_requirement, "args": vars(self.args)} diff --git a/expo/research_assistant.py b/expo/research_assistant.py index 8fadeb7fb..c574d5b18 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -6,7 +6,7 @@ from pydantic import model_validator -from expo.utils import DATA_CONFIG, mcts_logger, save_notebook +from expo.utils import mcts_logger, save_notebook from metagpt.actions.di.write_analysis_code import WriteAnalysisCode from metagpt.const import SERDESER_PATH from metagpt.roles.di.data_interpreter import DataInterpreter @@ -39,13 +39,13 @@ class TimeoutException(Exception): pass -def async_timeout(seconds): +def async_timeout(): def decorator(func): async def wrapper(self, *args, **kwargs): try: - result = await asyncio.wait_for(func(self, *args, **kwargs), timeout=seconds) + result = await asyncio.wait_for(func(self, *args, **kwargs), timeout=self.role_timeout) except asyncio.TimeoutError: - text = f"Function timed out after {seconds} seconds" + text = f"Function timed out after {self.role_timeout} seconds" mcts_logger.error(text) self.save_state() raise TimeoutException(text) @@ -61,6 +61,7 @@ class ResearchAssistant(DataInterpreter): start_task_id: int = 1 state_saved: bool = False role_dir: str = SERDESER_PATH.joinpath("team", "environment", "roles", "Experimenter") + role_timeout: int = 1000 def get_node_name(self): return f"Node-{self.node_id}" @@ -163,7 +164,7 @@ def remap_tasks(self): self.planner.plan.task_map[task_id] for task_id in sorted(self.planner.plan.task_map.keys()) ] - @async_timeout(DATA_CONFIG["role_timeout"]) + @async_timeout() @role_raise_decorator async def run(self, with_message=None) -> Message | None: """Observe, and think and act based on the results of the observation""" diff --git a/expo/resources/MCTS-Experimenter.jpg b/expo/resources/MCTS-Experimenter.jpg new file mode 100644 index 0000000000000000000000000000000000000000..bbae98ee3ef6781b51e34278a561bb1d5b8f1d0a GIT binary patch literal 659150 zcmeEv2UOEbw{HL?C}8Lvf`ZaPsZs+XU8G5GDj*${COsfhr6ZtpK?I~kr1uU2BE9$C zA)y8c;RR3geCNFHe)qm|-+kAtH7kor7-sg~zuo`C*~r-pfJjkRK^B08d2wQK0sv>@ 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type=int, default=1000) get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) From 9f4eba7f60c6c47ebe64e3e366e05ea5de071d6f Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 10 Oct 2024 19:43:56 +0800 Subject: [PATCH 106/135] add scripts --- expo/scripts/run_cls.sh | 15 +++++++++++++++ expo/scripts/run_cls_mod.sh | 13 +++++++++++++ expo/scripts/run_reg.sh | 14 ++++++++++++++ 3 files changed, 42 insertions(+) create mode 100644 expo/scripts/run_cls.sh create mode 100644 expo/scripts/run_cls_mod.sh create mode 100644 expo/scripts/run_reg.sh diff --git a/expo/scripts/run_cls.sh b/expo/scripts/run_cls.sh new file mode 100644 index 000000000..f0ee5ddcf --- /dev/null +++ b/expo/scripts/run_cls.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +tasks=("smoker-status" "software-defects" "jasmine" "credit-g" "Click_prediction_small" "kick" "kc1" "titanic" "icr" "wine-quality-white" "mfeat-factors" "segment" "GesturePhaseSegmentationProcessed") + + +for i in {1..3} +do + for task in "${tasks[@]}"; do + echo "Running experiment for task: $task" + python run_experiment.py --exp_mode mcts --task "$task" --rollouts 10 --special_instruction stacking + echo "Experiment for task $task completed." + done +done + +echo "All experiments completed." diff --git a/expo/scripts/run_cls_mod.sh b/expo/scripts/run_cls_mod.sh new file mode 100644 index 000000000..ae3622b7a --- /dev/null +++ b/expo/scripts/run_cls_mod.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +tasks=("banking77" "gnad10" "sms_spam" "oxford-iiit-pet" "stanford_cars" "fashion_mnist" ) + +for i in {1..3} +do + for task in "${tasks[@]}"; do + echo "Running experiment for task: $task" + python run_experiment.py --exp_mode mcts --task "$task" --rollouts 10 + echo "Experiment for task $task completed." + done +done +echo "All experiments completed." diff --git a/expo/scripts/run_reg.sh b/expo/scripts/run_reg.sh new file mode 100644 index 000000000..f8a742886 --- /dev/null +++ b/expo/scripts/run_reg.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +tasks=("concrete-strength" "Moneyball" "colleges" "SAT11-HAND-runtime-regression" "diamonds" "boston" "house-prices") + +for i in {1..3} +do + for task in "${tasks[@]}"; do + echo "Running experiment for task: $task" + python run_experiment.py --exp_mode mcts --task "$task" --rollouts 10 --low_is_better --special_instruction stacking + echo "Experiment for task $task completed." + done +done + +echo "All experiments completed." From e1c2683038069395ccfafc1fd106aab9562549bf Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 11 Oct 2024 10:03:08 +0800 Subject: [PATCH 107/135] =?UTF-8?q?=E5=88=A0=E9=99=A4=E5=9B=BE=E7=89=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/README.md | 1 - expo/resources/MCTS-Experimenter.jpg | Bin 659150 -> 0 bytes 2 files changed, 1 deletion(-) delete mode 100644 expo/resources/MCTS-Experimenter.jpg diff --git a/expo/README.md b/expo/README.md index 0a807c928..598de039d 100644 --- a/expo/README.md +++ b/expo/README.md @@ -1,6 +1,5 @@ # SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning -![pipeline](resources/MCTS-Experimenter.jpg) diff --git a/expo/resources/MCTS-Experimenter.jpg b/expo/resources/MCTS-Experimenter.jpg deleted file mode 100644 index bbae98ee3ef6781b51e34278a561bb1d5b8f1d0a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 659150 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za6~L2qo?GPQt3Tg3_!tDC3o_{OUPXc4u_NuSlX(v`|ELgGx|Q^Z1JJOv9-HI&^o-;ooCMFBsjU13M(glmZF$aGG;Z?k&_dA|(fz{|P;)|UT9WkUM%<)B zopl7%q{0IkrqdyNNV4x4Fm_pUL5Z0rL}IZgL)2@sF;=zqsj*;Um;WV@x}?M5E9BC( zI^r$_aT??@`r#8e?h^rw0>wf8GH+f`A`8r_{^p|}er0D7&`div*||^t=0VwccBXf1 zvg00i+~dwR3I-cHu3*O%?6`s*SFqy>e$~uN5^+03q~Q}@W>MO|h!&KJ7tgcVzDU&P zVQ5dwxvFvlpPWc`OzaQlr_JrAG7(wThB}E}qb~5K2S#>OMpcU78{d8QuMKYm@dWK< z;nm897Jy{KhkoPA9^W>z?z~oF{#9)7;dSuDso*=^s-JI0_8wFz7jLp1+lOMv6_u$x zAaZ%EvFIxmM%=D zL6A+s2xSw#E(X@Lz4^XELXv|ah@BJIvA~W6b}X=CfgKC{AuS*dGXDN$H!QK=7Ol^a zFgqRDlB%x&=*({EQ{%U#iOPL`qbBnP8QW+9u*Nf)4g0zJFpRB(k@+yQ*TvfQwzC;5 afBZ0`Di^xNyOtmGof6QWrnG6l4*oyw6y(GJ From cdfb413f9d8e7e57e2d64efa0616ecf491460b17 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 11 Oct 2024 10:05:15 +0800 Subject: [PATCH 108/135] =?UTF-8?q?=E5=8E=BB=E6=8E=89role=E8=A3=85?= =?UTF-8?q?=E9=A5=B0=E5=99=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/research_assistant.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/expo/research_assistant.py b/expo/research_assistant.py index c574d5b18..fb34ece38 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -11,7 +11,7 @@ from metagpt.const import SERDESER_PATH from metagpt.roles.di.data_interpreter import DataInterpreter from metagpt.schema import Message, Task, TaskResult -from metagpt.utils.common import CodeParser, role_raise_decorator, write_json_file +from metagpt.utils.common import CodeParser, write_json_file EXTRACT_SCORE_PROMPT = """ # Code: @@ -165,7 +165,6 @@ def remap_tasks(self): ] @async_timeout() - @role_raise_decorator async def run(self, with_message=None) -> Message | None: """Observe, and think and act based on the results of the observation""" if with_message == "continue": From 9c54113f777990174bacd0ea789435f53982e71a Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 11 Oct 2024 10:41:33 +0800 Subject: [PATCH 109/135] make sure image task starting from datapreprocessing --- expo/experimenter/mcts.py | 5 ++++- expo/run_experiment.py | 1 + 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 22b480caf..c063268c8 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -8,9 +8,12 @@ class MCTSExperimenter(Experimenter): result_path: str = "results/mcts" - start_task_id = 2 def __init__(self, args, tree_mode=None, **kwargs): + if args.special_instruction == "image": + self.start_task_id = 1 # start from datapreprocessing if it is image task + else: + self.start_task_id = args.start_task_id super().__init__(args, **kwargs) self.tree_mode = tree_mode diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 49d058f13..15be27d60 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -31,6 +31,7 @@ def get_mcts_args(parser): parser.set_defaults(load_tree=False) parser.add_argument("--rollouts", type=int, default=5) parser.add_argument("--use_fixed_insights", dest="use_fixed_insights", action="store_true") + parser.add_argument("--start_task_id", type=int, default=2) def get_aug_exp_args(parser): From ae12d737479a142293df2a11d560404db75c99dd Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 11 Oct 2024 13:12:03 +0800 Subject: [PATCH 110/135] remove dependency --- expo/data/dataset.py | 3 ++- expo/data/hf_data.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index dd4cb4543..e076284d6 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -9,6 +9,7 @@ from sklearn.model_selection import train_test_split from expo.insights.solution_designer import SolutionDesigner +from expo.utils import DATA_CONFIG BASE_USER_REQUIREMENT = """ This is a {datasetname} dataset. Your goal is to predict the target column `{target_col}`. @@ -361,7 +362,7 @@ async def process_dataset(dataset, solution_designer: SolutionDesigner, save_ana if __name__ == "__main__": - datasets_dir = "D:/work/automl/datasets" + datasets_dir = DATA_CONFIG["datasets_dir"] force_update = False save_analysis_pool = True datasets_dict = {"datasets": {}} diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index a43fcd415..133fbdfa6 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -9,6 +9,7 @@ from expo.data.dataset import ExpDataset, process_dataset, save_datasets_dict_to_yaml from expo.insights.solution_designer import SolutionDesigner +from expo.utils import DATA_CONFIG HFDATSETS = [ {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"}, @@ -114,7 +115,7 @@ def get_dataset_info(self): if __name__ == "__main__": - dataset_dir = "D:/work/automl/datasets" + dataset_dir = DATA_CONFIG["datasets_dir"] save_analysis_pool = True force_update = False datasets_dict = {"datasets": {}} From 56e7a08a1c7304b5262da58a55546eb0244cb9c0 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 11 Oct 2024 14:48:56 +0800 Subject: [PATCH 111/135] insight pool is now able to dynamically increase --- expo/MCTS.py | 16 ++++++++++------ expo/insights/instruction_generator.py | 16 +++++++++------- expo/insights/solution_designer.py | 1 + 3 files changed, 20 insertions(+), 13 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 7de123572..9d778e4ed 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -155,18 +155,15 @@ def save_new_role(self, role: ResearchAssistant): role = role.model_copy() role.save_state(static_save=True) - async def expand(self, max_children, use_fixed_insights): + async def expand(self, max_children: int, instruction_generator: InstructionGenerator): if self.is_fully_expanded(): return - insight_geneartor = InstructionGenerator() role = self.load_role() original_instruction = role.get_next_instruction() - insights = await insight_geneartor.generate_new_instructions( + insights = await instruction_generator.generate_new_instructions( task_id=role.start_task_id + 1, original_instruction=original_instruction, max_num=max_children, - file_path=self.state["exp_pool_path"], - use_fixed_insights=use_fixed_insights, ) new_state = self.state.copy() new_state["start_task_id"] += 1 @@ -249,6 +246,8 @@ class MCTS: c_explore: float = 1.4 c_unvisited: float = 0.8 node_order: list = [] + # insight generator + instruction_generator: InstructionGenerator = None def __init__(self, root_node, max_depth, use_fixed_insights): self.root_node = root_node @@ -272,7 +271,7 @@ def uct(node: Node): return max(all_children, key=uct) async def expand(self, node: Node, max_children=5): - await node.expand(max_children, self.use_fixed_insights) + await node.expand(max_children, self.instruction_generator) if node not in self.children or not self.children[node]: self.children[node] = node.children return node.children @@ -284,6 +283,7 @@ async def simulate(self, node: Node, role=None): node = random.choice(node.children) reward = await node.run_node(role) mcts_logger.log("MCTS", f"Simulated node's reward: {reward}") + return reward def backpropagate(self, node: Node, reward): @@ -344,6 +344,10 @@ def get_score_order_dict(self): async def search(self, state, rollouts, load_tree=False, reflection=False): role, root = initialize_di_root_node(state, reflection=reflection) self.root_node = root + self.instruction_generator = InstructionGenerator( + file_path=state["exp_pool_path"], use_fixed_insights=self.use_fixed_insights + ) + tree_loaded = False if load_tree: tree_loaded = self.load_tree() diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 07e5fb655..ae6c742fb 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -2,6 +2,7 @@ import os import random +from expo.insights.solution_designer import SolutionDesigner from expo.utils import clean_json_from_rsp, load_data_config, mcts_logger from metagpt.llm import LLM from metagpt.schema import Message @@ -32,6 +33,12 @@ class InstructionGenerator: data_config = DATA_CONFIG + def __init__(self, file_path, use_fixed_insights=False): + self.file_path = file_path + self.use_fixed_insights = use_fixed_insights + self.analysis_pool = self.load_analysis_pool(file_path, use_fixed_insights) + self.proposer = SolutionDesigner() + @staticmethod def load_json_data(json_dir): with open(json_dir, "r") as file: @@ -83,13 +90,8 @@ def load_analysis_pool(file_path, use_fixed_insights, task_id=None): data = [item for item in data if int(item["task_id"]) == int(task_id)] return data - @staticmethod - async def generate_new_instructions( - task_id, original_instruction, max_num, file_path, ext_info=None, use_fixed_insights=False - ): - data = InstructionGenerator.load_analysis_pool( - file_path, task_id=task_id, use_fixed_insights=use_fixed_insights - ) + async def generate_new_instructions(self, task_id, original_instruction, max_num, ext_info=None): + data = self.analysis_pool new_instructions = [] if len(data) == 0: mcts_logger.log("MCTS", f"No insights available for task {task_id}") diff --git a/expo/insights/solution_designer.py b/expo/insights/solution_designer.py index b1fcf4188..9968131ca 100644 --- a/expo/insights/solution_designer.py +++ b/expo/insights/solution_designer.py @@ -21,6 +21,7 @@ Each task type should have at least 5 insights. Make sure each method is diverse enough and can be implemented separately. Be specific about models' choices, ensemble and tuning techniques, and preprocessing & feature engineering techniques. +Your model choices should be advanced enough to be helpful. # Format ```json From f7374c03afe8ba5c0e3278772a634310decb7cfb Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 11 Oct 2024 14:55:45 +0800 Subject: [PATCH 112/135] rename analysis pool to insight pool --- expo/insights/instruction_generator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index ae6c742fb..330795730 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -36,7 +36,7 @@ class InstructionGenerator: def __init__(self, file_path, use_fixed_insights=False): self.file_path = file_path self.use_fixed_insights = use_fixed_insights - self.analysis_pool = self.load_analysis_pool(file_path, use_fixed_insights) + self.analysis_pool = self.load_insight_pool(file_path, use_fixed_insights) self.proposer = SolutionDesigner() @staticmethod @@ -76,7 +76,7 @@ def format_output(rsp): return new_data @staticmethod - def load_analysis_pool(file_path, use_fixed_insights, task_id=None): + def load_insight_pool(file_path, use_fixed_insights, task_id=None): data = InstructionGenerator.load_json_data(file_path) if use_fixed_insights: current_directory = os.path.dirname(__file__) From eda9322361cc112fc34cb340365688123272b2a3 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Fri, 11 Oct 2024 18:37:35 +0800 Subject: [PATCH 113/135] 1. dynamically add insight 2. insight from scratch in real time --- expo/MCTS.py | 14 +++++-- expo/experimenter/mcts.py | 7 +--- expo/insights/instruction_generator.py | 49 ++++++++++++++++++++--- expo/insights/solution_designer.py | 55 ++++++++++++++++++++++++-- expo/research_assistant.py | 5 +++ expo/run_experiment.py | 3 ++ 6 files changed, 115 insertions(+), 18 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 9d778e4ed..7e1d7c88a 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -235,7 +235,8 @@ def normalize_score(score): score_dict = {k: normalize_score(v) for k, v in score_dict.items()} self.normalized_reward = score_dict - return score_dict + result_dict = role.get_solution() + return score_dict, result_dict class MCTS: @@ -281,7 +282,7 @@ async def simulate(self, node: Node, role=None): mcts_logger.log("MCTS", f"Start simulating node {node.id}:") while node.children: node = random.choice(node.children) - reward = await node.run_node(role) + reward, result_dict = await node.run_node(role) mcts_logger.log("MCTS", f"Simulated node's reward: {reward}") return reward @@ -341,12 +342,17 @@ def get_score_order_dict(self): scores["test_raw"].append(node.raw_reward["test_score"]) return scores - async def search(self, state, rollouts, load_tree=False, reflection=False): + async def search(self, state, args): + reflection = args.reflection + load_tree = args.load_tree + rollouts = args.rollouts + from_scratch = args.from_scratch role, root = initialize_di_root_node(state, reflection=reflection) self.root_node = root self.instruction_generator = InstructionGenerator( - file_path=state["exp_pool_path"], use_fixed_insights=self.use_fixed_insights + state=state, use_fixed_insights=self.use_fixed_insights, from_scratch=from_scratch ) + await self.instruction_generator.initialize() tree_loaded = False if load_tree: diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index c063268c8..d212eb204 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -24,12 +24,7 @@ async def run_experiment(self): mcts = Random(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) else: mcts = MCTS(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) - best_nodes = await mcts.search( - state=self.state, - reflection=self.args.reflection, - rollouts=self.args.rollouts, - load_tree=self.args.load_tree, - ) + best_nodes = await mcts.search(state=self.state, args=self.args) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] score_dict = best_nodes["scores"] diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 330795730..7fa4d72ea 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -1,6 +1,7 @@ import json import os import random +from difflib import SequenceMatcher from expo.insights.solution_designer import SolutionDesigner from expo.utils import clean_json_from_rsp, load_data_config, mcts_logger @@ -33,11 +34,21 @@ class InstructionGenerator: data_config = DATA_CONFIG - def __init__(self, file_path, use_fixed_insights=False): - self.file_path = file_path + def __init__(self, state, use_fixed_insights, from_scratch): + self.state = state + self.file_path = state["exp_pool_path"] + self.dataset_info_path = f"{self.data_config['datasets_dir']}/{state['task']}/dataset_info.json" + with open(self.dataset_info_path, "r") as file: + self.dataset_info = json.load(file) self.use_fixed_insights = use_fixed_insights - self.analysis_pool = self.load_insight_pool(file_path, use_fixed_insights) self.proposer = SolutionDesigner() + self.from_scratch = from_scratch + + async def initialize(self): + if self.from_scratch: + self.insight_pool = await self.generate_solutions_from_scratch(self.dataset_info, self.state["task"]) + else: + self.insight_pool = self.load_insight_pool(self.file_path, self.use_fixed_insights) @staticmethod def load_json_data(json_dir): @@ -84,14 +95,14 @@ def load_insight_pool(file_path, use_fixed_insights, task_id=None): data.extend(fixed_insights) for item in data: if "task_id" not in item: - raise ValueError("task_id is not found in the analysis pool") + raise ValueError("task_id is not found in the insight_pool") if task_id: data = [item for item in data if int(item["task_id"]) == int(task_id)] return data async def generate_new_instructions(self, task_id, original_instruction, max_num, ext_info=None): - data = self.analysis_pool + data = self.insight_pool new_instructions = [] if len(data) == 0: mcts_logger.log("MCTS", f"No insights available for task {task_id}") @@ -108,6 +119,34 @@ async def generate_new_instructions(self, task_id, original_instruction, max_num new_instructions.append(new_instruction) return new_instructions + async def propose_new_insights(self, solution, score): + new_insights = await self.proposer.propose_insights(solution, score) + added_insights = self.add_insight(new_insights) + return added_insights + + async def generate_solutions_from_scratch(self, dataset_info, dataset_name): + insight_pool = await self.proposer.generate_solutions(dataset_info, dataset_name, save_analysis_pool=False) + return insight_pool + + def add_insight(self, new_insights): + added_insights = [] + for new_insight in new_insights: + if not self.is_similar_to_existing(new_insight): + added_insights.append(new_insight) + self.insight_pool.append(new_insight) + return added_insights + + def is_similar_to_existing(self, new_insight, similarity_threshold=0.8): + for existing_insight in self.insight_pool: + similarity = self.calculate_similarity(new_insight["Analysis"], existing_insight["Analysis"]) + if similarity > similarity_threshold: + return True + return False + + @staticmethod + def calculate_similarity(text1, text2): + return SequenceMatcher(None, text1, text2).ratio() + @staticmethod async def generate_new_instruction(original_instruction, insights, ext_info): prompt = CHANGE_INSTRUCTION.format(instruction=original_instruction, insights=insights) diff --git a/expo/insights/solution_designer.py b/expo/insights/solution_designer.py index 9968131ca..2336911db 100644 --- a/expo/insights/solution_designer.py +++ b/expo/insights/solution_designer.py @@ -70,6 +70,45 @@ ``` """ + +INSIGHT_PROPOSAL_PROMPT = """ +You are an AI assistant tasked with analyzing a machine learning solution and proposing new insights to improve its performance. Given the current solution code and development score, suggest innovative approaches to enhance the model. + +Current Solution Code: +{solution_code} + +Development Score: {dev_score} + +Based on this information, propose 3-5 new insights across different aspects of the machine learning pipeline (Data Preprocessing, Feature Engineering, and Model Training). Your insights should be specific, actionable, and have the potential to improve the model's performance. + +Please format your response as a JSON array with the following structure: +[ + + {{ + "task_type": "Data Preprocessing", + "insights": [ + "insight1", + "insight2" + ] + }}, + {{ + "task_type": "Feature Engineering", + "insights": [ + "insight1", + "insight2" + ] + }}, + {{ + "task_type": "Model Training", + "insights": [ + "insight1", + "insight2" + ] + }} +] +""" + + KEY_DATASET_FEATURES = [ "NumberOfClasses", "NumberOfFeatures", @@ -86,7 +125,7 @@ class SolutionDesigner: data_dir: str = DATA_CONFIG["datasets_dir"] - async def generate_solutions(self, dataset_info, dataset_name): + async def generate_solutions(self, dataset_info, dataset_name, save_analysis_pool=True): llm = LLM() context = DATASET_INSIGHT_PROMPT.format( dataset=dataset_info["description"], @@ -96,8 +135,18 @@ async def generate_solutions(self, dataset_info, dataset_name): rsp = await llm.aask(context) rsp = clean_json_from_rsp(rsp) analysis_pool = self.process_analysis_pool(json.loads(rsp)) - dataset_path = f"{self.data_dir}/{dataset_name}" - self.save_analysis_pool(dataset_path, analysis_pool) + if save_analysis_pool: + dataset_path = f"{self.data_dir}/{dataset_name}" + self.save_analysis_pool(dataset_path, analysis_pool) + return analysis_pool + + async def propose_new_insights(self, solution, score): + llm = LLM() + context = INSIGHT_PROPOSAL_PROMPT.format(solution_code=solution, dev_score=score) + rsp = await llm.aask(context) + rsp = clean_json_from_rsp(rsp) + new_insights = self.process_analysis_pool(json.loads(rsp)) + return new_insights def process_analysis_pool(self, insights_rsp): analysis_pool = [] diff --git a/expo/research_assistant.py b/expo/research_assistant.py index fb34ece38..0b53521a3 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -139,6 +139,11 @@ async def _act_on_task(self, current_task: Task) -> TaskResult: save_notebook(role=self, save_dir=self.role_dir, name=self.get_node_name()) return task_result + def get_solution(self): + codes = [task.code for task in self.planner.plan.tasks] + results = [task.result for task in self.planner.plan.tasks] + return {"codes": codes, "results": results} + def save_state(self, static_save=False): """ attribute: diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 15be27d60..c43da12fd 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -32,6 +32,9 @@ def get_mcts_args(parser): parser.add_argument("--rollouts", type=int, default=5) parser.add_argument("--use_fixed_insights", dest="use_fixed_insights", action="store_true") parser.add_argument("--start_task_id", type=int, default=2) + parser.add_argument( + "--from_scratch", dest="from_scratch", action="store_true", help="Generate solutions from scratch" + ) def get_aug_exp_args(parser): From 3a57060e25a8acfd2ed0f80b4d68a5a110425159 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Sat, 12 Oct 2024 17:16:51 +0800 Subject: [PATCH 114/135] 1. add eval_func for sela and compatibility to others 2. llm extract score (use all code block and execution results) 3. add argument for custom dataset dir 4. dataset custom requirement support --- expo/MCTS.py | 65 ++++++++++++++------- expo/data/custom_task.py | 38 ++++++++++++ expo/data/dataset.py | 2 +- expo/evaluation/evaluation.py | 12 ++++ expo/experimenter/experimenter.py | 5 +- expo/experimenter/mcts.py | 21 ++++++- expo/experimenter/mle_bench/instructions.py | 47 +++++++++++++++ expo/insights/instruction_generator.py | 14 +++-- expo/research_assistant.py | 28 ++++++--- expo/run_experiment.py | 6 ++ expo/utils.py | 2 + 11 files changed, 202 insertions(+), 38 deletions(-) create mode 100644 expo/data/custom_task.py create mode 100644 expo/experimenter/mle_bench/instructions.py diff --git a/expo/MCTS.py b/expo/MCTS.py index 7e1d7c88a..a8410748e 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -3,10 +3,12 @@ import os import pickle import random +import shutil import numpy as np import pandas as pd +from expo.data.custom_task import get_mle_bench_requirements from expo.data.dataset import generate_task_requirement, get_split_dataset_path from expo.evaluation.evaluation import evaluate_score from expo.insights.instruction_generator import InstructionGenerator @@ -17,9 +19,6 @@ def initialize_di_root_node(state, reflection: bool = True): - # state = create_initial_state( - # task, start_task_id=start_task_id, data_config=data_config, low_is_better=low_is_better, name=name - # ) role = ResearchAssistant( node_id="0", start_task_id=state["start_task_id"], use_reflection=reflection, role_dir=state["node_dir"] ) @@ -29,20 +28,33 @@ def initialize_di_root_node(state, reflection: bool = True): def create_initial_state( task, start_task_id, data_config, low_is_better: bool, name: str, special_instruction: str, args ): + external_eval = args.external_eval + + if args.custom_dataset_dir: + dataset_config = None + datasets_dir = args.custom_dataset_dir + requirement = get_mle_bench_requirements(args.custom_dataset_dir, data_config) + exp_pool_path = None + else: + dataset_config = data_config["datasets"][task] + datasets_dir = get_split_dataset_path(task, data_config) + requirement = generate_task_requirement(task, data_config, is_di=True, special_instruction=special_instruction) + exp_pool_path = get_exp_pool_path(task, data_config, pool_name="ds_analysis_pool") + initial_state = { "task": task, "work_dir": data_config["work_dir"], "node_dir": os.path.join(data_config["work_dir"], data_config["role_dir"], f"{task}{name}"), - "dataset_config": data_config["datasets"][task], - "datasets_dir": get_split_dataset_path(task, data_config), - "exp_pool_path": get_exp_pool_path(task, data_config, pool_name="ds_analysis_pool"), - "requirement": generate_task_requirement( - task, data_config, is_di=True, special_instruction=special_instruction - ), + "dataset_config": dataset_config, + "datasets_dir": datasets_dir, # won't be used if external eval is used + "exp_pool_path": exp_pool_path, + "requirement": requirement, "has_run": False, "start_task_id": start_task_id, "low_is_better": low_is_better, "role_timeout": args.role_timeout, + "external_eval": external_eval, + "custom_dataset_dir": args.custom_dataset_dir, } os.makedirs(initial_state["node_dir"], exist_ok=True) return initial_state @@ -173,22 +185,34 @@ async def expand(self, max_children: int, instruction_generator: InstructionGene node.save_new_role(new_role) self.add_child(node) - def evaluate_prediction(self, split): - pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}_predictions.csv") - pred_node_path = os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}_predictions.csv") + def get_predictions_path(self, split): + return os.path.join(self.state["node_dir"], f"Node-{self.id}-{split}_predictions.csv") + + def get_and_move_predictions(self, split): + if not os.path.exists(self.get_predictions_path(split)): + pred_path = os.path.join(self.state["work_dir"], self.state["task"], f"{split}_predictions.csv") + shutil.copy(pred_path, self.get_predictions_path(split)) + os.remove(pred_path) + return pd.read_csv(self.get_predictions_path(split)) + + def get_gt(self, split): gt_path = os.path.join(self.state["datasets_dir"][f"{split}_target"]) - preds = pd.read_csv(pred_path)["target"] - preds.to_csv(pred_node_path, index=False) - gt = pd.read_csv(gt_path)["target"] + return pd.read_csv(gt_path) + + def evaluate_prediction(self, split): + preds = self.get_and_move_predictions(split)["target"] + gt = self.get_gt(split)["target"] metric = self.state["dataset_config"]["metric"] - # remove original predictions.csv - os.remove(pred_path) return evaluate_score(preds, gt, metric) def evaluate_simulation(self, score_dict): - scores = {"dev_score": self.evaluate_prediction("dev"), "test_score": self.evaluate_prediction("test")} - scores["score"] = scores["dev_score"] - score_dict.update(scores) + if self.state["external_eval"]: # use external evaluation + scores = {"dev_score": self.evaluate_prediction("dev"), "test_score": self.evaluate_prediction("test")} + scores["score"] = scores["dev_score"] + score_dict.update(scores) + else: + self.get_and_move_predictions("dev") + self.get_and_move_predictions("test") return score_dict async def run_node(self, role=None): @@ -215,7 +239,6 @@ async def run_node(self, role=None): mcts_logger.log("MCTS", f"Role-level timeout: {e}") break except Exception as e: - print(f"Error: {e}") mcts_logger.log("MCTS", f"Error in running the role: {e}") num_runs += 1 diff --git a/expo/data/custom_task.py b/expo/data/custom_task.py new file mode 100644 index 000000000..2bd88abde --- /dev/null +++ b/expo/data/custom_task.py @@ -0,0 +1,38 @@ +import os + +from expo.experimenter.mle_bench.instructions import ( + ADDITIONAL_NOTES, + INSTRUCTIONS, + INSTRUCTIONS_OBFUSCATED, +) + +MLE_BENCH_FILES = ["description.md", "description_obfuscated.md"] + + +MLE_REQUIREMENTS = """ +{instructions} + +{additonal_notes} + +COMPETITION INSTRUCTIONS +------ + +{task_description} + +""" + + +def get_mle_bench_requirements(dataset_dir, data_config, obfuscated=False): + if obfuscated: + instructions = INSTRUCTIONS_OBFUSCATED + task_file = "description_obfuscated.md" + else: + instructions = INSTRUCTIONS + task_file = "description.md" + + with open(os.path.join(dataset_dir, task_file)) as f: + task_description = f.read() + mle_requirement = MLE_REQUIREMENTS.format( + instructions=instructions, additonal_notes=ADDITIONAL_NOTES, task_description=task_description + ) + return mle_requirement diff --git a/expo/data/dataset.py b/expo/data/dataset.py index e076284d6..8b0c5b980 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -268,7 +268,7 @@ def get_metric(self): dataset_info = self.get_dataset_info() num_classes = dataset_info["metadata"]["NumberOfClasses"] if num_classes == 2: - metric = "f1" + metric = "f1 binary" elif 2 < num_classes <= 200: metric = "f1 weighted" elif num_classes > 200 or num_classes == 0: diff --git a/expo/evaluation/evaluation.py b/expo/evaluation/evaluation.py index 16b3acb71..1ba7fa60f 100644 --- a/expo/evaluation/evaluation.py +++ b/expo/evaluation/evaluation.py @@ -22,3 +22,15 @@ def evaluate_score(pred, gt, metric): return mean_squared_error(np.log1p(gt), np.log1p(pred), squared=False) else: raise ValueError(f"Metric {metric} not supported") + + +def node_evaluate_score_sela(node): + preds = node.get_and_move_predictions("test")["target"] + gt = node.get_gt("test")["target"] + metric = node.state["dataset_config"]["metric"] + return evaluate_score(preds, gt, metric) + + +def node_evaluate_score_mlebench(node): + # TODO + return 0 diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 9aa879e24..417adabad 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -43,7 +43,10 @@ async def run_di(self, di, user_requirement, run_idx): except Exception as e: print(f"Error: {e}") num_runs += 1 - save_notebook(role=di, save_dir=self.result_path, name=f"{self.args.task}_{self.start_time}_{run_idx}") + # save_notebook(role=di, save_dir=self.result_path, name=f"{self.args.task}_{self.start_time}_{run_idx}") + save_name = self.get_save_name() + save_notebook(role=di, save_dir=self.result_path, name=f"{save_name}_{run_idx}") + if not run_finished: score_dict = {"train_score": -1, "dev_score": -1, "test_score": -1, "score": -1} return score_dict diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index d212eb204..37fc7a071 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -1,5 +1,9 @@ import shutil +from expo.evaluation.evaluation import ( + node_evaluate_score_mlebench, + node_evaluate_score_sela, +) from expo.evaluation.visualize_mcts import get_tree_text from expo.experimenter.experimenter import Experimenter from expo.Greedy import Greedy, Random @@ -14,25 +18,35 @@ def __init__(self, args, tree_mode=None, **kwargs): self.start_task_id = 1 # start from datapreprocessing if it is image task else: self.start_task_id = args.start_task_id + + if args.eval_func == "sela": + self.eval_func = node_evaluate_score_sela + elif args.eval_func == "mlebench": + self.eval_func = node_evaluate_score_mlebench + super().__init__(args, **kwargs) self.tree_mode = tree_mode async def run_experiment(self): + use_fixed_insights = self.args.use_fixed_insights + depth = 5 if self.tree_mode == "greedy": - mcts = Greedy(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) + mcts = Greedy(root_node=None, max_depth=depth, use_fixed_insights=use_fixed_insights) elif self.tree_mode == "random": - mcts = Random(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) + mcts = Random(root_node=None, max_depth=depth, use_fixed_insights=use_fixed_insights) else: - mcts = MCTS(root_node=None, max_depth=5, use_fixed_insights=self.args.use_fixed_insights) + mcts = MCTS(root_node=None, max_depth=depth, use_fixed_insights=use_fixed_insights) best_nodes = await mcts.search(state=self.state, args=self.args) best_node = best_nodes["global_best"] dev_best_node = best_nodes["dev_best"] score_dict = best_nodes["scores"] + additional_scores = {"grader": self.eval_func(dev_best_node)} text, num_generated_codes = get_tree_text(mcts.root_node) text += f"Generated {num_generated_codes} unique codes.\n" text += f"Best node: {best_node.id}, score: {best_node.raw_reward}\n" text += f"Dev best node: {dev_best_node.id}, score: {dev_best_node.raw_reward}\n" + text += f"Grader score: {additional_scores['grader']}\n" print(text) results = [ { @@ -45,6 +59,7 @@ async def run_experiment(self): "tree_text": text, "args": vars(self.args), "scores": score_dict, + "additional_scores": additional_scores, } ] self.save_result(results) diff --git a/expo/experimenter/mle_bench/instructions.py b/expo/experimenter/mle_bench/instructions.py new file mode 100644 index 000000000..301b465c1 --- /dev/null +++ b/expo/experimenter/mle_bench/instructions.py @@ -0,0 +1,47 @@ +INSTRUCTIONS = """ +You are participating in *MLE-bench*, an offline version of Kaggle competitions adapted to test the coding ability of agents like yourself. + +You will be given a machine learning task. You must solve the task by training a model and running the model on the test set to produce a submission file. You may not hand-label the data to produce a submission file. + +BENCHMARK INSTRUCTIONS +------ +First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `/home/instructions.txt`. + +- **Competition-specific instructions**: The instructions for the competition are available in `/home/data/description.md`. Read this file to understand the problem setup for the competition you are currently solving. +- **Dataset**: The dataset files for the competition are available in `/home/data/`. +- **Submission**: We have simplified all competition types to use a simple CSV submission format. Regardless of what the competition instructions say, you MUST produce a submission file at `/home/submission/submission.csv`. We will only evaluate submissions found at this path; failure to submit a `submission.csv` at this path will result in a failed attempt. +- **Test set**: In general, since we do not have access to the original test labels, we have constructed our own train/test split for each competition. As such, the dataset you see in `/home/data/` may differ slightly from what is described in the competition instructions. Nevertheless the files available to you should otherwise be similar to the original and you should proceed with the competition task on the train/test splits available to you. +- **Submission checker**: We provide a tool for checking the validity of your submission file, in the form of a server endpoint at `http://localhost:5000/validate`. If you post a submission file to the server (e.g. `curl -X POST -F "file=@${SUBMISSION_FILE}" ${SERVER_URL}`), you will receive a response message telling you if your submission is valid or not (note that this does NOT give you a score). For your convenience, we provide a script `/home/validate_submission.sh` which can help you make this submission to the validation server. +- **Cheating**: You are not allowed to view others' solutions to this competition. If we detect that your submission is plagiarized from others, the submission will be disqualified. + +Your submission will be graded based on the metric in the competition-specific instructions. Your goal is to achieve the best score possible on the competition task. + +Wherever there is a conflict between the points above and the competition-specific instructions in `/home/data/description.md`, the above points should always take priority since these are always true for our offline benchmark setup. +""" + +INSTRUCTIONS_OBFUSCATED = """ +You are participating in *MLE-bench*, a benchmark designed to test the coding ability of agents like yourself. + +You will be given a machine learning task. You must solve the task by training a model and running the model on the test set to produce a submission file. You may not hand-label the data to produce a submission file. + +BENCHMARK INSTRUCTIONS +------ +First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `/home/instructions.txt`. + +- **Task-specific instructions**: The instructions for the task are available in `/home/data/description.md`. Read this file to understand the problem setup for the task you are currently solving. +- **Dataset**: The dataset files for the task are available in `/home/data/`. +- **Submission**: Regardless of what the task instructions say, you MUST produce a submission file at `/home/submission/submission.csv`. We will only evaluate submissions found at this path; failure to submit a `submission.csv` at this path will result in a failed attempt. +- **Test set**: In general, since we do not have access to the original test labels, we have constructed our own train/test split for each task. As such, the dataset you see in `/home/data/` may differ slightly from what is described in the task instructions. Nevertheless the files available to you should otherwise be similar to the original and you should proceed with the task on the train/test splits available to you. +- **Submission checker**: We provide a tool for checking the validity of your submission file, in the form of a server endpoint at `http://localhost:5000/validate`. If you post a submission file to the server (e.g. `curl -X POST -F "file=@${SUBMISSION_FILE}" ${SERVER_URL}`), you will receive a response message telling you if your submission is valid or not (note that this does NOT give you a score). For your convenience, we provide a script `/home/validate_submission.sh` which can help you make this submission to the validation server. +- **Cheating**: You are not allowed to view others' solutions to this task. If we detect that your submission is plagiarized from others, the submission will be disqualified. + +Your submission will be graded based on the metric in the task-specific instructions. Your goal is to achieve the best score possible on the task. + +Wherever there is a conflict between the points above and the task-specific instructions in `/home/data/description.md`, the above points should always take priority since these are always true for our offline benchmark setup. +""" + +ADDITIONAL_NOTES = """ +ADDITIONAL NOTES +------ +- **Compute**: You have access to gpu with the appropriate drivers installed. +""" diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 7fa4d72ea..7fe5ceece 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -37,12 +37,18 @@ class InstructionGenerator: def __init__(self, state, use_fixed_insights, from_scratch): self.state = state self.file_path = state["exp_pool_path"] - self.dataset_info_path = f"{self.data_config['datasets_dir']}/{state['task']}/dataset_info.json" - with open(self.dataset_info_path, "r") as file: - self.dataset_info = json.load(file) + if state["custom_dataset_dir"]: + self.dataset_info = "xxx" + else: + dataset_info_path = f"{self.data_config['datasets_dir']}/{state['task']}/dataset_info.json" + with open(dataset_info_path, "r") as file: + self.dataset_info = json.load(file) self.use_fixed_insights = use_fixed_insights self.proposer = SolutionDesigner() - self.from_scratch = from_scratch + if self.file_path is None: + self.from_scratch = True + else: + self.from_scratch = from_scratch async def initialize(self): if self.from_scratch: diff --git a/expo/research_assistant.py b/expo/research_assistant.py index 0b53521a3..d068dd4e5 100644 --- a/expo/research_assistant.py +++ b/expo/research_assistant.py @@ -13,15 +13,19 @@ from metagpt.schema import Message, Task, TaskResult from metagpt.utils.common import CodeParser, write_json_file -EXTRACT_SCORE_PROMPT = """ -# Code: +CODE_BLOCK_RESULT = """ +## Code: {code} -# Execution Result: +## Execution Result: {result} +""" +EXTRACT_SCORE_PROMPT = """ +# Code Blocks +{code_block} # Instruction: -Based on the code and execution result, please extract the scores and return it as a dictionary. +Based on the code and execution result, please extract the **final scores** and return it as a dictionary. If you cannot find the scores, please still return a dictionary with the keys 'train_score', 'dev_score', and 'test_score', and set the values to -1. # Format: @@ -109,9 +113,17 @@ async def get_score(self): return score_dict async def llm_extract_score(self): - result_text = self.planner.plan.task_map[str(len(self.planner.plan.task_map))].result - code_text = self.planner.plan.task_map[str(len(self.planner.plan.task_map))].code - rsp = await self.llm.aask(EXTRACT_SCORE_PROMPT.format(code=code_text, result=result_text, role="user")) + # result_text = self.planner.plan.task_map[str(len(self.planner.plan.task_map))].result + # code_text = self.planner.plan.task_map[str(len(self.planner.plan.task_map))].code + num_tasks = len(self.planner.plan.task_map) + task_map = self.planner.plan.task_map + code_block = "\n".join( + [ + CODE_BLOCK_RESULT.format(code=task_map[str(i + 1)].code, result=task_map[str(i + 1)].result) + for i in range(num_tasks) + ] + ) + rsp = await self.llm.aask(EXTRACT_SCORE_PROMPT.format(code_block=code_block, role="user")) json_block = CodeParser.parse_code(block=None, text=rsp) score_dict = json.loads(json_block) return score_dict @@ -161,7 +173,7 @@ def save_state(self, static_save=False): stg_path = self.role_dir name = self.get_node_name() role_path = os.path.join(stg_path, f"{name}.json") - # 将状态保存为 JSON 文件 + # save state as json file write_json_file(role_path, self.model_dump()) def remap_tasks(self): diff --git a/expo/run_experiment.py b/expo/run_experiment.py index c43da12fd..53fcdd18c 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -31,10 +31,16 @@ def get_mcts_args(parser): parser.set_defaults(load_tree=False) parser.add_argument("--rollouts", type=int, default=5) parser.add_argument("--use_fixed_insights", dest="use_fixed_insights", action="store_true") + parser.set_defaults(use_fixed_insights=False) parser.add_argument("--start_task_id", type=int, default=2) parser.add_argument( "--from_scratch", dest="from_scratch", action="store_true", help="Generate solutions from scratch" ) + parser.set_defaults(from_scratch=False) + parser.add_argument("--no_external_eval", dest="external_eval", action="store_false") + parser.set_defaults(external_eval=True) + parser.add_argument("--eval_func", type=str, default="sela", choices=["sela", "mlebench"]) + parser.add_argument("--custom_dataset_dir", type=str, default=None) def get_aug_exp_args(parser): diff --git a/expo/utils.py b/expo/utils.py index b022879b0..f3381c91c 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -51,6 +51,8 @@ def get_exp_pool_path(task_name, data_config, pool_name="analysis_pool"): f"Dataset {task_name} not found in config file. Available datasets: {data_config['datasets'].keys()}" ) exp_pool_path = os.path.join(data_path, f"{pool_name}.json") + if not os.path.exists(exp_pool_path): + return None return exp_pool_path From a91003a7fe2235609d99e74d6d4a93402fb61fc4 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 14 Oct 2024 09:56:55 +0800 Subject: [PATCH 115/135] disable submission --- expo/data/custom_task.py | 22 +++++++++++++--- expo/experimenter/mle_bench/instructions.py | 29 +++++++++++---------- expo/insights/instruction_generator.py | 3 ++- expo/insights/solution_designer.py | 26 +++++++++++++----- 4 files changed, 55 insertions(+), 25 deletions(-) diff --git a/expo/data/custom_task.py b/expo/data/custom_task.py index 2bd88abde..14eb6aac2 100644 --- a/expo/data/custom_task.py +++ b/expo/data/custom_task.py @@ -19,20 +19,34 @@ {task_description} +## More Instructions +- output_dir: {output_dir} +- Besides `submission.csv`, you should also save your output in the output directory. +- Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. + +Do not make visualizations. """ def get_mle_bench_requirements(dataset_dir, data_config, obfuscated=False): + work_dir = data_config["work_dir"] + output_dir = f"{work_dir}/output" + os.makedirs(output_dir, exist_ok=True) + if obfuscated: - instructions = INSTRUCTIONS_OBFUSCATED + instructions = INSTRUCTIONS_OBFUSCATED.format(dataset_dir=dataset_dir, output_dir=output_dir) task_file = "description_obfuscated.md" else: - instructions = INSTRUCTIONS + instructions = INSTRUCTIONS.format(dataset_dir=dataset_dir, output_dir=output_dir) task_file = "description.md" - with open(os.path.join(dataset_dir, task_file)) as f: + with open(os.path.join(dataset_dir, task_file), encoding="utf-8") as f: task_description = f.read() mle_requirement = MLE_REQUIREMENTS.format( - instructions=instructions, additonal_notes=ADDITIONAL_NOTES, task_description=task_description + instructions=instructions, + additonal_notes=ADDITIONAL_NOTES, + task_description=task_description, + output_dir=output_dir, ) + print(mle_requirement) return mle_requirement diff --git a/expo/experimenter/mle_bench/instructions.py b/expo/experimenter/mle_bench/instructions.py index 301b465c1..631f410dc 100644 --- a/expo/experimenter/mle_bench/instructions.py +++ b/expo/experimenter/mle_bench/instructions.py @@ -1,3 +1,6 @@ +SUBMISSION_INSTURCTION = """ +- **Submission checker**: We provide a tool for checking the validity of your submission file, in the form of a server endpoint at `http://localhost:5000/validate`. If you post a submission file to the server (e.g. `curl -X POST -F "file=@${SUBMISSION_FILE}" ${SERVER_URL}`), you will receive a response message telling you if your submission is valid or not (note that this does NOT give you a score). For your convenience, we provide a script `/home/validate_submission.sh` which can help you make this submission to the validation server. +""" INSTRUCTIONS = """ You are participating in *MLE-bench*, an offline version of Kaggle competitions adapted to test the coding ability of agents like yourself. @@ -5,18 +8,17 @@ BENCHMARK INSTRUCTIONS ------ -First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `/home/instructions.txt`. +First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `expo.experimenter.mle_bench.instructions.py`. -- **Competition-specific instructions**: The instructions for the competition are available in `/home/data/description.md`. Read this file to understand the problem setup for the competition you are currently solving. -- **Dataset**: The dataset files for the competition are available in `/home/data/`. -- **Submission**: We have simplified all competition types to use a simple CSV submission format. Regardless of what the competition instructions say, you MUST produce a submission file at `/home/submission/submission.csv`. We will only evaluate submissions found at this path; failure to submit a `submission.csv` at this path will result in a failed attempt. -- **Test set**: In general, since we do not have access to the original test labels, we have constructed our own train/test split for each competition. As such, the dataset you see in `/home/data/` may differ slightly from what is described in the competition instructions. Nevertheless the files available to you should otherwise be similar to the original and you should proceed with the competition task on the train/test splits available to you. -- **Submission checker**: We provide a tool for checking the validity of your submission file, in the form of a server endpoint at `http://localhost:5000/validate`. If you post a submission file to the server (e.g. `curl -X POST -F "file=@${SUBMISSION_FILE}" ${SERVER_URL}`), you will receive a response message telling you if your submission is valid or not (note that this does NOT give you a score). For your convenience, we provide a script `/home/validate_submission.sh` which can help you make this submission to the validation server. +- **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. +- **Dataset**: The dataset files for the competition are available in `{dataset_dir}`. +- **Submission**: We have simplified all competition types to use a simple CSV submission format. Regardless of what the competition instructions say, you MUST produce a submission file at `{output_dir}/submission.csv`. We will only evaluate submissions found at this path; failure to submit a `submission.csv` at this path will result in a failed attempt. +- **Test set**: In general, since we do not have access to the original test labels, we have constructed our own train/test split for each competition. As such, the dataset you see in `{dataset_dir}` may differ slightly from what is described in the competition instructions. Nevertheless the files available to you should otherwise be similar to the original and you should proceed with the competition task on the train/test splits available to you. - **Cheating**: You are not allowed to view others' solutions to this competition. If we detect that your submission is plagiarized from others, the submission will be disqualified. Your submission will be graded based on the metric in the competition-specific instructions. Your goal is to achieve the best score possible on the competition task. -Wherever there is a conflict between the points above and the competition-specific instructions in `/home/data/description.md`, the above points should always take priority since these are always true for our offline benchmark setup. +Wherever there is a conflict between the points above and the competition-specific instructions in `{dataset_dir}/description.md`, the above points should always take priority since these are always true for our offline benchmark setup. """ INSTRUCTIONS_OBFUSCATED = """ @@ -26,18 +28,17 @@ BENCHMARK INSTRUCTIONS ------ -First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `/home/instructions.txt`. +First, please read the instructions below which are specific to our benchmark setup. Note, these instructions can be found again in `expo.experimenter.mle_bench.instructions.py`. -- **Task-specific instructions**: The instructions for the task are available in `/home/data/description.md`. Read this file to understand the problem setup for the task you are currently solving. -- **Dataset**: The dataset files for the task are available in `/home/data/`. -- **Submission**: Regardless of what the task instructions say, you MUST produce a submission file at `/home/submission/submission.csv`. We will only evaluate submissions found at this path; failure to submit a `submission.csv` at this path will result in a failed attempt. -- **Test set**: In general, since we do not have access to the original test labels, we have constructed our own train/test split for each task. As such, the dataset you see in `/home/data/` may differ slightly from what is described in the task instructions. Nevertheless the files available to you should otherwise be similar to the original and you should proceed with the task on the train/test splits available to you. -- **Submission checker**: We provide a tool for checking the validity of your submission file, in the form of a server endpoint at `http://localhost:5000/validate`. If you post a submission file to the server (e.g. `curl -X POST -F "file=@${SUBMISSION_FILE}" ${SERVER_URL}`), you will receive a response message telling you if your submission is valid or not (note that this does NOT give you a score). For your convenience, we provide a script `/home/validate_submission.sh` which can help you make this submission to the validation server. +- **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. +- **Dataset**: The dataset files for the task are available in `{dataset_dir}/`. +- **Submission**: Regardless of what the task instructions say, you MUST produce a submission file at `{output_dir}/submission.csv`. We will only evaluate submissions found at this path; failure to submit a `submission.csv` at this path will result in a failed attempt. +- **Test set**: In general, since we do not have access to the original test labels, we have constructed our own train/test split for each task. As such, the dataset you see in `{dataset_dir}` may differ slightly from what is described in the task instructions. Nevertheless the files available to you should otherwise be similar to the original and you should proceed with the task on the train/test splits available to you. - **Cheating**: You are not allowed to view others' solutions to this task. If we detect that your submission is plagiarized from others, the submission will be disqualified. Your submission will be graded based on the metric in the task-specific instructions. Your goal is to achieve the best score possible on the task. -Wherever there is a conflict between the points above and the task-specific instructions in `/home/data/description.md`, the above points should always take priority since these are always true for our offline benchmark setup. +Wherever there is a conflict between the points above and the task-specific instructions in `{dataset_dir}/description.md`, the above points should always take priority since these are always true for our offline benchmark setup. """ ADDITIONAL_NOTES = """ diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 7fe5ceece..835c1ff9d 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -38,7 +38,8 @@ def __init__(self, state, use_fixed_insights, from_scratch): self.state = state self.file_path = state["exp_pool_path"] if state["custom_dataset_dir"]: - self.dataset_info = "xxx" + with open(f"{state['custom_dataset_dir']}/description.md", "r", encoding="utf-8") as file: + self.dataset_info = file.read() else: dataset_info_path = f"{self.data_config['datasets_dir']}/{state['task']}/dataset_info.json" with open(dataset_info_path, "r") as file: diff --git a/expo/insights/solution_designer.py b/expo/insights/solution_designer.py index 2336911db..262caa0f6 100644 --- a/expo/insights/solution_designer.py +++ b/expo/insights/solution_designer.py @@ -5,7 +5,8 @@ DATA_CONFIG = load_data_config() -DATASET_INSIGHT_PROMPT = """ + +DATASET_DESCRIPTION_SELA_PROMPT = """ # Dataset Description {dataset} @@ -14,6 +15,15 @@ # Dataset Head {head} +""" + +DATASET_DESCRIPTION_CUSTOM_PROMPT = """ +# Dataset Description +{dataset_description} +""" + +DATASET_INSIGHT_PROMPT = """ +{description} # Instruction Propose insights to help improve the performance of the model on this dataset. @@ -127,11 +137,15 @@ class SolutionDesigner: async def generate_solutions(self, dataset_info, dataset_name, save_analysis_pool=True): llm = LLM() - context = DATASET_INSIGHT_PROMPT.format( - dataset=dataset_info["description"], - metadata=self.metadata_builder(dataset_info["metadata"]), - head=dataset_info["df_head"], - ) + if type(dataset_info) == dict: + description_prompt = DATASET_DESCRIPTION_SELA_PROMPT.format( + dataset=dataset_info["description"], + metadata=self.metadata_builder(dataset_info["metadata"]), + head=dataset_info["df_head"], + ) + else: + description_prompt = DATASET_DESCRIPTION_CUSTOM_PROMPT.format(dataset_description=dataset_info) + context = DATASET_INSIGHT_PROMPT.format(description=description_prompt) rsp = await llm.aask(context) rsp = clean_json_from_rsp(rsp) analysis_pool = self.process_analysis_pool(json.loads(rsp)) From 1d4a84512039a3d2a29714f640bac08302d004cf Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Mon, 14 Oct 2024 16:12:26 +0800 Subject: [PATCH 116/135] =?UTF-8?q?=E6=94=AF=E6=8C=81=E8=B7=91=E9=80=9Amle?= =?UTF-8?q?=20bench?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- expo/MCTS.py | 6 ++++-- expo/data/custom_task.py | 17 ++++++++++++----- expo/evaluation/evaluation.py | 14 +++++++++++++- expo/run_experiment.py | 5 +++++ 4 files changed, 34 insertions(+), 8 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index a8410748e..749850dd6 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -8,7 +8,7 @@ import numpy as np import pandas as pd -from expo.data.custom_task import get_mle_bench_requirements +from expo.data.custom_task import get_mle_bench_requirements, get_mle_task_id from expo.data.dataset import generate_task_requirement, get_split_dataset_path from expo.evaluation.evaluation import evaluate_score from expo.insights.instruction_generator import InstructionGenerator @@ -35,6 +35,8 @@ def create_initial_state( datasets_dir = args.custom_dataset_dir requirement = get_mle_bench_requirements(args.custom_dataset_dir, data_config) exp_pool_path = None + # external_eval = False # make sure external eval is false if custom dataset is used + task = get_mle_task_id(args.custom_dataset_dir) else: dataset_config = data_config["datasets"][task] datasets_dir = get_split_dataset_path(task, data_config) @@ -120,7 +122,7 @@ def generate_id(self): return f"{self.parent.id}-{num_sibling}" def is_terminal(self): - return int(self.state["start_task_id"]) == self.max_depth + 1 + return int(self.state["start_task_id"]) == self.max_depth + 1 # TODO: Check if this is correct or +1 def is_fully_expanded(self): return len(self.children) > 0 diff --git a/expo/data/custom_task.py b/expo/data/custom_task.py index 14eb6aac2..f66b4aa58 100644 --- a/expo/data/custom_task.py +++ b/expo/data/custom_task.py @@ -22,19 +22,26 @@ ## More Instructions - output_dir: {output_dir} - Besides `submission.csv`, you should also save your output in the output directory. -- Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. - -Do not make visualizations. +- You should split the training data into train and dev set. +- Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. They should be in the same format as the `submission.csv`. +- Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. +**Do not make any plots or visualizations.** """ +def get_mle_task_id(dataset_dir): + return dataset_dir.split("/")[-3] + + def get_mle_bench_requirements(dataset_dir, data_config, obfuscated=False): work_dir = data_config["work_dir"] - output_dir = f"{work_dir}/output" + task = get_mle_task_id(dataset_dir) + output_dir = f"{work_dir}/{task}" + final_output_dir = f"{work_dir}/submission" os.makedirs(output_dir, exist_ok=True) if obfuscated: - instructions = INSTRUCTIONS_OBFUSCATED.format(dataset_dir=dataset_dir, output_dir=output_dir) + instructions = INSTRUCTIONS_OBFUSCATED.format(dataset_dir=dataset_dir, output_dir=final_output_dir) task_file = "description_obfuscated.md" else: instructions = INSTRUCTIONS.format(dataset_dir=dataset_dir, output_dir=output_dir) diff --git a/expo/evaluation/evaluation.py b/expo/evaluation/evaluation.py index 1ba7fa60f..2c19b81fc 100644 --- a/expo/evaluation/evaluation.py +++ b/expo/evaluation/evaluation.py @@ -1,3 +1,5 @@ +from pathlib import Path + import numpy as np from sklearn.metrics import accuracy_score, f1_score, mean_squared_error, roc_auc_score @@ -33,4 +35,14 @@ def node_evaluate_score_sela(node): def node_evaluate_score_mlebench(node): # TODO - return 0 + from mlebench.grade import grade_csv + from mlebench.registry import registry + + competition_id = node.state["task"] + pred_path = node.get_predictions_path("test") + new_registry = registry.set_data_dir(Path(registry.get_data_dir())) + competition = new_registry.get_competition(competition_id) + submission = Path(pred_path) + report = grade_csv(submission, competition).to_dict() + report["submission_path"] = str(submission) + return report diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 53fcdd18c..bf90cb07a 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -60,6 +60,11 @@ def get_di_args(parser): async def main(args): + if args.custom_dataset_dir: + args.external_eval = False + args.eval_func = "mlebench" + args.from_scratch = True + if args.exp_mode == "mcts": experimenter = MCTSExperimenter(args) elif args.exp_mode == "greedy": From 07800be4417c265c0b2ebe76f30bf1b2a7dd20c1 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 15 Oct 2024 14:13:25 +0800 Subject: [PATCH 117/135] allow datasets to be prepared by users --- expo/data/dataset.py | 31 ++++++++++++++++++++++--------- expo/data/hf_data.py | 12 +++++++++--- 2 files changed, 31 insertions(+), 12 deletions(-) diff --git a/expo/data/dataset.py b/expo/data/dataset.py index 8b0c5b980..91490dcd7 100644 --- a/expo/data/dataset.py +++ b/expo/data/dataset.py @@ -1,3 +1,4 @@ +import argparse import asyncio import json import os @@ -18,22 +19,22 @@ """ USE_AG = """ -7. Please use autogluon for model training with presets='medium_quality', time_limit=None, give dev dataset to tuning_data, and use right eval_metric. +- Please use autogluon for model training with presets='medium_quality', time_limit=None, give dev dataset to tuning_data, and use right eval_metric. """ TEXT_MODALITY = """ -7. You could use models from transformers library for this text dataset. -8. Use gpu if available for faster training. +- You could use models from transformers library for this text dataset. +- Use gpu if available for faster training. """ IMAGE_MODALITY = """ -7. You could use models from transformers/torchvision library for this image dataset. -8. Use gpu if available for faster training. +- You could use models from transformers/torchvision library for this image dataset. +- Use gpu if available for faster training. """ STACKING = """ -7. To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor. -8. You could do some quick model prototyping to see which models work best and then use them in the ensemble. +- To avoid overfitting, train a weighted ensemble model such as StackingClassifier or StackingRegressor. +- You could do some quick model prototyping to see which models work best and then use them in the ensemble. """ @@ -361,10 +362,22 @@ async def process_dataset(dataset, solution_designer: SolutionDesigner, save_ana datasets_dict["datasets"][dataset.name] = dataset_dict +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("--force_update", action="store_true", help="Force update datasets") + parser.add_argument("--save_analysis_pool", action="store_true", help="Save analysis pool") + parser.add_argument( + "--no_save_analysis_pool", dest="save_analysis_pool", action="store_false", help="Do not save analysis pool" + ) + parser.set_defaults(save_analysis_pool=True) + return parser.parse_args() + + if __name__ == "__main__": datasets_dir = DATA_CONFIG["datasets_dir"] - force_update = False - save_analysis_pool = True + args = parse_args() + force_update = args.force_update + save_analysis_pool = args.save_analysis_pool datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() for dataset_id in OPENML_DATASET_IDS: diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py index 133fbdfa6..a18517d49 100644 --- a/expo/data/hf_data.py +++ b/expo/data/hf_data.py @@ -7,7 +7,12 @@ from datasets import load_dataset from PIL import Image -from expo.data.dataset import ExpDataset, process_dataset, save_datasets_dict_to_yaml +from expo.data.dataset import ( + ExpDataset, + parse_args, + process_dataset, + save_datasets_dict_to_yaml, +) from expo.insights.solution_designer import SolutionDesigner from expo.utils import DATA_CONFIG @@ -116,8 +121,9 @@ def get_dataset_info(self): if __name__ == "__main__": dataset_dir = DATA_CONFIG["datasets_dir"] - save_analysis_pool = True - force_update = False + args = parse_args() + force_update = args.force_update + save_analysis_pool = args.save_analysis_pool datasets_dict = {"datasets": {}} solution_designer = SolutionDesigner() for dataset_meta in HFDATSETS: From d1799829493b66351054a5dcd0051507b50c394c Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 15 Oct 2024 14:14:29 +0800 Subject: [PATCH 118/135] allow special-instruction for mle-bench --- expo/MCTS.py | 6 ++-- expo/README.md | 60 +++++++++++++++++++++++----------------- expo/data/custom_task.py | 7 +++-- expo/utils.py | 2 +- 4 files changed, 45 insertions(+), 30 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 749850dd6..378474b4e 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -33,7 +33,9 @@ def create_initial_state( if args.custom_dataset_dir: dataset_config = None datasets_dir = args.custom_dataset_dir - requirement = get_mle_bench_requirements(args.custom_dataset_dir, data_config) + requirement = get_mle_bench_requirements( + args.custom_dataset_dir, data_config, special_instruction=special_instruction + ) exp_pool_path = None # external_eval = False # make sure external eval is false if custom dataset is used task = get_mle_task_id(args.custom_dataset_dir) @@ -309,7 +311,7 @@ async def simulate(self, node: Node, role=None): node = random.choice(node.children) reward, result_dict = await node.run_node(role) mcts_logger.log("MCTS", f"Simulated node's reward: {reward}") - + # TODO: add new insights return reward def backpropagate(self, node: Node, reward): diff --git a/expo/README.md b/expo/README.md index 598de039d..5b913e415 100644 --- a/expo/README.md +++ b/expo/README.md @@ -6,7 +6,12 @@ ## 1. Data Preparation - Download Datasets：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink - +- Download and prepare datasets from scratch: + ``` + cd expo/data + python dataset.py --save_analysis_pool + python hf_data.py --save_analysis_pool + ``` ## 2. Configs @@ -85,6 +90,23 @@ Each baseline needs to produce `dev_predictions.csv`和`test_predictions.csv`. E - Use the function `evaluate_score` to evaluate. +#### MLE-Bench +**Note: mle-bench requires python 3.11 or higher** +``` +git clone https://github.com/openai/mle-bench.git +cd mle-bench +pip install -e . +``` + +``` +mlebench prepare -c --data-dir +``` + +Enter the following command to run the experiment: +``` +python run_experiment.py --exp_mode mcts --custom_dataset_dir --rollouts 10 --from_scratch +``` + ## 5. Baselines ### DS Agent @@ -92,7 +114,7 @@ Each baseline needs to produce `dev_predictions.csv`和`test_predictions.csv`. E git clone https://github.com/guosyjlu/DS-Agent.git ``` -将其deployment/generate.py line46-48行部分修改如下（目的是用deepseek而非GPT的API）： +Modify the following lines in deployment/generate.py (lines 46-48) as shown below (the purpose is to use deepseek instead of OpenAI's API): ```python messages = [{"role": "user", "content": prompt}] @@ -120,7 +142,7 @@ elif llm == 'deepseek-coder': completion = raw_completion.split("```python")[1].split("```")[0] ``` -修改完后在新建一个`deployment/test.sh` 分别运行下列两行，`$TASK` 是你要测试的task name +After making the changes, create a new `deployment/test.sh` and run the following two lines separately, where `$TASK` is the name of the task you want to test ``` python -u generate.py --llm deepseek-coder --task $TASK --shot 1 --retrieval > "$TASK".txt 2>&1 @@ -135,7 +157,7 @@ python -u evaluation.py --path "deepseek-coder_True_1" --task $TASK --device 0 git clone https://github.com/WecoAI/aideml.git ``` -修改 `aideml/aide/utils/config.yaml` 内容如下 +Modify `aideml/aide/utils/config.yaml`: ```yaml # path to the task data directory @@ -192,14 +214,14 @@ agent: num_drafts: 5 ``` -由于 deepseek 完全兼容 OpenAI 的 API，修改`base_url`为`自己的url`，`api_key`为`自己的key`即可 +Since Deepseek is compatible to OpenAI's API, change `base_url` into `your own url`，`api_key` into `your api key` ``` -export OPENAI_API_KEY="自己的key" -export OPENAI_BASE_URL="自己的url" +export OPENAI_API_KEY="your api key" +export OPENAI_BASE_URL="your own url" ``` -修改`aideml/aide/backend/__init__.py` 30 行内容如下： +Modify `aideml/aide/backend/__init__.py`'s line 30 and below: ```python model_kwargs = model_kwargs | { @@ -213,7 +235,7 @@ model_kwargs = model_kwargs | { query_func = backend_openai.query ``` -由于 deepseekV2.5 不再支持 system message 使用 function call，修改 `aideml/aide/agent.py` 312 行内容如下： +Since deepseekV2.5 no longer supports system message using function call, modify `aideml/aide/agent.py`'s line 312: ```python response = cast( @@ -228,7 +250,7 @@ response = cast( ) ``` -修改完后 +Modify and install: ``` cd aideml @@ -237,8 +259,8 @@ pip install -e . #### Run -运行下面脚本获取运行结果，在当前目录下将生成一个 log 文件夹以及 workspace 文件夹 -log 文件夹中将包含实验使用配置以及生成方案记录，workspace 文件夹下将保存 aide 最后生成的结果文件 +Run the following script to get the running results, a `log` folder and a `workspace` folder will be generated in the current directory +The `log` folder will contain the experimental configuration and the generated scheme, and the `workspace` folder will save the final results generated by aide ``` python experimenter/aide.py @@ -264,7 +286,6 @@ python run_expriment.py --exp_mode autogluon --task {task_name} --is_multimodal Replace {task_name} with the specific task you want to run. -提供github链接，并说明使用的命令以及参数设置 ### AutoSklearn #### System requirements auto-sklearn has the following system requirements: @@ -295,15 +316,4 @@ python run_experiment.py --exp_mode autosklearn --task titanic For setup, check 4. - `python run_experiment.py --exp_mode base --task titanic --num_experiments 10` - Specifically instruct DI to use AutoGluon: `--special_instruction ag` -- Specifically instruct DI to use the stacking ensemble method: `--special_instruction stacking` - - - - - - - - - - - +- Specifically instruct DI to use the stacking ensemble method: `--special_instruction stacking` \ No newline at end of file diff --git a/expo/data/custom_task.py b/expo/data/custom_task.py index f66b4aa58..e904e9496 100644 --- a/expo/data/custom_task.py +++ b/expo/data/custom_task.py @@ -1,5 +1,6 @@ import os +from expo.data.dataset import SPECIAL_INSTRUCTIONS from expo.experimenter.mle_bench.instructions import ( ADDITIONAL_NOTES, INSTRUCTIONS, @@ -24,7 +25,7 @@ - Besides `submission.csv`, you should also save your output in the output directory. - You should split the training data into train and dev set. - Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. They should be in the same format as the `submission.csv`. -- Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. +- Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. {special_instruction} **Do not make any plots or visualizations.** """ @@ -33,12 +34,13 @@ def get_mle_task_id(dataset_dir): return dataset_dir.split("/")[-3] -def get_mle_bench_requirements(dataset_dir, data_config, obfuscated=False): +def get_mle_bench_requirements(dataset_dir, data_config, obfuscated=False, special_instruction=""): work_dir = data_config["work_dir"] task = get_mle_task_id(dataset_dir) output_dir = f"{work_dir}/{task}" final_output_dir = f"{work_dir}/submission" os.makedirs(output_dir, exist_ok=True) + special_instruction = SPECIAL_INSTRUCTIONS[special_instruction] if obfuscated: instructions = INSTRUCTIONS_OBFUSCATED.format(dataset_dir=dataset_dir, output_dir=final_output_dir) @@ -54,6 +56,7 @@ def get_mle_bench_requirements(dataset_dir, data_config, obfuscated=False): additonal_notes=ADDITIONAL_NOTES, task_description=task_description, output_dir=output_dir, + special_instruction=special_instruction, ) print(mle_requirement) return mle_requirement diff --git a/expo/utils.py b/expo/utils.py index f3381c91c..21b311e7f 100644 --- a/expo/utils.py +++ b/expo/utils.py @@ -111,7 +111,7 @@ async def load_execute_notebook(role): codes = [task.code for task in tasks if task.code] executor = role.execute_code executor.nb = nbformat.v4.new_notebook() - executor.nb_client = NotebookClient(executor.nb, timeout=executor.timeout) + executor.nb_client = NotebookClient(executor.nb, timeout=role.role_timeout) # await executor.build() for code in codes: outputs, success = await executor.run(code) From 0166834ce47396efb8827859eb2e788ab0dd5f6f Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 15 Oct 2024 16:19:02 +0800 Subject: [PATCH 119/135] fix special instruction bug --- expo/data/custom_task.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/expo/data/custom_task.py b/expo/data/custom_task.py index e904e9496..fe366d7ea 100644 --- a/expo/data/custom_task.py +++ b/expo/data/custom_task.py @@ -34,14 +34,16 @@ def get_mle_task_id(dataset_dir): return dataset_dir.split("/")[-3] -def get_mle_bench_requirements(dataset_dir, data_config, obfuscated=False, special_instruction=""): +def get_mle_bench_requirements(dataset_dir, data_config, special_instruction, obfuscated=False): work_dir = data_config["work_dir"] task = get_mle_task_id(dataset_dir) output_dir = f"{work_dir}/{task}" final_output_dir = f"{work_dir}/submission" os.makedirs(output_dir, exist_ok=True) - special_instruction = SPECIAL_INSTRUCTIONS[special_instruction] - + if special_instruction: + special_instruction = SPECIAL_INSTRUCTIONS[special_instruction] + else: + special_instruction = "" if obfuscated: instructions = INSTRUCTIONS_OBFUSCATED.format(dataset_dir=dataset_dir, output_dir=final_output_dir) task_file = "description_obfuscated.md" From 02b4f0aa13a238a8a053f4354048d76dbe99516b Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 15 Oct 2024 16:41:28 +0800 Subject: [PATCH 120/135] add timout to mlebench readme instruction --- expo/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/README.md b/expo/README.md index 5b913e415..6704582b8 100644 --- a/expo/README.md +++ b/expo/README.md @@ -104,7 +104,7 @@ mlebench prepare -c --data-dir Enter the following command to run the experiment: ``` -python run_experiment.py --exp_mode mcts --custom_dataset_dir --rollouts 10 --from_scratch +python run_experiment.py --exp_mode mcts --custom_dataset_dir --rollouts 10 --from_scratch --role_timeout 3600 ``` From 541f8a1b100bce3c88e9a346e090ce86ab01f91f Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Tue, 15 Oct 2024 19:18:30 +0800 Subject: [PATCH 121/135] fix path bug --- expo/evaluation/evaluation.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/expo/evaluation/evaluation.py b/expo/evaluation/evaluation.py index 2c19b81fc..1e58e1725 100644 --- a/expo/evaluation/evaluation.py +++ b/expo/evaluation/evaluation.py @@ -39,8 +39,9 @@ def node_evaluate_score_mlebench(node): from mlebench.registry import registry competition_id = node.state["task"] + data_dir = Path(node.state["custom_dataset_dir"]).parent.parent.parent # prepared/public/../../../ pred_path = node.get_predictions_path("test") - new_registry = registry.set_data_dir(Path(registry.get_data_dir())) + new_registry = registry.set_data_dir(data_dir) competition = new_registry.get_competition(competition_id) submission = Path(pred_path) report = grade_csv(submission, competition).to_dict() From 7794b99005ba84a8819c1564a7ad8e3d1e27c5b0 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 16 Oct 2024 09:50:24 +0800 Subject: [PATCH 122/135] fix: role timeout not passing in --- expo/MCTS.py | 6 +++++- expo/run_experiment.py | 7 +++++-- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 378474b4e..cfb21a61c 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -20,7 +20,11 @@ def initialize_di_root_node(state, reflection: bool = True): role = ResearchAssistant( - node_id="0", start_task_id=state["start_task_id"], use_reflection=reflection, role_dir=state["node_dir"] + node_id="0", + start_task_id=state["start_task_id"], + use_reflection=reflection, + role_dir=state["node_dir"], + role_timeout=state["role_timeout"], ) return role, Node(parent=None, state=state, action=None, value=0) diff --git a/expo/run_experiment.py b/expo/run_experiment.py index bf90cb07a..71529b955 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -9,7 +9,7 @@ from expo.experimenter.mcts import MCTSExperimenter -def get_args(): +def get_args(cmd=True): parser = argparse.ArgumentParser() parser.add_argument("--name", type=str, default="") parser.add_argument( @@ -22,7 +22,10 @@ def get_args(): get_di_args(parser) get_mcts_args(parser) get_aug_exp_args(parser) - return parser.parse_args() + if cmd: + return parser.parse_args() + else: + return parser.parse_args("") def get_mcts_args(parser): From 989a3b4299c4f2520b7fc6893529398b4826b98f Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 16 Oct 2024 10:53:37 +0800 Subject: [PATCH 123/135] allow max depth passing in --- expo/MCTS.py | 2 +- expo/experimenter/mcts.py | 2 +- expo/run_experiment.py | 1 + 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index cfb21a61c..1eb8a131c 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -274,7 +274,7 @@ class MCTS: # data_path root_node: Node = None children: dict = {} - max_depth: int = 5 + max_depth: int = None c_explore: float = 1.4 c_unvisited: float = 0.8 node_order: list = [] diff --git a/expo/experimenter/mcts.py b/expo/experimenter/mcts.py index 37fc7a071..a42566366 100644 --- a/expo/experimenter/mcts.py +++ b/expo/experimenter/mcts.py @@ -29,7 +29,7 @@ def __init__(self, args, tree_mode=None, **kwargs): async def run_experiment(self): use_fixed_insights = self.args.use_fixed_insights - depth = 5 + depth = self.args.max_depth if self.tree_mode == "greedy": mcts = Greedy(root_node=None, max_depth=depth, use_fixed_insights=use_fixed_insights) elif self.tree_mode == "random": diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 71529b955..4e6b41fd7 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -44,6 +44,7 @@ def get_mcts_args(parser): parser.set_defaults(external_eval=True) parser.add_argument("--eval_func", type=str, default="sela", choices=["sela", "mlebench"]) parser.add_argument("--custom_dataset_dir", type=str, default=None) + parser.add_argument("--max_depth", type=int, default=4) def get_aug_exp_args(parser): From df7a04dd1993f3fa7266afb2719d2995b080ef51 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Wed, 16 Oct 2024 14:28:27 +0800 Subject: [PATCH 124/135] output dev set score --- expo/data/custom_task.py | 1 + 1 file changed, 1 insertion(+) diff --git a/expo/data/custom_task.py b/expo/data/custom_task.py index fe366d7ea..c4dd0012c 100644 --- a/expo/data/custom_task.py +++ b/expo/data/custom_task.py @@ -24,6 +24,7 @@ - output_dir: {output_dir} - Besides `submission.csv`, you should also save your output in the output directory. - You should split the training data into train and dev set. +- You should use the dev set to improve your model. Print the final dev set score after training. - Save the prediction results of BOTH the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory. They should be in the same format as the `submission.csv`. - Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. {special_instruction} **Do not make any plots or visualizations.** From 38daf24c33f9e89dea3fa53e772924cd0daae16c Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 09:55:37 +0800 Subject: [PATCH 125/135] rename task if custom_data_dir is used --- expo/run_experiment.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 4e6b41fd7..c977b4dc9 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -1,6 +1,7 @@ import argparse import asyncio +from expo.data.custom_task import get_mle_task_id from expo.experimenter.aug import AugExperimenter from expo.experimenter.autogluon import GluonExperimenter from expo.experimenter.autosklearn import AutoSklearnExperimenter @@ -68,6 +69,7 @@ async def main(args): args.external_eval = False args.eval_func = "mlebench" args.from_scratch = True + args.task = get_mle_task_id(args.custom_dataset_dir) if args.exp_mode == "mcts": experimenter = MCTSExperimenter(args) From a46f5753612c9e5e5e6a948f309873f994b16174 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 10:11:31 +0800 Subject: [PATCH 126/135] clean up input argument --- expo/MCTS.py | 14 +++++++------- expo/experimenter/custom.py | 4 +--- expo/experimenter/experimenter.py | 3 --- expo/run_experiment.py | 17 +++++++++-------- 4 files changed, 17 insertions(+), 21 deletions(-) diff --git a/expo/MCTS.py b/expo/MCTS.py index 1eb8a131c..8778554ed 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -29,16 +29,14 @@ def initialize_di_root_node(state, reflection: bool = True): return role, Node(parent=None, state=state, action=None, value=0) -def create_initial_state( - task, start_task_id, data_config, low_is_better: bool, name: str, special_instruction: str, args -): +def create_initial_state(task, start_task_id, data_config, args): external_eval = args.external_eval if args.custom_dataset_dir: dataset_config = None datasets_dir = args.custom_dataset_dir requirement = get_mle_bench_requirements( - args.custom_dataset_dir, data_config, special_instruction=special_instruction + args.custom_dataset_dir, data_config, special_instruction=args.special_instruction ) exp_pool_path = None # external_eval = False # make sure external eval is false if custom dataset is used @@ -46,20 +44,22 @@ def create_initial_state( else: dataset_config = data_config["datasets"][task] datasets_dir = get_split_dataset_path(task, data_config) - requirement = generate_task_requirement(task, data_config, is_di=True, special_instruction=special_instruction) + requirement = generate_task_requirement( + task, data_config, is_di=True, special_instruction=args.special_instruction + ) exp_pool_path = get_exp_pool_path(task, data_config, pool_name="ds_analysis_pool") initial_state = { "task": task, "work_dir": data_config["work_dir"], - "node_dir": os.path.join(data_config["work_dir"], data_config["role_dir"], f"{task}{name}"), + "node_dir": os.path.join(data_config["work_dir"], data_config["role_dir"], f"{task}{args.name}"), "dataset_config": dataset_config, "datasets_dir": datasets_dir, # won't be used if external eval is used "exp_pool_path": exp_pool_path, "requirement": requirement, "has_run": False, "start_task_id": start_task_id, - "low_is_better": low_is_better, + "low_is_better": args.low_is_better, "role_timeout": args.role_timeout, "external_eval": external_eval, "custom_dataset_dir": args.custom_dataset_dir, diff --git a/expo/experimenter/custom.py b/expo/experimenter/custom.py index 92b7dafa2..f245499ca 100644 --- a/expo/experimenter/custom.py +++ b/expo/experimenter/custom.py @@ -21,9 +21,7 @@ def __init__(self, args, **kwargs): self.task, start_task_id=1, data_config=self.data_config, - low_is_better=self.low_is_better, - name=self.name, - special_instruction=self.args.special_instruction, + args=self.args, ) def run_experiment(self): diff --git a/expo/experimenter/experimenter.py b/expo/experimenter/experimenter.py index 417adabad..4a0b8413e 100644 --- a/expo/experimenter/experimenter.py +++ b/expo/experimenter/experimenter.py @@ -24,9 +24,6 @@ def __init__(self, args, **kwargs): self.args.task, start_task_id=self.start_task_id, data_config=self.data_config, - low_is_better=self.args.low_is_better, - name=self.args.name, - special_instruction=self.args.special_instruction, args=self.args, ) diff --git a/expo/run_experiment.py b/expo/run_experiment.py index c977b4dc9..be891814d 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -24,9 +24,16 @@ def get_args(cmd=True): get_mcts_args(parser) get_aug_exp_args(parser) if cmd: - return parser.parse_args() + args = parser.parse_args() else: - return parser.parse_args("") + args = parser.parse_args("") + + if args.custom_dataset_dir: + args.external_eval = False + args.eval_func = "mlebench" + args.from_scratch = True + args.task = get_mle_task_id(args.custom_dataset_dir) + return args def get_mcts_args(parser): @@ -65,12 +72,6 @@ def get_di_args(parser): async def main(args): - if args.custom_dataset_dir: - args.external_eval = False - args.eval_func = "mlebench" - args.from_scratch = True - args.task = get_mle_task_id(args.custom_dataset_dir) - if args.exp_mode == "mcts": experimenter = MCTSExperimenter(args) elif args.exp_mode == "greedy": From 0f01c07b836fd756ce66e6a882fab8c0a1a27fff Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 10:27:33 +0800 Subject: [PATCH 127/135] add tree visualization script and function --- expo/evaluation/visualize_mcts.py | 108 ++++++++++++++++++++++++++- expo/scripts/visualize_experiment.py | 23 ++++++ 2 files changed, 128 insertions(+), 3 deletions(-) create mode 100644 expo/scripts/visualize_experiment.py diff --git a/expo/evaluation/visualize_mcts.py b/expo/evaluation/visualize_mcts.py index d310036c0..e429789fd 100644 --- a/expo/evaluation/visualize_mcts.py +++ b/expo/evaluation/visualize_mcts.py @@ -1,5 +1,8 @@ import textwrap +import matplotlib.pyplot as plt +import networkx as nx + from expo.MCTS import Node NODE_TEMPLATE = """\ @@ -11,6 +14,9 @@ """ +NODE_SIZE = 12000 +NODE_FONT_SIZE = 18 + def get_role_plans(role): plans = role.planner.plan.tasks @@ -42,7 +48,7 @@ def visualize_node(node: Node, previous_plans=None): id=node_id, plans=instruct_plans_text, simulated=simulated, score=score, num_visits=num_visits ) - def visualize_tree(node, depth=0, previous_plans=None): + def visualize_tree_text(node, depth=0, previous_plans=None): text = "" if node is not None: text += visualize_node(node, previous_plans) @@ -50,10 +56,106 @@ def visualize_tree(node, depth=0, previous_plans=None): code_set.update({task.instruction for task in role.planner.plan.tasks}) previous_plans = get_role_plans(role) for child in node.children: - text += textwrap.indent(visualize_tree(child, depth + 1, previous_plans), "\t") + text += textwrap.indent(visualize_tree_text(child, depth + 1, previous_plans), "\t") return text num_simulations = node.visited text = f"Number of simulations: {num_simulations}\n" - text += visualize_tree(node) + text += visualize_tree_text(node) return text, len(code_set) + + +def get_node_color(node): + if node["visits"] == 0: + return "#D3D3D3" + else: + # The higher the avg_value, the more intense the color + # avg_value is between 0 and 1 + avg_value = node["avg_value"] + # Convert avg_value to a color ranging from red (low) to green (high) + red = int(255 * (1 - avg_value)) + green = int(255 * avg_value) + return f"#{red:02X}{green:02X}00" + + +def visualize_tree(graph, save_path=""): + # Use a hierarchical layout for tree-like visualization + pos = nx.spring_layout(graph, k=0.9, iterations=50) + + plt.figure(figsize=(30, 20)) # Further increase figure size for better visibility + + # Calculate node levels + root = "0" + levels = nx.single_source_shortest_path_length(graph, root) + max_level = max(levels.values()) + + # Adjust y-coordinates based on levels and x-coordinates to prevent overlap + nodes_by_level = {} + for node, level in levels.items(): + if level not in nodes_by_level: + nodes_by_level[level] = [] + nodes_by_level[level].append(node) + + for level, nodes in nodes_by_level.items(): + y = 1 - level / max_level + x_step = 1.0 / (len(nodes) + 1) + for i, node in enumerate(sorted(nodes)): + pos[node] = ((i + 1) * x_step, y) + + # Draw edges + nx.draw_networkx_edges(graph, pos, edge_color="gray", arrows=True, arrowsize=40, width=3) + + # Draw nodes + node_colors = [get_node_color(graph.nodes[node]) for node in graph.nodes] + nx.draw_networkx_nodes(graph, pos, node_size=NODE_SIZE, node_color=node_colors) + + # Add labels to nodes + labels = nx.get_node_attributes(graph, "label") + nx.draw_networkx_labels(graph, pos, labels, font_size=NODE_FONT_SIZE) + + # Add instructions to the right side of nodes + instructions = nx.get_node_attributes(graph, "instruction") + for node, (x, y) in pos.items(): + wrapped_text = textwrap.fill(instructions[node], width=30) # Adjust width as needed + plt.text(x + 0.05, y, wrapped_text, fontsize=15, ha="left", va="center") + + plt.title("MCTS Tree Visualization", fontsize=40) + plt.axis("off") # Turn off axis + plt.tight_layout() + if save_path: + plt.savefig(save_path) + plt.show() + + +def build_tree_recursive(graph, parent_id, node, start_task_id=2): + """ + Recursively builds the entire tree starting from the root node. + Adds nodes and edges to the NetworkX graph. + """ + role = node.load_role() + depth = node.get_depth() + if depth == 0: + instruction = "\n\n".join([role.planner.plan.tasks[i].instruction for i in range(start_task_id)]) + else: + instruction = role.planner.plan.tasks[depth + start_task_id - 1].instruction + print(instruction) + # Add the current node with attributes to the graph + dev_score = node.raw_reward.get("dev_score", 0) * 100 + avg_score = node.avg_value() * 100 + graph.add_node( + parent_id, + label=f"{node.id}\nAvg: {avg_score:.1f}\nScore: {dev_score:.1f}\nVisits: {node.visited}", + avg_value=node.avg_value(), + dev_score=dev_score, + visits=node.visited, + instruction=instruction, + ) + # Stopping condition: if the node has no children, return + if not node.children: + return + + # Recursively create all child nodes + for i, child in enumerate(node.children): + child_id = f"{parent_id}-{i}" + graph.add_edge(parent_id, child_id) + build_tree_recursive(graph, child_id, child) diff --git a/expo/scripts/visualize_experiment.py b/expo/scripts/visualize_experiment.py new file mode 100644 index 000000000..c06b1eeab --- /dev/null +++ b/expo/scripts/visualize_experiment.py @@ -0,0 +1,23 @@ +import networkx as nx + +from expo.evaluation.visualize_mcts import build_tree_recursive, visualize_tree +from expo.MCTS import MCTS, create_initial_state, initialize_di_root_node +from expo.run_experiment import get_args +from expo.utils import DATA_CONFIG + +if __name__ == "__main__": + args = get_args() + data_config = DATA_CONFIG + state = create_initial_state(args.task, 0, data_config, args=args) + role, node = initialize_di_root_node(state) + mcts = MCTS( + root_node=node, + max_depth=5, + use_fixed_insights=False, + ) + + mcts.load_tree() + root = mcts.root_node + G = nx.DiGraph() + tree = build_tree_recursive(G, "0", root) + visualize_tree(tree, save_path="../results/tree.png") From 1d22466ac53f848dfed275c6dfb08425efd130b3 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 10:29:46 +0800 Subject: [PATCH 128/135] change dir for tree fig --- expo/scripts/visualize_experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/scripts/visualize_experiment.py b/expo/scripts/visualize_experiment.py index c06b1eeab..940d1f11b 100644 --- a/expo/scripts/visualize_experiment.py +++ b/expo/scripts/visualize_experiment.py @@ -20,4 +20,4 @@ root = mcts.root_node G = nx.DiGraph() tree = build_tree_recursive(G, "0", root) - visualize_tree(tree, save_path="../results/tree.png") + visualize_tree(tree, save_path="results/tree.png") From 6646983a255dc81057be91faa47d7bb6d98bae93 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 10:34:05 +0800 Subject: [PATCH 129/135] fix visualization bug --- expo/scripts/visualize_experiment.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/expo/scripts/visualize_experiment.py b/expo/scripts/visualize_experiment.py index 940d1f11b..42b4490ec 100644 --- a/expo/scripts/visualize_experiment.py +++ b/expo/scripts/visualize_experiment.py @@ -19,5 +19,5 @@ mcts.load_tree() root = mcts.root_node G = nx.DiGraph() - tree = build_tree_recursive(G, "0", root) - visualize_tree(tree, save_path="results/tree.png") + build_tree_recursive(G, "0", root) + visualize_tree(G, save_path="results/tree.png") From 510136ab17e32e05d5a443fe832d57a7ba154605 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 10:37:16 +0800 Subject: [PATCH 130/135] allowing whether to show instructions --- expo/evaluation/visualize_mcts.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/expo/evaluation/visualize_mcts.py b/expo/evaluation/visualize_mcts.py index e429789fd..6a8869670 100644 --- a/expo/evaluation/visualize_mcts.py +++ b/expo/evaluation/visualize_mcts.py @@ -78,7 +78,7 @@ def get_node_color(node): return f"#{red:02X}{green:02X}00" -def visualize_tree(graph, save_path=""): +def visualize_tree(graph, show_instructions=False, save_path=""): # Use a hierarchical layout for tree-like visualization pos = nx.spring_layout(graph, k=0.9, iterations=50) @@ -113,11 +113,12 @@ def visualize_tree(graph, save_path=""): labels = nx.get_node_attributes(graph, "label") nx.draw_networkx_labels(graph, pos, labels, font_size=NODE_FONT_SIZE) - # Add instructions to the right side of nodes - instructions = nx.get_node_attributes(graph, "instruction") - for node, (x, y) in pos.items(): - wrapped_text = textwrap.fill(instructions[node], width=30) # Adjust width as needed - plt.text(x + 0.05, y, wrapped_text, fontsize=15, ha="left", va="center") + if show_instructions: + # Add instructions to the right side of nodes + instructions = nx.get_node_attributes(graph, "instruction") + for node, (x, y) in pos.items(): + wrapped_text = textwrap.fill(instructions[node], width=30) # Adjust width as needed + plt.text(x + 0.05, y, wrapped_text, fontsize=15, ha="left", va="center") plt.title("MCTS Tree Visualization", fontsize=40) plt.axis("off") # Turn off axis From 06710fbc18a9298ccf257edf7930100cce766bb9 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 15:24:22 +0800 Subject: [PATCH 131/135] fix typo in readme.md --- expo/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/expo/README.md b/expo/README.md index 6704582b8..1d0d8476d 100644 --- a/expo/README.md +++ b/expo/README.md @@ -65,7 +65,7 @@ pip install -r requirements.txt If the dataset has reg metric, remember to use `--low_is_better`: -- `python run_experiment.py --exp_mode mcts --task house_prices --rollouts 10 --low_is_better` +- `python run_experiment.py --exp_mode mcts --task house-prices --rollouts 10 --low_is_better` In addition to the generated insights, include the fixed insights saved in `expo/insights/fixed_insights.json` From 852fbc58ee9eaa77160dcd1bcdcd533c896ed6a9 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 17:47:41 +0800 Subject: [PATCH 132/135] automatically update args.low_is_better for mle-bench --- expo/data/custom_task.py | 9 +++++++++ expo/run_experiment.py | 4 +++- 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/expo/data/custom_task.py b/expo/data/custom_task.py index c4dd0012c..f3cd433f5 100644 --- a/expo/data/custom_task.py +++ b/expo/data/custom_task.py @@ -35,6 +35,15 @@ def get_mle_task_id(dataset_dir): return dataset_dir.split("/")[-3] +def get_mle_is_lower_better(task): + from mlebench.data import get_leaderboard + from mlebench.registry import registry + + competition = registry.get_competition(task) + competition_leaderboard = get_leaderboard(competition) + return competition.grader.is_lower_better(competition_leaderboard) + + def get_mle_bench_requirements(dataset_dir, data_config, special_instruction, obfuscated=False): work_dir = data_config["work_dir"] task = get_mle_task_id(dataset_dir) diff --git a/expo/run_experiment.py b/expo/run_experiment.py index be891814d..7b49e6738 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -1,7 +1,7 @@ import argparse import asyncio -from expo.data.custom_task import get_mle_task_id +from expo.data.custom_task import get_mle_is_lower_better, get_mle_task_id from expo.experimenter.aug import AugExperimenter from expo.experimenter.autogluon import GluonExperimenter from expo.experimenter.autosklearn import AutoSklearnExperimenter @@ -33,6 +33,8 @@ def get_args(cmd=True): args.eval_func = "mlebench" args.from_scratch = True args.task = get_mle_task_id(args.custom_dataset_dir) + args.low_is_better = get_mle_is_lower_better(args.task) + print("low_is_better:", args.low_is_better) return args From 6f437bb76d3897a687206ce9a7e9392d149dffc7 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 17:49:31 +0800 Subject: [PATCH 133/135] automatically change low_is_better for rmse --- expo/MCTS.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/expo/MCTS.py b/expo/MCTS.py index 8778554ed..2ce559ae0 100644 --- a/expo/MCTS.py +++ b/expo/MCTS.py @@ -43,6 +43,8 @@ def create_initial_state(task, start_task_id, data_config, args): task = get_mle_task_id(args.custom_dataset_dir) else: dataset_config = data_config["datasets"][task] + if dataset_config["metric"] == "rmse": + args.low_is_better = True datasets_dir = get_split_dataset_path(task, data_config) requirement = generate_task_requirement( task, data_config, is_di=True, special_instruction=args.special_instruction From de42e32b8e5a5293365852fde63ccaf0f69d4d95 Mon Sep 17 00:00:00 2001 From: Yizhou Chi Date: Thu, 17 Oct 2024 17:55:24 +0800 Subject: [PATCH 134/135] automatically update low_is_better for our task --- expo/insights/instruction_generator.py | 4 +++- expo/run_experiment.py | 1 - 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/expo/insights/instruction_generator.py b/expo/insights/instruction_generator.py index 835c1ff9d..78b32e45d 100644 --- a/expo/insights/instruction_generator.py +++ b/expo/insights/instruction_generator.py @@ -41,7 +41,9 @@ def __init__(self, state, use_fixed_insights, from_scratch): with open(f"{state['custom_dataset_dir']}/description.md", "r", encoding="utf-8") as file: self.dataset_info = file.read() else: - dataset_info_path = f"{self.data_config['datasets_dir']}/{state['task']}/dataset_info.json" + dataset_info_path = ( + f"{self.data_config['datasets_dir']}/{state['dataset_config']['dataset']}/dataset_info.json" + ) with open(dataset_info_path, "r") as file: self.dataset_info = json.load(file) self.use_fixed_insights = use_fixed_insights diff --git a/expo/run_experiment.py b/expo/run_experiment.py index 7b49e6738..68c3b35d4 100644 --- a/expo/run_experiment.py +++ b/expo/run_experiment.py @@ -34,7 +34,6 @@ def get_args(cmd=True): args.from_scratch = True args.task = get_mle_task_id(args.custom_dataset_dir) args.low_is_better = get_mle_is_lower_better(args.task) - print("low_is_better:", args.low_is_better) return args From 1915d19f24156073db688ab1a4472d5d274ac126 Mon Sep 17 00:00:00 2001 From: duiyipan Date: Thu, 17 Oct 2024 21:28:49 +0800 Subject: [PATCH 135/135] update aide readme --- expo/README.md | 39 +-------------------------------------- 1 file changed, 1 insertion(+), 38 deletions(-) diff --git a/expo/README.md b/expo/README.md index 598de039d..a25f384b6 100644 --- a/expo/README.md +++ b/expo/README.md @@ -135,46 +135,15 @@ python -u evaluation.py --path "deepseek-coder_True_1" --task $TASK --device 0 git clone https://github.com/WecoAI/aideml.git ``` -修改 `aideml/aide/utils/config.yaml` 内容如下 +修改 `aideml/aide/utils/config.yaml` 其中的 `step` `k_fold_validation` `code model` `feedback model` 参数如下 ```yaml -# path to the task data directory -data_dir: null - -# either provide a path to a plaintext file describing the task -desc_file: null -# or provide the task goal (and optionally evaluation information) as arguments -goal: null -eval: null - -log_dir: logs -workspace_dir: workspaces - -# whether to unzip any archives in the data directory -preprocess_data: True -# whether to copy the data to the workspace directory (otherwise it will be symlinked) -# copying is recommended to prevent the agent from accidentally modifying the original data -copy_data: True - -exp_name: null # a random experiment name will be generated if not provided - -# settings for code execution -exec: - timeout: 3600 - agent_file_name: runfile.py - format_tb_ipython: False - # agent hyperparams agent: # how many improvement iterations to run steps: 10 # whether to instruct the agent to use CV (set to 1 to disable) k_fold_validation: 1 - # whether to instruct the agent to generate a prediction function - expose_prediction: False - # whether to provide the agent with a preview of the data - data_preview: True - # LLM settings for coding code: model: deepseek-coder @@ -184,12 +153,6 @@ agent: feedback: model: deepseek-coder temp: 0.5 - - # hyperparameters for the tree search - search: - max_debug_depth: 3 - debug_prob: 0.5 - num_drafts: 5 ``` 由于 deepseek 完全兼容 OpenAI 的 API，修改`base_url`为`自己的url`，`api_key`为`自己的key`即可

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