MetaGPT/expo

SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning

1. Data Preparation

• Download Datasets：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink

2. Configs

Data Config

datasets.yaml Provide base prompts, metrics, target columns for respective datasets

• Modify datasets_dir to the root directory of all the datasets in data.yaml

LLM Config

llm:
  api_type: 'openai'
  model: deepseek-coder
  base_url: "https://oneapi.deepwisdom.ai/v1"
  api_key: sk-xxx
  temperature: 0.5

Budget

Experiment rollouts k = 5, 10, 20

Prompt Usage

• 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. SELA

Run SELA

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 --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

将其deployment/generate.py line46-48行部分修改如下（目的是用deepseek而非GPT的API）：

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

Setup

git clone https://github.com/WecoAI/aideml.git

修改 aideml/aide/utils/config.yaml 内容如下

# 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：

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：

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 experimenter/aide.py

Autogluon

Setup

pip install -U pip
pip install -U setuptools wheel
pip install autogluon

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

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).

For an explanation of missing Microsoft Windows and macOS support please check the Section Windows/macOS compatibility.

Setup

pip install auto-sklearn

Run

python run_experiment.py --exp_mode autosklearn --task titanic

Base DI

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