Tabpfn Mix 1.0 Regressor

🚀 TabPFNMix回归器

TabPFNMix回归器是一种表格基础模型，它在从随机回归器组合中采样得到的纯合成数据集上进行预训练。该模型能够有效处理表格数据的回归问题，为相关领域的研究和应用提供了强大的支持。

🚀 快速开始

要使用TabPFNMix回归器，你需要先安装AutoGluon，可通过运行以下命令进行安装：

pip install autogluon

✨ 主要特性

TabPFNMix基于一个具有3700万个参数的12层编码器 - 解码器Transformer架构。它采用了一种结合上下文学习的预训练策略，与TabPFN和TabForestPFN所使用的策略类似。

💻 使用示例

基础用法

以下是一个使用TabPFNMix回归器进行微调与推理的最小示例：

import pandas as pd

from autogluon.tabular import TabularPredictor


if __name__ == '__main__':  
    train_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')
    subsample_size = 5000
    if subsample_size is not None and subsample_size < len(train_data):
        train_data = train_data.sample(n=subsample_size, random_state=0)
    test_data = pd.read_csv('https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')

    tabpfnmix_default = {
        "model_path_classifier": "autogluon/tabpfn-mix-1.0-classifier",
        "model_path_regressor": "autogluon/tabpfn-mix-1.0-regressor",
        "n_ensembles": 1,
        "max_epochs": 30,
    }

    hyperparameters = {
        "TABPFNMIX": [
            tabpfnmix_default,
        ],
    }

    label = "age"
    problem_type = "regression"

    predictor = TabularPredictor(
        label=label,
        problem_type=problem_type,
    )
    predictor = predictor.fit(
        train_data=train_data,
        hyperparameters=hyperparameters,
        verbosity=3,
    )

    predictor.leaderboard(test_data, display=True)

📚 详细文档

如果你在研究中发现TabPFNMix很有用，请考虑引用相关论文：

@article{erickson2020autogluon,
  title={Autogluon-tabular: Robust and accurate automl for structured data},
  author={Erickson, Nick and Mueller, Jonas and Shirkov, Alexander and Zhang, Hang and Larroy, Pedro and Li, Mu and Smola, Alexander},
  journal={arXiv preprint arXiv:2003.06505},
  year={2020}
}

@article{hollmann2022tabpfn,
  title={Tabpfn: A transformer that solves small tabular classification problems in a second},
  author={Hollmann, Noah and M{\"u}ller, Samuel and Eggensperger, Katharina and Hutter, Frank},
  journal={arXiv preprint arXiv:2207.01848},
  year={2022}
}

@article{breejen2024context,
  title={Why In-Context Learning Transformers are Tabular Data Classifiers},
  author={Breejen, Felix den and Bae, Sangmin and Cha, Stephen and Yun, Se-Young},
  journal={arXiv preprint arXiv:2405.13396},
  year={2024}
}

📄 许可证

本项目采用Apache-2.0许可证。