Tabpfn Mix 1.0 Regressor

🚀 TabPFNMix Regressor

TabPFNMix regressor is a tabular foundation model pre - trained on synthetic datasets from a mix of random regressors, offering a powerful solution for tabular regression tasks.

🚀 Quick Start

TabPFNMix regressor is a tabular foundation model that is pre - trained on purely synthetic datasets sampled from a mix of random regressors.

✨ Features

TabPFNMix is based on a 12 - layer encoder - decoder Transformer of 37 M parameters. It uses a pre - training strategy incorporating in - context learning, similar to that used by TabPFN and TabForestPFN.

📦 Installation

To use TabPFNMix regressor, install AutoGluon by running:

pip install autogluon

💻 Usage Examples

Basic Usage

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)

📄 Citation

If you find TabPFNMix useful for your research, please consider citing the associated papers:

@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}
}

📄 License

This project is licensed under the Apache - 2.0 License.