The TabPFN is a neural network that learned to do tabular data prediction. This is the original CUDA-supporting pytorch impelementation.
We created a Colab, that lets you play with our scikit-learn interface.
We also created two demos. One to experiment with the TabPFNs predictions (https://huggingface.co/spaces/TabPFN/TabPFNPrediction) and one to check cross- validation ROC AUC scores on new datasets (https://huggingface.co/spaces/TabPFN/TabPFNEvaluation). Both of them run on a weak CPU, thus it can require a little bit of time. Both demos are based on a scikit-learn interface that makes using the TabPFN as easy as a scikit-learn SVM.
pip install tabpfnIf you want to evaluate our baselines, too, please install with
pip install tabpfn[baselines]To run the autogluon and autosklearn baseline please create a separate environment and install autosklearn / autogluon==0.4.0, installation in the same environment as our other baselines is not possible.
A simple usage of our sklearn interface is:
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from tabpfn.scripts.transformer_prediction_interface import TabPFNClassifier
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
classifier = TabPFNClassifier(device='cpu')
classifier.fit(X_train, y_train)
y_eval, p_eval = classifier.predict(X_test, return_winning_probability=True)
print('Accuracy', accuracy_score(y_test, y_eval))Read our paper for more information about the setup (or contact us
@misc{tabpfn,
doi = {10.48550/ARXIV.2207.01848},
url = {https://arxiv.org/abs/2207.01848},
author = {Hollmann, Noah and Müller, Samuel and Eggensperger, Katharina and Hutter, Frank},
keywords = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}