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https://github.com/PriorLabs/TabPFN
⚡ TabPFN: Foundation Model for Tabular Data ⚡
https://github.com/PriorLabs/TabPFN
data-science foundation-models machine-learning tabpfn tabular-data
Last synced: 3 days ago
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⚡ TabPFN: Foundation Model for Tabular Data ⚡
- Host: GitHub
- URL: https://github.com/PriorLabs/TabPFN
- Owner: PriorLabs
- License: other
- Created: 2022-07-01T11:54:47.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-05T17:00:26.000Z (6 days ago)
- Last Synced: 2025-02-07T09:00:11.346Z (4 days ago)
- Topics: data-science, foundation-models, machine-learning, tabpfn, tabular-data
- Language: Python
- Homepage: http://priorlabs.ai
- Size: 253 MB
- Stars: 2,463
- Watchers: 30
- Forks: 198
- Open Issues: 27
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# TabPFN
[](https://badge.fury.io/py/tabpfn)
[](https://pepy.tech/project/tabpfn)
[](https://discord.com/channels/1285598202732482621/)
[](https://priorlabs.ai/docs)
[](https://tinyurl.com/tabpfn-colab-local)
[](https://pypi.org/project/tabpfn/)
TabPFN is a foundation model for tabular data that outperforms traditional methods while
being dramatically faster. This repository contains the core PyTorch implementation with
CUDA optimization.
⚠️ **Major Update: Version 2.0:** Complete codebase overhaul with new architecture and
features. Previous version available at [v1.0.0](../../tree/v1.0.0) and
`pip install tabpfn<2`.
📚 For detailed usage examples and best practices, check out [Interactive Colab Tutorial](https://tinyurl.com/tabpfn-colab-local)
## 🌐 TabPFN Ecosystem
Choose the right TabPFN implementation for your needs:
- **[TabPFN Client](https://github.com/automl/tabpfn-client)**: Easy-to-use API client for cloud-based inference
- **[TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions)**: Community extensions and integrations
- **TabPFN (this repo)**: Core implementation for local deployment and research
- **[TabPFN UX](https://ux.priorlabs.ai)**: No-code TabPFN usage
Try our [Interactive Colab Tutorial](https://colab.research.google.com/drive/1SHa43VuHASLjevzO7y3-wPCxHY18-2H6?usp=sharing) to get started quickly.
## 🏁 Quick Start
### Installation
```bash
# Simple installation
pip install tabpfn
# Local development installation
git clone https://github.com/PriorLabs/TabPFN.git
pip install -e "TabPFN[dev]"
```
### Basic Usage
```python
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split
from tabpfn import TabPFNClassifier
# Load data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
# Initialize a classifier
clf = TabPFNClassifier()
clf.fit(X_train, y_train)
# Predict probabilities
prediction_probabilities = clf.predict_proba(X_test)
print("ROC AUC:", roc_auc_score(y_test, prediction_probabilities[:, 1]))
# Predict labels
predictions = clf.predict(X_test)
print("Accuracy", accuracy_score(y_test, predictions))
```
### Best Results
For optimal performance, use the `AutoTabPFNClassifier` or `AutoTabPFNRegressor` for post-hoc ensembling. These can be found in the [TabPFN Extensions](https://github.com/PriorLabs/tabpfn-extensions) repository. Post-hoc ensembling combines multiple TabPFN models into an ensemble.
**Steps for Best Results:**
1. Install the extensions:
```bash
git clone https://github.com/priorlabs/tabpfn-extensions.git
pip install -e tabpfn-extensions
```
2.
```python
from tabpfn_extensions.post_hoc_ensembles.sklearn_interface import AutoTabPFNClassifier
clf = AutoTabPFNClassifier(max_time=120) # 120 seconds tuning time
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
```
See our [Colab](https://colab.research.google.com/drive/1SHa43VuHASLjevzO7y3-wPCxHY18-2H6#scrollTo=49sMXWT5DYzj&line=1&uniqifier=1)
## 🤝 Join Our Community
We're building the future of tabular machine learning and would love your involvement:
1. **Connect & Learn**:
- Join our [Discord Community](https://discord.gg/VJRuU3bSxt)
- Read our [Documentation](https://priorlabs.ai/docs)
- Check out [GitHub Issues](https://github.com/priorlabs/tabpfn/issues)
2. **Contribute**:
- Report bugs or request features
- Submit pull requests
- Share your research and use cases
3. **Stay Updated**: Star the repo and join Discord for the latest updates
## 📜 License
Prior Labs License (Apache 2.0 with additional attribution requirement): [here](https://priorlabs.ai/tabpfn-license/)
## 📚 Citation
You can read our paper explaining TabPFN [here](https://doi.org/10.1038/s41586-024-08328-6).
```bibtex
@article{hollmann2025tabpfn,
title={Accurate predictions on small data with a tabular foundation model},
author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and
Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and
Schirrmeister, Robin Tibor and Hutter, Frank},
journal={Nature},
year={2025},
month={01},
day={09},
doi={10.1038/s41586-024-08328-6},
publisher={Springer Nature},
url={https://www.nature.com/articles/s41586-024-08328-6},
}
@inproceedings{hollmann2023tabpfn,
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},
booktitle={International Conference on Learning Representations 2023},
year={2023}
}
```
## 🛠️ Development
1. Setup environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
git clone https://github.com/PriorLabs/TabPFN.git
cd tabpfn
pip install -e ".[dev]"
pre-commit install
```
2. Before committing:
```bash
pre-commit run --all-files
```
3. Run tests:
```bash
pytest tests/
```
---
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