Metadata-Version: 2.4
Name: tabpfn
Version: 2.1.3
Summary: TabPFN: Foundation model for tabular data
Author: Noah Hollmann, Samuel Müller, Lennart Purucker, Arjun Krishnakumar, Max Körfer, Shi Bin Hoo, Robin Tibor Schirrmeister, Frank Hutter, Eddie Bergman, Leo Grinsztajn, Felix Jabloski, Klemens Flöge, Oscar Key, Felix Birkel, Philipp Jund
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# TabPFN

[![PyPI version](https://badge.fury.io/py/tabpfn.svg)](https://badge.fury.io/py/tabpfn)
[![Downloads](https://pepy.tech/badge/tabpfn)](https://pepy.tech/project/tabpfn)
[![Discord](https://img.shields.io/discord/1285598202732482621?color=7289da&label=Discord&logo=discord&logoColor=ffffff)](https://discord.gg/BHnX2Ptf4j)
[![Documentation](https://img.shields.io/badge/docs-priorlabs.ai-blue)](https://priorlabs.ai/docs)
[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)
[![Python Versions](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue)](https://pypi.org/project/tabpfn/)

<img src="https://github.com/PriorLabs/tabpfn-extensions/blob/main/tabpfn_summary.webp" width="80%" alt="TabPFN Summary">

## 🏁 Quick Start

### Interactive Notebook Tutorial
> [!TIP]
>
> Dive right in with our interactive Colab notebook! It's the best way to get a hands-on feel for TabPFN, walking you through installation, classification, and regression examples.
>
> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PriorLabs/TabPFN/blob/main/examples/notebooks/TabPFN_Demo_Local.ipynb)

> ⚡ **GPU Recommended**:
> For optimal performance, use a GPU (even older ones with ~8GB VRAM work well; 16GB needed for some large datasets).
> On CPU, only small datasets (≲1000 samples) are feasible.
> No GPU? Use our free hosted inference via [TabPFN Client](https://github.com/PriorLabs/tabpfn-client).

### Installation
Official installation (pip)
```bash
pip install tabpfn
```
OR installation from source
```bash
pip install "tabpfn @ git+https://github.com/PriorLabs/TabPFN.git"
```
OR local development installation
```bash

git clone https://github.com/PriorLabs/TabPFN.git --depth 1
pip install -e "TabPFN[dev]"
```

### Basic Usage

#### Classification
```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))
```

#### Regression
```python
from sklearn.datasets import fetch_openml
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split

from tabpfn import TabPFNRegressor

# Load Boston Housing data
df = fetch_openml(data_id=531, as_frame=True)  # Boston Housing dataset
X = df.data
y = df.target.astype(float)  # Ensure target is float for regression

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)

# Initialize the regressor
regressor = TabPFNRegressor()
regressor.fit(X_train, y_train)

# Predict on the test set
predictions = regressor.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print("Mean Squared Error (MSE):", mse)
print("R² Score:", r2)
```

### 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, device="cuda") # 120 seconds tuning time
   clf.fit(X_train, y_train)
   predictions = clf.predict(X_test)
   ```

## 🌐 TabPFN Ecosystem

Choose the right TabPFN implementation for your needs:

- **[TabPFN Client](https://github.com/priorlabs/tabpfn-client)**
  Simple API client for using TabPFN via cloud-based inference.

- **[TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions)**
  A powerful companion repository packed with advanced utilities, integrations, and features - great place to contribute:

  - 🔍 **`interpretability`**: Gain insights with SHAP-based explanations, feature importance, and selection tools.
  - 🕵️‍♂️ **`unsupervised`**: Tools for outlier detection and synthetic tabular data generation.
  - 🧬 **`embeddings`**: Extract and use TabPFN’s internal learned embeddings for downstream tasks or analysis.
  - 🧠 **`many_class`**: Handle multi-class classification problems that exceed TabPFN's built-in class limit.
  - 🌲 **`rf_pfn`**: Combine TabPFN with traditional models like Random Forests for hybrid approaches.
  - ⚙️ **`hpo`**: Automated hyperparameter optimization tailored to TabPFN.
  - 🔁 **`post_hoc_ensembles`**: Boost performance by ensembling multiple TabPFN models post-training.

  ✨ To install:
  ```bash
  git clone https://github.com/priorlabs/tabpfn-extensions.git
  pip install -e tabpfn-extensions
  ```

- **[TabPFN (this repo)](https://github.com/priorlabs/tabpfn)**
  Core implementation for fast and local inference with PyTorch and CUDA support.

- **[TabPFN UX](https://ux.priorlabs.ai)**
  No-code graphical interface to explore TabPFN capabilities—ideal for business users and prototyping.

## 📜 License

Prior Labs License (Apache 2.0 with additional attribution requirement): [here](https://priorlabs.ai/tabpfn-license/)

## 🤝 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

## 📚 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}
}
```



## ❓ FAQ

### **Usage & Compatibility**

**Q: What dataset sizes work best with TabPFN?**
A: TabPFN is optimized for **datasets up to 10,000 rows**. For larger datasets, consider using **Random Forest preprocessing** or other extensions. See our [Colab notebook](https://colab.research.google.com/drive/154SoIzNW1LHBWyrxNwmBqtFAr1uZRZ6a#scrollTo=OwaXfEIWlhC8) for strategies.

**Q: Why can't I use TabPFN with Python 3.8?**
A: TabPFN v2 requires **Python 3.9+** due to newer language features. Compatible versions: **3.9, 3.10, 3.11, 3.12, 3.13**.

### **Installation & Setup**

**Q: How do I use TabPFN without an internet connection?**

TabPFN automatically downloads model weights when first used. For offline usage:

**Using the Provided Download Script**

If you have the TabPFN repository, you can use the included script to download all models (including ensemble variants):

```bash
# After installing TabPFN
python scripts/download_all_models.py
```

This script will download the main classifier and regressor models, as well as all ensemble variant models to your system's default cache directory.

**Manual Download**

1. Download the model files manually from HuggingFace:
   - Classifier: [tabpfn-v2-classifier.ckpt](https://huggingface.co/Prior-Labs/TabPFN-v2-clf/resolve/main/tabpfn-v2-classifier.ckpt)
   - Regressor: [tabpfn-v2-regressor.ckpt](https://huggingface.co/Prior-Labs/TabPFN-v2-reg/resolve/main/tabpfn-v2-regressor.ckpt)

2. Place the file in one of these locations:
   - Specify directly: `TabPFNClassifier(model_path="/path/to/model.ckpt")`
   - Set environment variable: `export TABPFN_MODEL_CACHE_DIR="/path/to/dir"` (see environment variables FAQ below)
   - Default OS cache directory:
     - Windows: `%APPDATA%\tabpfn\`
     - macOS: `~/Library/Caches/tabpfn/`
     - Linux: `~/.cache/tabpfn/`

**Q: I'm getting a `pickle` error when loading the model. What should I do?**
A: Try the following:
- Download the newest version of tabpfn `pip install tabpfn --upgrade`
- Ensure model files downloaded correctly (re-download if needed)

**Q: What environment variables can I use to configure TabPFN?**
A: TabPFN uses Pydantic settings for configuration, supporting environment variables and `.env` files:

**Model Configuration:**
- `TABPFN_MODEL_CACHE_DIR`: Custom directory for caching downloaded TabPFN models (default: platform-specific user cache directory)
- `TABPFN_ALLOW_CPU_LARGE_DATASET`: Allow running TabPFN on CPU with large datasets (>1000 samples). Set to `true` to override the CPU limitation. Note: This will be very slow!

**PyTorch Settings:**
- `PYTORCH_CUDA_ALLOC_CONF`: PyTorch CUDA memory allocation configuration to optimize GPU memory usage (default: `max_split_size_mb:512`). See [PyTorch CUDA documentation](https://docs.pytorch.org/docs/stable/notes/cuda.html#optimizing-memory-usage-with-pytorch-cuda-alloc-conf) for more information.

Example:
```bash
export TABPFN_MODEL_CACHE_DIR="/path/to/models"
export TABPFN_ALLOW_CPU_LARGE_DATASET=true
export PYTORCH_CUDA_ALLOC_CONF="max_split_size_mb:512"
```

Or simply set them in your `.env`

**Q: How do I save and load a trained TabPFN model?**
A: Use :func:`save_fitted_tabpfn_model` to persist a fitted estimator and reload
it later with :func:`load_fitted_tabpfn_model` (or the corresponding
``load_from_fit_state`` class methods).

```python
from tabpfn import TabPFNRegressor
from tabpfn.model_loading import (
    load_fitted_tabpfn_model,
    save_fitted_tabpfn_model,
)

# Train the regressor on GPU
reg = TabPFNRegressor(device="cuda")
reg.fit(X_train, y_train)
save_fitted_tabpfn_model(reg, "my_reg.tabpfn_fit")

# Later or on a CPU-only machine
reg_cpu = load_fitted_tabpfn_model("my_reg.tabpfn_fit", device="cpu")
```

To store just the foundation model weights (without a fitted estimator) use
``save_tabpfn_model(reg.model_, "my_tabpfn.ckpt")``. This merely saves a
checkpoint of the pre-trained weights so you can later create and fit a fresh
estimator. Reload the checkpoint with ``load_model_criterion_config``.

### **Performance & Limitations**

**Q: Can TabPFN handle missing values?**
A: **Yes!**

**Q: How can I improve TabPFN’s performance?**
A: Best practices:
- Use **AutoTabPFNClassifier** from [TabPFN Extensions](https://github.com/priorlabs/tabpfn-extensions) for post-hoc ensembling
- Feature engineering: Add domain-specific features to improve model performance
Not effective:
  - Adapt feature scaling
  - Convert categorical features to numerical values (e.g., one-hot encoding)

## 🛠️ 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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