Metadata-Version: 2.3
Name: deeptlf
Version: 0.3.0
Summary: Deep Tabular Learning Framework
Project-URL: Homepage, https://github.com/unnir/deeptlf
Author-email: Vadim Borisov <vadim.borisov@uni-tuebingen.de>
License: MIT License
        
        Copyright (c) 2024 Vadim Borisov
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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License-File: LICENSE
Keywords: deep-learning,machine-learning,neural-networks,tabular-data
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: torch
Requires-Dist: tqdm
Requires-Dist: xgboost
Description-Content-Type: text/markdown

# DeepTLF: A Framework for Enhanced Deep Learning on Tabular Data

![DeepTLF Pipeline](pipeline.png)

## Overview

**DeepTLF** significantly outperforms traditional Deep Neural Networks (DNNs) in handling tabular data. Using our novel TreeDrivenEncoder, we transform complex, heterogeneous data into a format highly compatible with DNNs. This enables a 19.6% average performance increase compared to conventional DNNs.

## Installation

You can install DeepTLF directly from PyPI:

```bash
pip install deeptlf
```

## Quick Start

Seamlessly integrate DeepTLF into your workflow through its scikit-learn-compatible API:

```python
from deeptlf import DeepTFL

# Initialize and train model
dtlf_model = DeepTFL(n_est=23, max_depth=3, drop=0.23, n_layers=4, task='class')
dtlf_model.fit(X_train, y_train)

# Make predictions
dtlf_y_hat = dtlf_model.predict(X_test)
```

## Features
- Transforms heterogeneous data into DNN-friendly format
- Supports multimodal learning
- Adheres to the scikit-learn API for effortless integration
- Features advanced options like custom layers, dropout rates, and more


## Citation
To cite DeepTLF in your work:
```bib
@article{borisov2022deeptlf,
  title={DeepTLF: robust deep neural networks for heterogeneous tabular data},
  author={Borisov, Vadim and Broelemann, Klaus and Kasneci, Enkelejda and Kasneci, Gjergji},
  journal={International Journal of Data Science and Analytics},
  pages={1--16},
  year={2022},
  publisher={Springer}
}
