End-to-end Learning of Deterministic Decision Trees

This repository contains an implementation of the algorithms described in the papers "End-to-End Learning of Decision Trees and Forests" written by Thomas Hehn, Julian Kooij and Fred Hamprecht (https://link.springer.com/article/10.1007/s11263-019-01237-6) as well as "End-to-end Learning of Deterministic Decision Trees" written by Thomas Hehn and Fred Hamprecht (https://link.springer.com/chapter/10.1007/978-3-030-12939-2_42).

Installation

The code was tested for python 3.6 and pytorch 1.0. It is recommended to use conda to install the required libraries.

conda env create -f conda_env.yml

How to use

The files run.py and run_forest_run.py are example scripts to show how to use the e2edt package. Run

python run.py --help

for further instructions.

Example to run the decision tree code:

source activate e2edt
python run.py --data MNIST --depth 4 6 --epochs 50 -s 0.01 --batch_size 1000 --algo EM --refine

Example to run the random forest code:

source activate e2edt
python run_forest_run.py --data USPS --depths 10 --epochs 15 -s 0.1 --refine

See data/README.md for details on the datasets.

Intention

The code is published with the intention to support other researchers who want to use the algorithms for their research. Efficiency and speed had only minor priority during implementation. Instead it is intended to be flexible and to offer possibilities for easy debugging.

How to cite

If you use the code, please cite the corresponding IJCV paper:

@Article{Hehn2020,
  author={Hehn, Thomas M. and Kooij, Julian F. P. and Hamprecht, Fred A.},
  title={End-to-End Learning of Decision Trees and Forests},
  journal={International Journal of Computer Vision},
  year={2020},
  month={Apr},
  day={01},
  volume={128},
  number={4},
  pages={997-1011},
  issn={1573-1405},
  doi={10.1007/s11263-019-01237-6},
  url={https://doi.org/10.1007/s11263-019-01237-6}
}

Contact

Feel free to contact the first author Thomas Hehn for questions or feedback who is now at TU Delft (http://intelligent-vehicles.org/people/).

License

The code in this repository is published under the MIT License. See the file LICENSE or https://mit-license.org/ for details.