MAPTree

This repository contains all of the code to reproduce the experiments from 'MAPTree: Beating "Optimal" Decision Trees with Bayesian Decision Trees' by Colin Sullivan*, Mo Tiwari*, and Sebastian Thrun.

Our main algorithm is written in C++ and called from Python via Python bindings.

Below, we have instructions on how to reproduce all of our results. If you have a question about our code, please submit a Github issue.

Set Up The Environment

Our code only supports Python 3.10. Note that our dependencies require installing the old sklearn package (not scikit-learn); you may need run the following command to allow this:

export SKLEARN_ALLOW_DEPRECATED_SKLEARN_PACKAGE_INSTALL=True

M1 Macs

If you're on an M1 Mac, you'll need to install gosdt from source; see the instructions here. Afterwards, install the other dependencies and the maptree Python package with

python -m pip install -r requirements_m1mac.txt
python -m pip install .

Other platforms

If you're not on an M1 Mac, you can install all dependencies and then build the maptree Python package directly:

python -m pip install -r requirements.txt
python -m pip install .

Install Data

Download all the necessary data with

python setup_data.py

Run Experiments on a Personal Computer

We support running all experiments on a single machine (e.g., a personal laptop) with:

python run_experiment.py

Note that this may take a long time (1-2 weeks), depending on your hardware.

Run Experiments on a Cluster

We have also included a script to run the experiments on a cluster via SLURM, which can be invoked with:

sbatch script.slurm

Note that this may take some time (1-2 days) to complete, depending on your hardware, and that the parameters of the script.slurm file may need to be modified according to your cluster setup.

Plot Results

You can plot the results of all experiments with:

python plot_results.py

The plots can be found in experiments/results/figures.

Hyperparameters for all the experiments can be found in the following files:

experiments/globals.py
experiments/experiments/fig1/constants.py
experiments/experiments/fig2/constants.py
experiments/experiments/fig3/constants.py