This fork makes the pgbart code functional on Python 3.X (tested on 3.6).
This folder contains the scripts used in the following paper:
Particle Gibbs for Bayesian Additive Regression Trees
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Proceedings of International Conference on Artificial Intelligence and Statistics (AISTATS), 2015.
Please cite the above paper if you use this code.
Code released under MIT license (see LICENSE for more info).
If you have any questions/comments/suggestions, please contact me at balaji@gatsby.ucl.ac.uk.
Copyright © 2015 Balaji Lakshminarayanan
I ran my experiments using Enthought python (which includes all the necessary python packages). If you are running a different version of python, you will need the following python packages to run the scripts:
- numpy
- scipy
The datasets are not included here; you can run experiments using toy data though. Run commands.sh in process_data folder for automatically downloading and processing the datasets. I have tested these scripts only on Ubuntu, but it should be straightforward to process datasets in other platforms.
List of scripts in the src folder:
- bart.py (main script that does all the computation)
- bart_utils.py (collection of utilities)
- treemcmc.py (MCMC for single tree using CGM/GrowPrune/Particle Gibbs)
- pg.py (Particle Gibbs algorithm)
Help on usage (type the following command on the terminal):
./bart.py -h
Example usage:
CGM: ./bart.py --store_every=1 --m_bart=1 --verbose=0 --mcmc_type=cgm --dataset=toy-hypercube-3 --save=1 --n_iterations=200 --init_id=1 --alpha_split=0.95 --beta_split=0.5 --tag=example --n_run_avg=1
GrowPrune: ./bart.py --store_every=1 --m_bart=1 --verbose=0 --mcmc_type=growprune --dataset=toy-hypercube-3 --save=1 --n_iterations=200 --init_id=1 --alpha_split=0.95 --beta_split=0.5 --tag=example --n_run_avg=1
Particle Gibbs: ./bart.py --store_every=1 --m_bart=1 --verbose=0 --mcmc_type=pg --dataset=toy-hypercube-3 --save=1 --n_iterations=200 --init_id=1 --alpha_split=0.95 --beta_split=0.5 --tag=example --n_run_avg=1 --init_pg=empty
Example on a real-world dataset:
(assuming you have successfully run commands.sh in process_data folder)
Particle Gibbs: ./bart.py --store_every=1 --m_bart=1 --verbose=0 --mcmc_type=pg --dataset=houses_01 --save=1 --n_iterations=200 --init_id=1 --alpha_split=0.95 --beta_split=0.5 --tag=example --n_run_avg=1 --init_pg=empty --data_path='../process_data/'
Running experiments on your dataset:
- process your dataset into suitable format: see load_rgf_datasets in bart_utils.py for an example
- modify load_data in bart_utils.py: either add your dataset name or remove the "raise Exception" line if you prefer that
- Note: you might have to pass data_path as an argument when you call bart.py
Note that the results (predictions, mse, log predictive probability on training/test data, runtimes) are stored in the pickle files. You need to write additional scripts to aggregate the results from these pickle files and generate the plots.
I generated commands for parameter sweeps using 'build_cmds' script by Jan Gasthaus, available publicly at https://github.com/jgasthaus/Gitsby/tree/master/pbs/python.
Example of parameter sweep:
./build_cmds ./bart.py "--store_every={0}" "--m_bart={200}" "--q_bart={0.9}" "--verbose={0}" "--mcmc_type={cgm,growprune,pg}" "--sample_y={0}" "--dataset={msd_01,ctslices_01,houses_01}" "--save={1}" "--n_iterations={2000}" "--init_id=1:1:2" "--alpha_split={0.95}" "--beta_split={2.0}" "--n_run_avg={10}" "--data_path={../process_data/}" >> run