Approximate Stein Classes for Truncated Density Estimation

This python repo is intended to reproduce the results from the Approximate Stein Classes for Truncated Density Estimation paper. Within it you will find examples of the TKSD method for basic scenarios, and scripts for reproducing plots found in the paper.

Installation and Requirements

First, you must clone this environment to your working directory, using

git clone https://github.com/dannyjameswilliams/tksd.git 
cd tksd

This work was created using Python 3.8.13. We have included a requirements.txt file, made from pipreqs on this workspace. We recommend if you are going to use this repo, to make a virtual environment. For example, with Anaconda, you can do the following:

conda create -y --name tksd python=3.8.13
conda activate tksd
conda install -y --file requirements.txt 
pip install git+https://github.com/wittawatj/kernel-gof.git

This will create a conda virtual environment called tksd, and install all the packages necessary. We also give credit to the kgof package, from the A Linear-Time Kernel Goodness-of-Fit Test paper, for fast computation of Gram matrices.

Additionally, you could use pip to install it to any environment, not just a virtual one

Reproducing Results

The following lines will reproduce the plots presented in the paper.

Main Text

Figure 1 (top)

python results/usa_boundary.py

Figure 1 (bottom)

python results/usa_benchmark.py

Figure 2

In this example, you can modify the argument to the script to be either the $\ell_2$ ball, or the $\ell_1$ ball. For example,

python results/dimension_benchmark.py 2

will run the experiment for the l2 ball, and

python results/dimension_benchmark.py 1

will run it for the $\ell_1$ ball. Note that running for the $\ell_1$ ball will take a while due to the computational complexity of calculating projections on the $\ell_1$ ball for the distance functions in TruncSM and bdKSD.

Appendix

Figure 3

python appendix/g_convergence.py

Figure 4

python appendix/decreasing_epsilon.py

Figure 5

python appendix/consistency.py

Figure 6

python appendix/mixture.py

Figure 7

python appendix/regression.py

Figure 8 and 9

python appendix/boundary_distribution.py

Figure 10

python appendix/percentage_truncation.py