Two Sample Testing Based on Random Forest (Replication Code)

We follow the line of using classifiers for two-sample testing and propose several tests based on the Random Forest classifier. The developed tests are easy to use, require no tuning and are applicable for \emph{any} distribution on $\R^p$, even in high-dimensions. We provide a comprehensive treatment for the use of classification for two-sample testing, derive the distribution of our tests under the Null and provide a power analysis, both in theory and with simulations.

Link to the pre-print: https://arxiv.org/abs/1903.06287

How to launch the simulation files

• Install the Python module found on https://github.com/wittawatj/interpretable-test from Wittawat Jitkrittum via the command: pip install git+https://github.com/wittawatj/interpretable-test Once installed, you should be able to import freqopttest.
• Install the R-package hypoRF found in \hypoRF_Code\hypoRF via `the R command install.packages("./hypoRF/", repos = NULL, type = "source").
• Make sure you have installed the R-packages ranger, mvtnorm, reticulate, MASS and parSim (only necessary if you are interested in parallel computing).
• Launch the desired simulation R-file locally in the folder \hypoRF_Code\Replicate_Simulations. If you want to save or plot any result, use the outcommented code at the end of each script.