About

Overview

Given two datasets sample_P and sample_Q drawn from distributions $P$ and $Q$, the goal is to estimate a $p$ value for the null hypothesis $P=Q$. autotst achieves this by learning a witness function and taking its mean discrepancy as a test statistic (see References).

The package provides functionalities to prepare the data, an interface to train an ML model, and methods to evaluate p values and interpret results.

By default, autotst uses the Tabular Predictor of AutoGluon, but it is easy to wrap and use your own favorite ML framework.

Source code and usage examples

The source code and usage examples can be found on github

Reference

Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf: “AutoML Two-Sample Test” (2022).