This repository contains a Python 3.6 implementation of the nonparametric linear-time relative goodness-of-fit tests (i.e., Rel-UME and Rel-FSSD) described in our paper
Informative Features for Model Comparison Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton NIPS 2018 https://arxiv.org/abs/1810.11630
If you plan to reproduce experimental results, you will probably want to modify our code. It is best to install by:
Clone the repository by
git clone email@example.com:wittawatj/kernel-mod.git.
cdto the folder that you get, and install our package by
pip install -e .
Alternatively, if you only want to use the developed package, you can do the following without cloning the repository.
pip install git+https://github.com/wittawatj/kernel-mod.git
Either way, once installed, you should be able to do
import kmod without any error.
scipy, Pytorch 0.4.1 and the following two packages.
kgofpackage. This can be obtained from its git repository.
freqopttest(containing the UME two-sample test) package from its git repository.
In Python, make sure you can
import freqopttest and
import kgof without
To get started, check
This is a Jupyter notebook which will guide you through from the beginning.
There are many Jupyter notebooks in
ipynb folder demonstrating other
implemented tests. Be sure to check them if you would like to explore.
All experiments which involve test powers can be found in
kmod/ex/ex3_real_images.py. Each file is runnable with a command line
argument. For example in
ex1_vary_n.py, we aim to check the test power of each testing algorithm
as a function of the sample size
n. The script
ex1_vary_n.py takes a
dataset name as its argument. See
run_ex1.sh which is a standalone Bash
script on how to execute
We used independent-jobs
package to parallelize our experiments over a
Slurm cluster (the package is not needed if you
just need to use our developed tests). For example, for
ex1_vary_n.py, a job is created for each combination of
(dataset, test algorithm, n, trial)
If you do not use Slurm, you can change the line
engine = SlurmComputationEngine(batch_parameters)
engine = SerialComputationEngine()
which will instruct the computation engine to just use a normal for-loop on a
single machine (will take a lot of time). Other computation engines that you
use might be supported. Running simulation will
create a lot of result files (one for each tuple above) saved as Pickle. Also, the
package requires a scratch folder to save temporary files for communication
among computing nodes. Path to the folder containing the saved results can be specified in
kmod/config.py by changing the value of
# Full path to the directory to store experimental results. 'expr_results_path': '/full/path/to/where/you/want/to/save/results/',
The scratch folder needed by the
independent-jobs package can be specified in the same file
by changing the value of
# Full path to the directory to store temporary files when running experiments 'scratch_path': '/full/path/to/a/temporary/folder/',
To plot the results, see the experiment's corresponding Jupyter notebook in the
ipynb/ folder. For example, for
ipynb/ex1_results.ipynb to plot the results.
Preprocessing scripts for
cifar10data can be found under
preprocessing/. See the readme files in the sub-folders under
The CNN feature extractor (used to define the kernel) in our Mnist experiment is trained with
Many GAN variants we used (i.e., in experiment 5 in the main text and in the appendix) were trained using the code from https://github.com/janesjanes/GAN_training_code.
Trained GAN models (Pytorch 0.4.1) used in this work can be found at http://ftp.tuebingen.mpg.de/pub/is/wittawat/kmod_share/. The readme files in the sub-folders under
preprocessing/will tell you how to download the model files, for the purpose of reproducing the results.
numpy. Part of the code relies on
autogradto do automatic differentiation. Also use
np.dot(X, Y)instead of
autogradcannot differentiate the latter. Also, do not use
x += .... Use
x = x + ..instead.