https://github.com/josipd/torch-two-sample

A PyTorch library for two-sample tests
https://github.com/josipd/torch-two-sample

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A PyTorch library for two-sample tests

• Host: GitHub
• URL: https://github.com/josipd/torch-two-sample
• Owner: josipd
• License: other
• Created: 2017-09-26T08:07:20.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-05-27T01:04:12.000Z (over 1 year ago)
• Last Synced: 2024-08-04T00:13:12.031Z (5 months ago)
• Language: Jupyter Notebook
• Size: 82 KB
• Stars: 236
• Watchers: 6
• Forks: 34
• Open Issues: 8
• Metadata Files:
  • Readme: readme.md
  • License: license.txt

Awesome Lists containing this project

• Awesome-pytorch-list-CNVersion - torch-two-sample
• Awesome-pytorch-list - torch-two-sample - sample tests (Pytorch & related libraries / Other libraries:)

README

        
# `torch-two-sample`

[![Documentation Status](https://readthedocs.org/projects/torch-two-sample/badge/?version=latest)](https://torch-two-sample.readthedocs.io/en/latest/)

[![Build Status](https://travis-ci.org/josipd/torch-two-sample.svg?branch=master)](https://travis-ci.org/josipd/torch-two-sample)

A PyTorch library for differentiable two-sample tests

### Description

This package implements a total of six two sample tests:

  * The classical Friedman-Rafsky test *[FR79]*.

  * The classical k-nearest neighbours (kNN) test *[FR83]*.

  * The differentiable Friedman-Rafsky test *[DK17]*.

  * The differentiable k-nearest neighbours (kNN) test *[DK17]*.

  * The maximum mean discrepancy (MMD) test *[GBR+12]*.

  * The energy test *[SzekelyR13]*.

Please refer to the [documentation](https://torch_two_sample.readthedocs.io)

for more information about the project.

You can also have a look at the following [notebook](notebooks/mnist.ipynb)

that showcases how to use the code to train a generative model on MNIST.

### Installation

After installing PyTorch, you can install the package with:

```

python setup.py install

```

### Testing

To run the tests you simply have to run:

```

python setup.py test

```

Note that you will need to have [Shogun](http://www.shogun-toolbox.org)

installed for one of the test cases.

### Bibliography

  * *[DK17]* J. Djolonga and A. Krause. Learning Implicit Generative Models Using Differentiable Graph Tests. ArXiv e-prints, September 2017. arXiv:1709.01006.

  * *[FR79]* Jerome H Friedman and Lawrence C Rafsky. Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests. Annals of Statistics, pages 697–717, 1979.

  * *[FR83]* Jerome H Friedman and Lawrence C Rafsky. Graph-theoretic measures of multivariate association and prediction. Annals of Statistics, pages 377–391, 1983.

  * *[GBR+12]* Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. A kernel two-sample test. Journal of Machine Learning Research, 13(Mar):723–773, 2012.

  * *[SST+12]* Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S Zemel, Ruslan R Salakhutdinov, and Ryan P Adams. Cardinality restricted boltzmann machines. In Advances in Neural Information Processing Systems (NIPS), 3293–3301. 2012.

  * *[SzekelyR13]* Gábor J Székely and Maria L Rizzo. Energy statistics: a class of statistics based on distances. Journal of Statistical Planning and Inference, 143(8):1249–1272, 2013.

  * *[TSZ+12]* Daniel Tarlow, Kevin Swersky, Richard S Zemel, Ryan Prescott Adams, and Brendan J Frey. Fast exact inference for recursive cardinality models. Uncertainty in Artificial Intelligence (UAI), 2012.