POT 0.1.6

Python Optimal Transport Library

This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

It provides the following solvers:

• OT solver for the linear program/ Earth Movers Distance [1].

• Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2].

• Bregman projections for Wasserstein barycenter [3] and unmixing [4].

• Optimal transport for domain adaptation with group lasso regularization [5]

• Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].

We are also currently working on the following features:

• [ ] Image color adaptation demo

• [ ] Scikit-learn inspired classes for domain adaptation

• [ ] Mapping estimation as proposed in [8]

Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder.

Installation

The Library has been tested on Linux and MacOSX. It requires a C++ compiler for using the EMD solver and rely on the following Python modules:

• Numpy (>=1.11)

• Scipy (>=0.17)

• Cython (>=0.23)

• Matplotlib (>=1.5)

Under debian based linux the dependencies can be installed with

sudo apt-get install python-numpy python-scipy python-matplotlib cython

To install the library, you can install it locally (after downloading it) on you machine using

python setup.py install --user

The toolbox is also available on PyPI with a possibly slightly older version. You can install it with:

pip install POT

After a correct installation, you should be able to import the module without errors:

import ot

Note that for easier access the module is name ot instead of pot.

Examples

The examples folder contain several examples and use case for the library. The full documentation is available on Readthedoc

Here is a list of the Python notebook if you want a quick look:

• 1D optimal transport

• 2D optimal transport on empirical distributions

• 1D Wasserstein barycenter

• OT with user provided regularization

Acknowledgements

The contributors to this library are:

• Rémi Flamary

• Nicolas Courty

• Laetitia Chapel

This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages):

• Gabriel Peyré (Wasserstein Barycenters in Matlab)

• Nicolas Bonneel ( C++ code for EMD)

• Antoine Rolet ( Mex file for EMD )

• Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda)

References

[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.

[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300).

[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138.

[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016.

[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, “Optimal Transport for Domain Adaptation,” in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1

[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.

[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.

[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, “Mapping estimation for discrete optimal transport”, Neural Information Processing Systems (NIPS), 2016.