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] and stabilized version [9][10] with optional GPU implementation (required cudamat).
• 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].
• Joint OT matrix and mapping estimation [8].
• Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
• Gromov-Wasserstein distances and barycenters [12]

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

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

• Numpy (>=1.11)
• Scipy (>=0.17)
• Cython (>=0.23)
• Matplotlib (>=1.5)

You can install the toolbox through PyPI with:

pip install POT

or get the very latest version by downloading it and then running:

python setup.py install --user # for user install (no root)

If you use the Anaconda python distribution, POT is available in conda-forge. To install it and the required dependencies:

conda install -c conda-forge 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.

Some sub-modules require additional dependences which are discussed below

• ot.dr (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with:

pip install pymanopt autograd

• ot.gpu (GPU accelerated OT) depends on cudamat that have to be installed with:

git clone https://github.com/cudamat/cudamat.git
cd cudamat
python setup.py install --user # for user install (no root)

obviously you need CUDA installed and a compatible GPU.

• Import the toolbox

import ot

• Compute Wasserstein distances

# a,b are 1D histograms (sum to 1 and positive)
# M is the ground cost matrix
Wd=ot.emd2(a,b,M) # exact linear program
Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT
# if b is a matrix compute all distances to a and return a vector

• Compute OT matrix

# a,b are 1D histograms (sum to 1 and positive)
# M is the ground cost matrix
T=ot.emd(a,b,M) # exact linear program
T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT

• Compute Wasserstein barycenter

# A is a n*d matrix containing d  1D histograms
# M is the ground cost matrix
ba=ot.barycenter(A,M,reg) # reg is regularization parameter

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

Here is a list of the Python notebooks available here if you want a quick look:

• 1D optimal transport
• OT Ground Loss
• Multiple EMD computation
• 2D optimal transport on empirical distributions
• 1D Wasserstein barycenter
• OT with user provided regularization
• Domain adaptation with optimal transport
• Color transfer in images
• OT mapping estimation for domain adaptation
• OT mapping estimation for color transfer in images
• Wasserstein Discriminant Analysis
• Gromov Wasserstein
• Gromov Wasserstein Barycenter

You can also see the notebooks with Jupyter nbviewer.

The contributors to this library are:

• Rémi Flamary
• Nicolas Courty
• Alexandre Gramfort
• Laetitia Chapel
• Michael Perrot (Mapping estimation)
• Léo Gautheron (GPU implementation)
• Nathalie Gayraud
• Stanislas Chambon
• Antoine Rolet
• Erwan Vautier (Gromov-Wasserstein)

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)
• Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda)

If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference:

@article{flamary2017pot,
  title={POT Python Optimal Transport library},
  author={Flamary, R{\'e}mi and Courty, Nicolas},
  year={2017}
}

Every contribution is welcome and should respect the contribution guidelines. Each member of the project is expected to follow the code of conduct.

You can ask questions and join the development discussion:

• On the POT Slack channel
• On the POT mailing list

You can also post bug reports and feature requests in Github issues. Make sure to read our guidelines first.

[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.

[9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519.

[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.

[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063.

[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, Gromov-Wasserstein averaging of kernel and distance matrices International Conference on Machine Learning (ICML). 2016.

[13] Mémoli, Facundo. Gromov–Wasserstein distances and the metric approach to object matching. Foundations of computational mathematics 11.4 (2011): 417-487.