POT: Python Optimal Transport


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).

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


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 # for user install (no root)

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.


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.


Short examples

  • 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
    # 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
    Totp=ot.emd(a,b,M) # exact linear program
    Totp_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

Examples and Notebooks

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:

You can also see the notebooks with Jupyter nbviewer.


The contributors to this library are:

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):


[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.

Indices and tables