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 .
- Entropic regularization OT solver with Sinkhorn Knopp Algorithm  and stabilized version  with optional GPU implementation (required cudamat).
- Bregman projections for Wasserstein barycenter  and unmixing .
- Optimal transport for domain adaptation with group lasso regularization 
- Conditional gradient  and Generalized conditional gradient for regularized OT .
- Joint OT matrix and mapping estimation .
- Wasserstein Discriminant Analysis  (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, 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)
Anaconda installation with conda-forge¶
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
Post installation check¶
After a correct installation, you should be able to import the module without errors:
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
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
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)
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):
Contributions and code of conduct¶
You can ask questions and join the development discussion:
You can also post bug reports and feature requests in Github issues. Make sure to read our guidelines first.
 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.
 Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300).
 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.
 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.
 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
 Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
 Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567.
 M. Perrot, N. Courty, R. Flamary, A. Habrard, Mapping estimation for discrete optimal transport, Neural Information Processing Systems (NIPS), 2016.
 Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519.
 Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.
 Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063.