This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.
Website and documentation: https://PythonOT.github.io/
Source Code (MIT): https://github.com/PythonOT/POT
POT provides the following generic OT solvers (links to examples):
- OT Network Simplex solver for the linear program/ Earth Movers Distance [1] .
- Conditional gradient [6] and Generalized conditional gradient for regularized OT [7].
- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] , stabilized version [9] [10] [34], greedy Sinkhorn [22] and Screening Sinkhorn [26] .
- Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4].
- Sinkhorn divergence [23] and entropic regularization OT from empirical data.
- Debiased Sinkhorn barycenters Sinkhorn divergence barycenter [37]
- Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17].
- Weak OT solver between empirical distributions [39]
- Non regularized Wasserstein barycenters [16] ) with LP solver (only small scale).
- Gromov-Wasserstein distances and GW barycenters (exact [13] and regularized [12]), differentiable using gradients from
- Fused-Gromov-Wasserstein distances solver and FGW barycenters [24]
- Stochastic solver for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
- Stochastic solver of Gromov Wasserstein for large-scale problem with any loss functions [33]
- Non regularized free support Wasserstein barycenters [20].
- Unbalanced OT with KL relaxation and barycenter [10, 25].
- Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations).
- Sliced Wasserstein [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36].
- Graph Dictionary Learning solvers [38].
- Several backends for easy use of POT with Pytorch/jax/Numpy/Cupy/Tensorflow arrays.
POT provides the following Machine Learning related solvers:
- Optimal transport for domain adaptation with group lasso regularization, Laplacian regularization [5] [30] and semi supervised setting.
- Linear OT mapping [14] and Joint OT mapping estimation [8].
- Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt).
- JCPOT algorithm for multi-source domain adaptation with target shift [27].
Some other examples are available in the documentation.
If you use this toolbox in your research and find it useful, please cite POT using the following reference from our JMLR paper:
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer,
POT Python Optimal Transport library,
Journal of Machine Learning Research, 22(78):1−8, 2021.
Website: https://pythonot.github.io/
In Bibtex format:
@article{flamary2021pot,
author = {R{\'e}mi Flamary and Nicolas Courty and Alexandre Gramfort and Mokhtar Z. Alaya and Aur{\'e}lie Boisbunon and Stanislas Chambon and Laetitia Chapel and Adrien Corenflos and Kilian Fatras and Nemo Fournier and L{\'e}o Gautheron and Nathalie T.H. Gayraud and Hicham Janati and Alain Rakotomamonjy and Ievgen Redko and Antoine Rolet and Antony Schutz and Vivien Seguy and Danica J. Sutherland and Romain Tavenard and Alexander Tong and Titouan Vayer},
title = {POT: Python Optimal Transport},
journal = {Journal of Machine Learning Research},
year = {2021},
volume = {22},
number = {78},
pages = {1-8},
url = {http://jmlr.org/papers/v22/20-451.html}
}The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules:
- Numpy (>=1.16)
- Scipy (>=1.0)
- Cython (>=0.23) (build only, not necessary when installing from pip or conda)
You can install the toolbox through PyPI with:
pip install POTor get the very latest version by running:
pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --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 potAfter a correct installation, you should be able to import the module without errors:
import otNote that for easier access the module is named ot instead of pot.
Some sub-modules require additional dependences which are discussed below
- ot.dr (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
pip install pymanopt autograd- ot.gpu (GPU accelerated OT) depends on cupy that have to be installed following instructions on this page. Obviously you will need CUDA installed and a compatible GPU. Note that this module is deprecated since version 0.8 and will be deleted in the future. GPU is now handled automatically through the backends and several solver already can run on GPU using the Pytorch backend.
- 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 parameterThe examples folder contain several examples and use case for the library. The full documentation with examples and output is available on https://PythonOT.github.io/.
This toolbox has been created and is maintained by
The contributors to this library are
- Alexandre Gramfort (CI, documentation)
- Laetitia Chapel (Partial OT)
- Michael Perrot (Mapping estimation)
- Léo Gautheron (GPU implementation)
- Nathalie Gayraud (DA classes)
- Stanislas Chambon (DA classes)
- Antoine Rolet (EMD solver debug)
- Erwan Vautier (Gromov-Wasserstein)
- Kilian Fatras (Stochastic solvers)
- Alain Rakotomamonjy
- Vayer Titouan (Gromov-Wasserstein -, Fused-Gromov-Wasserstein)
- Hicham Janati (Unbalanced OT, Debiased barycenters)
- Romain Tavenard (1d Wasserstein)
- Mokhtar Z. Alaya (Screenkhorn)
- Ievgen Redko (Laplacian DA, JCPOT)
- Adrien Corenflos (Sliced Wasserstein Distance)
- Tanguy Kerdoncuff (Sampled Gromov Wasserstein)
- Minhui Huang (Projection Robust Wasserstein Distance)
- Nathan Cassereau (Backends)
- Cédric Vincent-Cuaz (Graph Dictionary Learning)
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)
- Mathieu Blondel (original implementation smooth OT)
- Nicolas Bonneel (C++ code for EMD)
- Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda)
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 gitter 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.
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