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https://github.com/PythonOT/POT
POT : Python Optimal Transport
https://github.com/PythonOT/POT
domain-adaptation emd gromov-wasserstein machine-learning numerical-optimization optimal-transport ot-mapping-estimation ot-solver pot python sinkhorn-divergences sinkhorn-knopp wasserstein wasserstein-barycenter wasserstein-barycenters wasserstein-discriminant-analysis
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POT : Python Optimal Transport
- Host: GitHub
- URL: https://github.com/PythonOT/POT
- Owner: PythonOT
- License: mit
- Created: 2016-10-20T14:42:14.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-05-28T20:38:37.000Z (4 months ago)
- Last Synced: 2024-05-29T04:13:49.934Z (4 months ago)
- Topics: domain-adaptation, emd, gromov-wasserstein, machine-learning, numerical-optimization, optimal-transport, ot-mapping-estimation, ot-solver, pot, python, sinkhorn-divergences, sinkhorn-knopp, wasserstein, wasserstein-barycenter, wasserstein-barycenters, wasserstein-discriminant-analysis
- Language: Python
- Homepage: https://PythonOT.github.io/
- Size: 42.9 MB
- Stars: 2,270
- Watchers: 48
- Forks: 483
- Open Issues: 51
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-sciml - PythonOT/POT: POT : Python Optimal Transport
- awesome-list - POT - Python Optimal Transport. (Linear Algebra / Statistics Toolkit / Statistical Toolkit)
README
# POT: Python Optimal Transport
[](https://badge.fury.io/py/POT)
[](https://anaconda.org/conda-forge/pot)
[](https://github.com/PythonOT/POT/actions)
[](https://codecov.io/gh/PythonOT/POT)
[](https://pepy.tech/project/pot)
[](https://anaconda.org/conda-forge/pot)
[](https://github.com/PythonOT/POT/blob/master/LICENSE)
This open source Python library provides several solvers for optimization
problems related to Optimal Transport for signal, image processing and machine
learning.
Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/)
Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT)
POT provides the following generic OT solvers (links to examples):
* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] .
* [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7].
* Entropic regularization OT solver with [Sinkhorn Knopp
Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] ,
stabilized version [9] [10] [34], lazy CPU/GPU solver from geomloss [60] [61], greedy Sinkhorn [22] and [Screening
Sinkhorn [26]
](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html).
* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21] and unmixing [4].
* Sinkhorn divergence [23] and entropic regularization OT from empirical data.
* Debiased Sinkhorn barycenters [Sinkhorn divergence barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_debiased_barycenter.html) [37]
* [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17].
* Weak OT solver between empirical distributions [39]
* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) with LP solver (only small scale).
* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12,51]), differentiable using gradients from Graph Dictionary Learning [38]
* [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) (exact [24] and regularized [12,51]).
* [Stochastic
solver](https://pythonot.github.io/auto_examples/others/plot_stochastic.html) and
[differentiable losses](https://pythonot.github.io/auto_examples/backends/plot_stoch_continuous_ot_pytorch.html) for
Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19])
* [Sampled solver of Gromov Wasserstein](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) for large-scale problem with any loss functions [33]
* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20].
* [One dimensional Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25]. Also [exact unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_unbalanced_ot.html) with KL and quadratic regularization and the [regularization path of UOT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_regpath.html) [41]
* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3]
formulations).
* [Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance.html) [31, 32] and Max-sliced Wasserstein [35] that can be used for gradient flows [36].
* [Wasserstein distance on the circle](https://pythonot.github.io/auto_examples/plot_compute_wasserstein_circle.html) [44, 45]
* [Spherical Sliced Wasserstein](https://pythonot.github.io/auto_examples/sliced-wasserstein/plot_variance_ssw.html) [46]
* [Graph Dictionary Learning solvers](https://pythonot.github.io/auto_examples/gromov/plot_gromov_wasserstein_dictionary_learning.html) [38].
* [Semi-relaxed (Fused) Gromov-Wasserstein divergences](https://pythonot.github.io/auto_examples/gromov/plot_semirelaxed_fgw.html) with corresponding barycenter solvers (exact and regularized [48]).
* [Quantized (Fused) Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_quantized_gromov_wasserstein.html) [68].
* [Efficient Discrete Multi Marginal Optimal Transport Regularization](https://pythonot.github.io/auto_examples/others/plot_demd_gradient_minimize.html) [50].
* [Several backends](https://pythonot.github.io/quickstart.html#solving-ot-with-multiple-backends) for easy use of POT with [Pytorch](https://pytorch.org/)/[jax](https://github.com/google/jax)/[Numpy](https://numpy.org/)/[Cupy](https://cupy.dev/)/[Tensorflow](https://www.tensorflow.org/) arrays.
* [Smooth Strongly Convex Nearest Brenier Potentials](https://pythonot.github.io/auto_examples/others/plot_SSNB.html#sphx-glr-auto-examples-others-plot-ssnb-py) [58], with an extension to bounding potentials using [59].
* Gaussian Mixture Model OT [69]
POT provides the following Machine Learning related solvers:
* [Optimal transport for domain
adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html)
with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi
supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html).
* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html) [8].
* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt).
* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27].
* [Graph Neural Network OT layers TFGW](https://pythonot.github.io/auto_examples/gromov/plot_gnn_TFGW.html) [52] and TW (OT-GNN) [53]
Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html).
#### Using and citing the toolbox
If you use this toolbox in your research and find it useful, please cite POT
using the following reference from our [JMLR paper](https://jmlr.org/papers/v22/20-451.html):
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:
```bibtex
@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}
}
```
## Installation
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)
#### Pip installation
You can install the toolbox through PyPI with:
```console
pip install POT
```
or get the very latest version by running:
```console
pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root)
```
Optional dependencies may be installed with
```console
pip install POT[all]
```
Note that this installs `cvxopt`, which is licensed under GPL 3.0. Alternatively, if you cannot use GPL-licensed software, the specific optional dependencies may be installed individually, or per-submodule. The available optional installations are `backend-jax, backend-tf, backend-torch, cvxopt, dr, gnn, all`.
#### Anaconda installation with conda-forge
If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies:
```console
conda install -c conda-forge pot
```
#### Post installation check
After a correct installation, you should be able to import the module without errors:
```python
import ot
```
Note that for easier access the module is named `ot` instead of `pot`.
### Dependencies
Some sub-modules require additional dependencies which are discussed below
* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with:
```shell
pip install pymanopt autograd
```
## Examples
### Short examples
* Import the toolbox
```python
import ot
```
* Compute Wasserstein distances
```python
# 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
```python
# 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
```python
# 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 with examples and output is available on [https://PythonOT.github.io/](https://PythonOT.github.io/).
## Acknowledgements
This toolbox has been created by
* [Rémi Flamary](https://remi.flamary.com/)
* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)
It is currently maintained by
* [Rémi Flamary](https://remi.flamary.com/)
* [Cédric Vincent-Cuaz](https://cedricvincentcuaz.github.io/)
The numerous contributors to this library are listed [here](CONTRIBUTORS.md).
POT has benefited from the financing or manpower from the following partners:
## Contributions and code of conduct
Every contribution is welcome and should respect the [contribution guidelines](https://pythonot.github.io/master/contributing.html). Each member of the project is expected to follow the [code of conduct](https://pythonot.github.io/master/code_of_conduct.html).
## Support
You can ask questions and join the development discussion:
* On the POT [slack channel](https://pot-toolbox.slack.com)
* On the POT [gitter channel](https://gitter.im/PythonOT/community)
* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/)
You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](.github/CONTRIBUTING.md) first.
## References
[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.
[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). 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](https://arxiv.org/pdf/1412.5154.pdf). 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](https://hal.archives-ouvertes.fr/hal-01377236/document), Workshop 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](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
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[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567.
[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS).
[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519.
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[13] Mémoli, Facundo (2011). [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 : 417-487.
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[29] Chapel, L., Alaya, M., Gasso, G. (2020). [Partial Optimal Transport with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), Advances in Neural Information Processing Systems (NeurIPS), 2020.
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[38] C. Vincent-Cuaz, T. Vayer, R. Flamary, M. Corneli, N. Courty, [Online Graph
Dictionary Learning](https://arxiv.org/pdf/2102.06555.pdf), International Conference on Machine Learning (ICML), 2021.
[39] Gozlan, N., Roberto, C., Samson, P. M., & Tetali, P. (2017). [Kantorovich duality for general transport costs and applications](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.712.1825&rep=rep1&type=pdf). Journal of Functional Analysis, 273(11), 3327-3405.
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[41] Chapel*, L., Flamary*, R., Wu, H., Févotte, C., Gasso, G. (2021). [Unbalanced Optimal Transport through Non-negative Penalized Linear Regression](https://proceedings.neurips.cc/paper/2021/file/c3c617a9b80b3ae1ebd868b0017cc349-Paper.pdf) Advances in Neural Information Processing Systems (NeurIPS), 2020. (Two first co-authors)
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