diff options
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 35 |
1 files changed, 31 insertions, 4 deletions
@@ -15,7 +15,8 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] and greedy SInkhorn [22] with optional GPU implementation (requires cupy). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. @@ -26,6 +27,8 @@ It provides the following solvers: * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) * Non regularized free support Wasserstein barycenters [20]. +* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +* Screening Sinkhorn Algorithm for OT [26]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -43,7 +46,7 @@ year={2017} ## Installation -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: +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 (>=1.11) - Scipy (>=1.0) @@ -52,6 +55,12 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ comp #### Pip installation +Note that due to a limitation of pip, `cython` and `numpy` need to be installed +prior to installing POT. This can be done easily with +``` +pip install numpy cython +``` + You can install the toolbox through PyPI with: ``` pip install POT @@ -61,6 +70,8 @@ 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](https://conda-forge.org). To install it and the required dependencies: @@ -142,17 +153,21 @@ Here is a list of the Python notebooks available [here](https://github.com/rflam * [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) * [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) * [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) - +* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). ## Acknowledgements -The contributors to this library are: +This toolbox has been created and is maintained by * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) + +The contributors to this library are + * [Alexandre Gramfort](http://alexandre.gramfort.net/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) @@ -163,6 +178,10 @@ The contributors to this library are: * Erwan Vautier (Gromov-Wasserstein) * [Kilian Fatras](https://kilianfatras.github.io/) * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) +* [Vayer Titouan](https://tvayer.github.io/) +* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) +* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) +* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) 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): @@ -230,3 +249,11 @@ You can also post bug reports and feature requests in Github issues. Make sure t [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. [22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 + +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). + +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). + +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). |