POT: Python Optimal Transport
=============================

|PyPI version| |Build Status| |Documentation Status|

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.

Installation
------------

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:

.. code:: python

    import ot

Note that for easier access the module is name ot instead of pot.

Dependencies
~~~~~~~~~~~~

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.

Examples
--------

The examples folder contain several examples and use case for the
library. The full documentation is available on
`Readthedocs <http://pot.readthedocs.io/>`__

Here is a list of the Python notebooks if you want a quick look:

-  `1D optimal
   transport <https://github.com/rflamary/POT/blob/master/examples/Demo_1D_OT.ipynb>`__
-  `2D optimal transport on empirical
   distributions <https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb>`__
-  `1D Wasserstein
   barycenter <https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb>`__
-  `OT with user provided
   regularization <https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb>`__
-  `Domain adaptation with optimal
   transport <https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_DomainAdaptation.ipynb>`__
-  `Color transfer in
   images <https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb>`__
-  `OT mapping estimation for domain
   adaptation <https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb>`__
-  `OT mapping estimation for color transfer in
   images <https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation_mapping.ipynb>`__

You can also see the notebooks with `Jupyter
nbviewer <https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/>`__.

Acknowledgements
----------------

The contributors to this library are:

-  `Rémi Flamary <http://remi.flamary.com/>`__
-  `Nicolas Courty <http://people.irisa.fr/Nicolas.Courty/>`__
-  `Laetitia Chapel <http://people.irisa.fr/Laetitia.Chapel/>`__
-  `Michael Perrot <http://perso.univ-st-etienne.fr/pem82055/>`__
   (Mapping estimation)
-  `Léo Gautheron <https://github.com/aje>`__ (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):

-  `Gabriel Peyré <http://gpeyre.github.io/>`__ (Wasserstein Barycenters
   in Matlab)
-  `Nicolas Bonneel <http://liris.cnrs.fr/~nbonneel/>`__ ( C++ code for
   EMD)
-  `Antoine Rolet <https://arolet.github.io/>`__ ( Mex file for EMD )
-  `Marco Cuturi <http://marcocuturi.net/>`__ (Sinkhorn Knopp in
   Matlab/Cuda)

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>`__,
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 <https://arxiv.org/pdf/1507.00504.pdf>`__, 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 <https://arxiv.org/pdf/1307.5551.pdf>`__. 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 <https://arxiv.org/pdf/1510.06567.pdf>`__. arXiv preprint
arXiv:1510.06567.

[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, `Mapping estimation
for discrete optimal
transport <http://remi.flamary.com/biblio/perrot2016mapping.pdf>`__,
Neural Information Processing Systems (NIPS), 2016.

[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.

[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016).
`Scaling algorithms for unbalanced transport
problems <https://arxiv.org/pdf/1607.05816.pdf>`__. arXiv preprint
arXiv:1607.05816.

[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).
`Wasserstein Discriminant
Analysis <https://arxiv.org/pdf/1608.08063.pdf>`__. arXiv preprint
arXiv:1608.08063.

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