path: root/docs/source/quickstart.rst

Quick start guide
=================

In the following we provide some pointers about which functions and classes 
to use for different problems related to optimal transport (OT).

This document is not a tutorial on numerical optimal transport. For this we strongly
recommend to read the very nice book [15]_ . 


Optimal transport and Wasserstein distance
------------------------------------------

.. note::
    In POT, most functions that solve OT or regularized OT problems have two
    versions that return the OT matrix or the value of the optimal solution. For
    instance :any:`ot.emd` return the OT matrix and :any:`ot.emd2` return the
    Wassertsein distance.

Solving optimal transport
^^^^^^^^^^^^^^^^^^^^^^^^^

The optimal transport problem between discrete distributions is often expressed
as

.. math::
    \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j}

    s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0

where :

- :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`.
- :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions.

Solving the linear program above can be done using the function :any:`ot.emd`
that will return the optimal transport matrix :math:`\gamma^*`:

.. code:: 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

The method used for solving the OT problem is the network simplex, it is
implemented in C from  [1]_. It has a complexity of :math:`O(n^3)` but the
solver is quite efficient and uses sparsity of the solution.

.. hint::
    Examples of use for :any:`ot.emd` are available in the following examples:

    - :any:`auto_examples/plot_OT_2D_samples`
    - :any:`auto_examples/plot_OT_1D` 
    - :any:`auto_examples/plot_OT_L1_vs_L2` 

Computing Wasserstein distance
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The value of the OT solution is often more of interest that the OT matrix :

    .. math::
        OT(a,b)=\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j}

        s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0


It can computed from an already estimated OT matrix with
:code:`np.sum(T*M)` or directly with the function :any:`ot.emd2`.

.. code:: python

    # a,b are 1D histograms (sum to 1 and positive)
    # M is the ground cost matrix
    W=ot.emd2(a,b,M) # Wasserstein distance / EMD value

Note that the well known  `Wasserstein distance
<https://en.wikipedia.org/wiki/Wasserstein_metric>`_ between distributions a and
b is defined as


    .. math::

        W_p(a,b)=(\min_\gamma \sum_{i,j}\gamma_{i,j}\|x_i-y_j\|_p)^\frac{1}{p}

        s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0

This means that if you want to compute the :math:`W_2` you need to compute the
square root of :any:`ot.emd2` when providing
:code:`M=ot.dist(xs,xt)` that use the squared euclidean distance by default. Computing
the :math:`W_1` wasserstein distance can be done directly with  :any:`ot.emd2`
when providing :code:`M=ot.dist(xs,xt, metric='euclidean')` to use the euclidean
distance.

 

.. hint::
    Examples of use for :any:`ot.emd2` are available in the following examples:

    - :any:`auto_examples/plot_compute_emd`
 

Special cases
^^^^^^^^^^^^^

Note that the OT problem and the corresponding Wasserstein distance can in some
special cases be computed very efficiently. 

For instance when the samples are in 1D, then the OT problem can be solved in
:math:`O(n\log(n))` by using a simple sorting. In this case we provide the
function :any:`ot.emd_1d` and   :any:`ot.emd2_1d` to return respectively the OT
matrix and value. Note that since the solution is very sparse the :code:`sparse`
parameter of :any:`ot.emd_1d` allows for solving and returning the solution for
very large problems. Note that in order to computed directly the :math:`W_p`
Wasserstein distance in 1D we provide the function :any:`ot.wasserstein_1d` that
takes :code:`p` as a parameter. 

Another specials for estimating OT and Monge mapping is between Gaussian
distributions. In this case there exists a close form solution given in Remark
2.29 in [15]_ and the Monge mapping is an affine function and can be
also computed from the covariances and means of the source and target
distributions. In this case when the finite sample dataset is supposed gaussian, we provide 
:any:`ot.da.OT_mapping_linear` that returns the parameters for the Monge
mapping.


Regularized Optimal Transport
-----------------------------

Recent developments have shown the interest of regularized OT both in terms of
computational and statistical properties.

We address in this section the regularized OT problem that can be expressed as

.. math::
    \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + \lambda\Omega(\gamma)

        s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0


where :

- :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`.
- :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions.
- :math:`\Omega` is the regularization term.

We discuss in the following specific algorithms that can be used depending on
the regularization term.



Entropic regularized OT
^^^^^^^^^^^^^^^^^^^^^^^

This is the most common regularization used for optimal transport. It has been
proposed in the ML community by Marco Cuturi in his seminal paper [2]_. This
regularization has the following expression

.. math::
    \Omega(\gamma)=\sum_{i,j}\gamma_{i,j}\log(\gamma_{i,j})


The use of the regularization term above in the optimization problem has a very
strong impact. First it makes the problem smooth which leads to new optimization
procedures such as L-BFGS (see :any:`ot.smooth` ). Next it makes the problem
strictly convex meaning that there will be a unique solution. Finally the
solution of the resulting optimization problem can be expressed as:

.. math::

    \gamma_\lambda^*=\text{diag}(u)K\text{diag}(v)

where :math:`u` and :math:`v` are vectors and :math:`K=\exp(-M/\lambda)` where
the :math:`\exp` is taken component-wise. In order to solve the optimization
problem, on can use an alternative projection algorithm that can be very
efficient for large values if regularization. 

The main function is POT are  :any:`ot.sinkhorn` and
:any:`ot.sinkhorn2` that return respectively the OT matrix and the value of the
linear term. Note that the regularization parameter :math:`\lambda` in the
equation above is given to those function with the parameter :code:`reg`.

    >>> import ot
    >>> a=[.5,.5]
    >>> b=[.5,.5]
    >>> M=[[0.,1.],[1.,0.]]
    >>> ot.sinkhorn(a,b,M,1)
    array([[ 0.36552929,  0.13447071],
        [ 0.13447071,  0.36552929]])



More details about the algorithm used is given in the following note.


.. note::
    The main function to solve entropic regularized OT is :any:`ot.sinkhorn`.
    This function is a wrapper and the parameter :code:`method` help you select
    the actual algorithm used to solve the problem:

    + :code:`method='sinkhorn'` calls :any:`ot.bregman.sinkhorn_knopp`  the
      classic algorithm [2]_.
    + :code:`method='sinkhorn_stabilized'` calls :any:`ot.bregman.sinkhorn_stabilized`  the
      log stabilized version of the algorithm [9]_.    
    + :code:`method='sinkhorn_epsilon_scaling'` calls
      :any:`ot.bregman.sinkhorn_epsilon_scaling`  the epsilon scaling version
      of the algorithm [9]_.   
    + :code:`method='greenkhorn'` calls :any:`ot.bregman.greenkhorn`  the
      greedy sinkhorn verison of the algorithm [22]_.   

    In addition to all those variants of sinkhorn, we have another
    implementation solving the problem in the smooth dual or semi-dual in
    :any:`ot.smooth`. This solver uses the :any:`scipy.optimize.minimize`
    function to solve the smooth problem with :code:`L-BFGS` algorithm. Tu use
    this solver, use functions :any:`ot.smooth.smooth_ot_dual` or
    :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to
    choose entropic/Kullbach Leibler regularization.

.. hint::
    Examples of use for :any:`ot.sinkhorn` are available in the following examples:

    - :any:`auto_examples/plot_OT_2D_samples`
    - :any:`auto_examples/plot_OT_1D` 
    - :any:`auto_examples/plot_OT_1D_smooth`
    - :any:`auto_examples/plot_stochastic`


Recently [23]_ introduced the sinkhorn divergence that build from entropic
regularization to compute fast and differentiable geometric diveregnce between
empirical distributions.  



Finally note that we also provide in :any:`ot.stochastic` several implementation
of stochastic solvers for entropic regularized OT [18]_ [19]_.  

Other regularization
^^^^^^^^^^^^^^^^^^^^

While entropic OT is the most common and favored in practice, there exist other
kind of regularization. We provide in POT two specific solvers for other
regularization terms: namely quadratic regularization and group lasso
regularization. But we also provide in :any:`ot.optim`  two generic solvers that allows solving any
smooth regularization in practice. 

The first general regularization term we can solve is the quadratic
regularization of the form 

.. math::
    \Omega(\gamma)=\sum_{i,j} \gamma_{i,j}^2

this regularization term has a similar effect to entropic regularization in
densifying the OT matrix but it keeps some sort of sparsity that is lost with
entropic regularization as soon as :math:`\lambda>0` [17]_. This problem cen be
solved with POT using solvers from :any:`ot.smooth`, more specifically
functions :any:`ot.smooth.smooth_ot_dual` or
:any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='l2'` to 
choose the quadratic regularization.

Another regularization that has been used in recent years is the group lasso
regularization

.. math::
    \Omega(\gamma)=\sum_{j,G\in\mathcal{G}} \|\gamma_{G,j}\|_q^p

where :math:`\mathcal{G}` contains non overlapping groups of lines in the OT
matrix. This regularization proposed in [5]_ will promote sparsity at the group level and for
instance will force target samples to get mass from a small number of groups.
Note that the exact OT solution is already sparse so this regularization does
not make sens if it is not combined with others such as entropic. Depending on
the choice of :code:`p` and :code:`q`, the problem can be solved with different
approaches.  When :code:`q=1` and :code:`p<1` the problem is non convex but can
be solved using an efficient majoration minimization approach  with
:any:`ot.sinkhorn_lpl1_mm`. When :code:`q=2` and :code:`p=1` we recover the
convex gourp lasso and we provide a solver using generalized conditional
gradient algorithm [7]_ in function
:any:`ot.da.sinkhorn_l1l2_gl`.



Wasserstein Barycenters
-----------------------




Monge mapping and Domain adaptation
-----------------------------------


Other applications
------------------

Wasserstein Discriminant Analysis
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^


Gromov-Wasserstein
^^^^^^^^^^^^^^^^^^


GPU acceleration
----------------

We provide several implementation of our OT solvers in :any:`ot.gpu`. Those
implementation use the :code:`cupy` toolbox.   



FAQ
---



1. **How to solve a discrete optimal transport problem ?**

    The solver for discrete  is the function :py:mod:`ot.emd` that returns
    the OT transport matrix. If you want to solve a regularized OT you can 
    use :py:mod:`ot.sinkhorn`.


    Here is a simple use case:

    .. code:: 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

    More detailed examples can be seen on this
    :doc:`auto_examples/plot_OT_2D_samples`
    

2. **pip install POT fails with error : ImportError: No module named Cython.Build**

    As discussed shortly in the README file. POT requires to have :code:`numpy`
    and :code:`cython` installed to build. This corner case is not yet handled
    by :code:`pip` and for now you need to install both library prior to
    installing POT.

    Note that this problem do not occur when using conda-forge since the packages
    there are pre-compiled. 

    See `Issue #59 <https://github.com/rflamary/POT/issues/59>`__ for more
    details.

3. **Why is Sinkhorn slower than EMD ?**

    This might come from the choice of the regularization term. The speed of
    convergence of sinkhorn depends directly on this term [22]_ and when the
    regularization gets very small the problem try and approximate the exact OT
    which leads to slow convergence in addition to numerical problems. In other
    words, for large regularization sinkhorn will be very fast to converge, for
    small regularization (when you need an OT matrix close to the true OT), it
    might be quicker to use the EMD solver.

    Also note that the numpy implementation of the sinkhorn can use parallel
    computation depending on the configuration of your system but very important
    speedup can be obtained by using a GPU implementation since all operations
    are matrix/vector products.

4. **Using GPU fails with error:  module 'ot' has no attribute 'gpu'**

    In order to limit import time and hard dependencies in POT. we do not import
    some sub-modules automatically with :code:`import ot`. In order to use the
    acceleration in :any:`ot.gpu` you need first to import is with
    :code:`import ot.gpu`.  

    See `Issue #85 <https://github.com/rflamary/POT/issues/85>`__ and :any:`ot.gpu`
    for more details.


References
----------

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