.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_otda_laplacian.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_otda_laplacian.py:


======================================================
OT with Laplacian regularization for domain adaptation
======================================================

This example introduces a domain adaptation in a 2D setting and OTDA
approach with Laplacian regularization.


.. code-block:: default


    # Authors: Ievgen Redko <ievgen.redko@univ-st-etienne.fr>

    # License: MIT License

    import matplotlib.pylab as pl
    import ot








Generate data
-------------


.. code-block:: default


    n_source_samples = 150
    n_target_samples = 150

    Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)
    Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)









Instantiate the different transport algorithms and fit them
-----------------------------------------------------------


.. code-block:: default


    # EMD Transport
    ot_emd = ot.da.EMDTransport()
    ot_emd.fit(Xs=Xs, Xt=Xt)

    # Sinkhorn Transport
    ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01)
    ot_sinkhorn.fit(Xs=Xs, Xt=Xt)

    # EMD Transport with Laplacian regularization
    ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)
    ot_emd_laplace.fit(Xs=Xs, Xt=Xt)

    # transport source samples onto target samples
    transp_Xs_emd = ot_emd.transform(Xs=Xs)
    transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)
    transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)








Fig 1 : plots source and target samples
---------------------------------------


.. code-block:: default


    pl.figure(1, figsize=(10, 5))
    pl.subplot(1, 2, 1)
    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
    pl.xticks([])
    pl.yticks([])
    pl.legend(loc=0)
    pl.title('Source  samples')

    pl.subplot(1, 2, 2)
    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
    pl.xticks([])
    pl.yticks([])
    pl.legend(loc=0)
    pl.title('Target samples')
    pl.tight_layout()





.. image:: /auto_examples/images/sphx_glr_plot_otda_laplacian_001.png
    :class: sphx-glr-single-img





Fig 2 : plot optimal couplings and transported samples
------------------------------------------------------


.. code-block:: default


    param_img = {'interpolation': 'nearest'}

    pl.figure(2, figsize=(15, 8))
    pl.subplot(2, 3, 1)
    pl.imshow(ot_emd.coupling_, **param_img)
    pl.xticks([])
    pl.yticks([])
    pl.title('Optimal coupling\nEMDTransport')

    pl.figure(2, figsize=(15, 8))
    pl.subplot(2, 3, 2)
    pl.imshow(ot_sinkhorn.coupling_, **param_img)
    pl.xticks([])
    pl.yticks([])
    pl.title('Optimal coupling\nSinkhornTransport')

    pl.subplot(2, 3, 3)
    pl.imshow(ot_emd_laplace.coupling_, **param_img)
    pl.xticks([])
    pl.yticks([])
    pl.title('Optimal coupling\nEMDLaplaceTransport')

    pl.subplot(2, 3, 4)
    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
               label='Target samples', alpha=0.3)
    pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,
               marker='+', label='Transp samples', s=30)
    pl.xticks([])
    pl.yticks([])
    pl.title('Transported samples\nEmdTransport')
    pl.legend(loc="lower left")

    pl.subplot(2, 3, 5)
    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
               label='Target samples', alpha=0.3)
    pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
               marker='+', label='Transp samples', s=30)
    pl.xticks([])
    pl.yticks([])
    pl.title('Transported samples\nSinkhornTransport')

    pl.subplot(2, 3, 6)
    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
               label='Target samples', alpha=0.3)
    pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,
               marker='+', label='Transp samples', s=30)
    pl.xticks([])
    pl.yticks([])
    pl.title('Transported samples\nEMDLaplaceTransport')
    pl.tight_layout()

    pl.show()



.. image:: /auto_examples/images/sphx_glr_plot_otda_laplacian_002.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/circleci/project/examples/plot_otda_laplacian.py:127: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
      pl.show()





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.994 seconds)


.. _sphx_glr_download_auto_examples_plot_otda_laplacian.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_otda_laplacian.py <plot_otda_laplacian.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_otda_laplacian.ipynb <plot_otda_laplacian.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_