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+
+
+.. _sphx_glr_auto_examples_plot_otda_semi_supervised.py:
+
+
+============================================
+OTDA unsupervised vs semi-supervised setting
+============================================
+
+This example introduces a semi supervised domain adaptation in a 2D setting.
+It explicits the problem of semi supervised domain adaptation and introduces
+some optimal transport approaches to solve it.
+
+Quantities such as optimal couplings, greater coupling coefficients and
+transported samples are represented in order to give a visual understanding
+of what the transport methods are doing.
+
+
+
+.. code-block:: python
+
+
+    # Authors: Remi Flamary <remi.flamary@unice.fr>
+    #          Stanislas Chambon <stan.chambon@gmail.com>
+    #
+    # License: MIT License
+
+    import matplotlib.pylab as pl
+    import ot
+
+
+
+
+
+
+
+
+Generate data
+-------------
+
+
+
+.. code-block:: python
+
+
+    n_samples_source = 150
+    n_samples_target = 150
+
+    Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)
+    Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)
+
+
+
+
+
+
+
+
+Transport source samples onto target samples
+--------------------------------------------
+
+
+
+.. code-block:: python
+
+
+
+    # unsupervised domain adaptation
+    ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)
+    ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)
+    transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)
+
+    # semi-supervised domain adaptation
+    ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)
+    ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)
+    transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)
+
+    # semi supervised DA uses available labaled target samples to modify the cost
+    # matrix involved in the OT problem. The cost of transporting a source sample
+    # of class A onto a target sample of class B != A is set to infinite, or a
+    # very large value
+
+    # note that in the present case we consider that all the target samples are
+    # labeled. For daily applications, some target sample might not have labels,
+    # in this case the element of yt corresponding to these samples should be
+    # filled with -1.
+
+    # Warning: we recall that -1 cannot be used as a class label
+
+
+
+
+
+
+
+
+Fig 1 : plots source and target samples + matrix of pairwise distance
+---------------------------------------------------------------------
+
+
+
+.. code-block:: python
+
+
+    pl.figure(1, figsize=(10, 10))
+    pl.subplot(2, 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(2, 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.subplot(2, 2, 3)
+    pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Cost matrix - unsupervised DA')
+
+    pl.subplot(2, 2, 4)
+    pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Cost matrix - semisupervised DA')
+
+    pl.tight_layout()
+
+    # the optimal coupling in the semi-supervised DA case will exhibit " shape
+    # similar" to the cost matrix, (block diagonal matrix)
+
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_001.png
+    :align: center
+
+
+
+
+Fig 2 : plots optimal couplings for the different methods
+---------------------------------------------------------
+
+
+
+.. code-block:: python
+
+
+    pl.figure(2, figsize=(8, 4))
+
+    pl.subplot(1, 2, 1)
+    pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Optimal coupling\nUnsupervised DA')
+
+    pl.subplot(1, 2, 2)
+    pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Optimal coupling\nSemi-supervised DA')
+
+    pl.tight_layout()
+
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png
+    :align: center
+
+
+
+
+Fig 3 : plot transported samples
+--------------------------------
+
+
+
+.. code-block:: python
+
+
+    # display transported samples
+    pl.figure(4, figsize=(8, 4))
+    pl.subplot(1, 2, 1)
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+               label='Target samples', alpha=0.5)
+    pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,
+               marker='+', label='Transp samples', s=30)
+    pl.title('Transported samples\nEmdTransport')
+    pl.legend(loc=0)
+    pl.xticks([])
+    pl.yticks([])
+
+    pl.subplot(1, 2, 2)
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+               label='Target samples', alpha=0.5)
+    pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,
+               marker='+', label='Transp samples', s=30)
+    pl.title('Transported samples\nSinkhornTransport')
+    pl.xticks([])
+    pl.yticks([])
+
+    pl.tight_layout()
+    pl.show()
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_006.png
+    :align: center
+
+
+
+
+**Total running time of the script:** ( 0 minutes  0.714 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_otda_semi_supervised.py <plot_otda_semi_supervised.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb <plot_otda_semi_supervised.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_