add files and notebooks

1 files changed, 265 insertions, 0 deletions

diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst
new file mode 100644
index 0000000..20b76ba
--- /dev/null
+++ b/docs/source/auto_examples/plot_otda_d2.rst
@@ -0,0 +1,265 @@
+
+
+.. _sphx_glr_auto_examples_plot_otda_d2.py:
+
+
+==============================
+OT for empirical distributions
+==============================
+
+This example introduces a domain adaptation in a 2D setting. It explicits
+the problem of 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)
+
+    # Cost matrix
+    M = ot.dist(Xs, Xt, metric='sqeuclidean')
+
+
+
+
+
+
+
+
+Instantiate the different transport algorithms and fit them
+#############################################################################
+
+
+
+.. code-block:: python
+
+
+    # EMD Transport
+    ot_emd = ot.da.EMDTransport()
+    ot_emd.fit(Xs=Xs, Xt=Xt)
+
+    # Sinkhorn Transport
+    ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
+    ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
+
+    # Sinkhorn Transport with Group lasso regularization
+    ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)
+    ot_lpl1.fit(Xs=Xs, ys=ys, 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_lpl1 = ot_lpl1.transform(Xs=Xs)
+
+
+
+
+
+
+
+
+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(M, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Matrix of pairwise distances')
+    pl.tight_layout()
+
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_001.png
+    :align: center
+
+
+
+
+Fig 2 : plots optimal couplings for the different methods
+#############################################################################
+
+
+
+.. code-block:: python
+
+
+    pl.figure(2, figsize=(10, 6))
+
+    pl.subplot(2, 3, 1)
+    pl.imshow(ot_emd.coupling_, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Optimal coupling\nEMDTransport')
+
+    pl.subplot(2, 3, 2)
+    pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Optimal coupling\nSinkhornTransport')
+
+    pl.subplot(2, 3, 3)
+    pl.imshow(ot_lpl1.coupling_, interpolation='nearest')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Optimal coupling\nSinkhornLpl1Transport')
+
+    pl.subplot(2, 3, 4)
+    ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])
+    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Main coupling coefficients\nEMDTransport')
+
+    pl.subplot(2, 3, 5)
+    ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])
+    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Main coupling coefficients\nSinkhornTransport')
+
+    pl.subplot(2, 3, 6)
+    ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])
+    pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')
+    pl.xticks([])
+    pl.yticks([])
+    pl.title('Main coupling coefficients\nSinkhornLpl1Transport')
+    pl.tight_layout()
+
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png
+    :align: center
+
+
+
+
+Fig 3 : plot transported samples
+#############################################################################
+
+
+
+.. code-block:: python
+
+
+    # display transported samples
+    pl.figure(4, figsize=(10, 4))
+    pl.subplot(1, 3, 1)
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+               label='Target samples', alpha=0.5)
+    pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 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, 3, 2)
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+               label='Target samples', alpha=0.5)
+    pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,
+               marker='+', label='Transp samples', s=30)
+    pl.title('Transported samples\nSinkhornTransport')
+    pl.xticks([])
+    pl.yticks([])
+
+    pl.subplot(1, 3, 3)
+    pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
+               label='Target samples', alpha=0.5)
+    pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,
+               marker='+', label='Transp samples', s=30)
+    pl.title('Transported samples\nSinkhornLpl1Transport')
+    pl.xticks([])
+    pl.yticks([])
+
+    pl.tight_layout()
+    pl.show()
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_006.png
+    :align: center
+
+
+
+
+**Total running time of the script:** ( 0 minutes  46.009 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_otda_d2.py <plot_otda_d2.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_otda_d2.ipynb <plot_otda_d2.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_