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diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst new file mode 100644 index 0000000..2ed7819 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_semi_supervised.rst @@ -0,0 +1,245 @@ + + +.. _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.make_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.make_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 0.256 seconds) + + + +.. only :: html + + .. 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>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |