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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 deleted file mode 100644 index 2ed7819..0000000 --- a/docs/source/auto_examples/plot_otda_semi_supervised.rst +++ /dev/null @@ -1,245 +0,0 @@ - - -.. _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>`_ |