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diff --git a/docs/source/auto_examples/plot_otda_laplacian.rst b/docs/source/auto_examples/plot_otda_laplacian.rst deleted file mode 100644 index 12cd7b9..0000000 --- a/docs/source/auto_examples/plot_otda_laplacian.rst +++ /dev/null @@ -1,233 +0,0 @@ -.. 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/rflamary/PYTHON/POT/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 1.195 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>`_ |