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