.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/domain-adaptation/plot_otda_laplacian.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_domain-adaptation_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. .. GENERATED FROM PYTHON SOURCE LINES 11-19 .. code-block:: Python # Authors: Ievgen Redko # License: MIT License import matplotlib.pylab as pl import ot .. GENERATED FROM PYTHON SOURCE LINES 20-22 Generate data ------------- .. GENERATED FROM PYTHON SOURCE LINES 22-30 .. code-block:: Python 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) .. GENERATED FROM PYTHON SOURCE LINES 31-33 Instantiate the different transport algorithms and fit them ----------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 33-51 .. 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=.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) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/circleci/project/ot/bregman/_sinkhorn.py:498: RuntimeWarning: divide by zero encountered in divide v = b / KtransposeU /home/circleci/project/ot/bregman/_sinkhorn.py:506: UserWarning: Warning: numerical errors at iteration 0 warnings.warn('Warning: numerical errors at iteration %d' % ii) .. GENERATED FROM PYTHON SOURCE LINES 52-54 Fig 1 : plots source and target samples --------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 54-72 .. code-block:: Python 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_laplacian_001.png :alt: Source samples, Target samples :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_laplacian_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 73-75 Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 75-128 .. code-block:: Python 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_laplacian_002.png :alt: Optimal coupling EMDTransport, Optimal coupling SinkhornTransport, Optimal coupling EMDLaplaceTransport, Transported samples EmdTransport, Transported samples SinkhornTransport, Transported samples EMDLaplaceTransport :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_laplacian_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.217 seconds) .. _sphx_glr_download_auto_examples_domain-adaptation_plot_otda_laplacian.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_laplacian.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_laplacian.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_