.. 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_classes.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_classes.py: ======================== OT for domain adaptation ======================== This example introduces a domain adaptation in a 2D setting and the 4 OTDA approaches currently supported in POT. .. GENERATED FROM PYTHON SOURCE LINES 11-20 .. code-block:: Python # Authors: Remi Flamary # Stanislas Chambon # # License: MIT License import matplotlib.pylab as pl import ot .. GENERATED FROM PYTHON SOURCE LINES 21-23 Generate data ------------- .. GENERATED FROM PYTHON SOURCE LINES 23-31 .. 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 32-34 Instantiate the different transport algorithms and fit them ----------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 34-59 .. 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=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) # Sinkhorn Transport with Group lasso regularization ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) # Sinkhorn Transport with Group lasso regularization l1l2 ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, verbose=True) ot_l1l2.fit(Xs=Xs, ys=ys, 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_lpl1 = ot_lpl1.transform(Xs=Xs) transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/circleci/project/ot/bregman/_sinkhorn.py:531: UserWarning: Sinkhorn did not converge. You might want to increase the number of iterations `numItermax` or the regularization parameter `reg`. warnings.warn("Sinkhorn did not converge. You might want to " It. |Loss |Relative loss|Absolute loss ------------------------------------------------ 0|9.763061e+00|0.000000e+00|0.000000e+00 1|2.081861e+00|3.689583e+00|7.681200e+00 2|1.862280e+00|1.179100e-01|2.195813e-01 3|1.821987e+00|2.211501e-02|4.029326e-02 4|1.808932e+00|7.216608e-03|1.305436e-02 5|1.792762e+00|9.019666e-03|1.617012e-02 6|1.785968e+00|3.804295e-03|6.794348e-03 7|1.778259e+00|4.335304e-03|7.709292e-03 8|1.772608e+00|3.187777e-03|5.650678e-03 9|1.768734e+00|2.190456e-03|3.874332e-03 10|1.768700e+00|1.876119e-05|3.318292e-05 11|1.767482e+00|6.894485e-04|1.218588e-03 12|1.765491e+00|1.127725e-03|1.990989e-03 13|1.762434e+00|1.734384e-03|3.056738e-03 14|1.759833e+00|1.478250e-03|2.601472e-03 15|1.758374e+00|8.294698e-04|1.458518e-03 16|1.757601e+00|4.396351e-04|7.727032e-04 17|1.756624e+00|5.562652e-04|9.771489e-04 18|1.754377e+00|1.281229e-03|2.247758e-03 19|1.753747e+00|3.587988e-04|6.292424e-04 It. |Loss |Relative loss|Absolute loss ------------------------------------------------ 20|1.753162e+00|3.336538e-04|5.849492e-04 .. GENERATED FROM PYTHON SOURCE LINES 60-62 Fig 1 : plots source and target samples --------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 62-80 .. 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_classes_001.png :alt: Source samples, Target samples :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_classes_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 81-83 Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 83-150 .. code-block:: Python param_img = {'interpolation': 'nearest'} pl.figure(2, figsize=(15, 8)) pl.subplot(2, 4, 1) pl.imshow(ot_emd.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nEMDTransport') pl.subplot(2, 4, 2) pl.imshow(ot_sinkhorn.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nSinkhornTransport') pl.subplot(2, 4, 3) pl.imshow(ot_lpl1.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nSinkhornLpl1Transport') pl.subplot(2, 4, 4) pl.imshow(ot_l1l2.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nSinkhornL1l2Transport') pl.subplot(2, 4, 5) 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, 4, 6) 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, 4, 7) pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples', alpha=0.3) pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, marker='+', label='Transp samples', s=30) pl.xticks([]) pl.yticks([]) pl.title('Transported samples\nSinkhornLpl1Transport') pl.subplot(2, 4, 8) pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples', alpha=0.3) pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, marker='+', label='Transp samples', s=30) pl.xticks([]) pl.yticks([]) pl.title('Transported samples\nSinkhornL1l2Transport') pl.tight_layout() pl.show() .. image-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_classes_002.png :alt: Optimal coupling EMDTransport, Optimal coupling SinkhornTransport, Optimal coupling SinkhornLpl1Transport, Optimal coupling SinkhornL1l2Transport, Transported samples EmdTransport, Transported samples SinkhornTransport, Transported samples SinkhornLpl1Transport, Transported samples SinkhornL1l2Transport :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_classes_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.275 seconds) .. _sphx_glr_download_auto_examples_domain-adaptation_plot_otda_classes.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_classes.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_classes.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_