.. 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 ======================== .. note:: Example added in release: 0.1.9. This example introduces a domain adaptation in a 2D setting and the 4 OTDA approaches currently supported in POT. .. GENERATED FROM PYTHON SOURCE LINES 14-23 .. code-block:: Python # Authors: Remi Flamary # Stanislas Chambon # # License: MIT License import matplotlib.pylab as pl import ot .. GENERATED FROM PYTHON SOURCE LINES 24-26 Generate data ------------- .. GENERATED FROM PYTHON SOURCE LINES 26-34 .. 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 35-37 Instantiate the different transport algorithms and fit them ----------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 37-61 .. 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:902: UserWarning: Sinkhorn did not converge. You might want to increase the number of iterations `numItermax` or the regularization parameter `reg`. warnings.warn( /home/circleci/project/ot/bregman/_sinkhorn.py:666: UserWarning: Sinkhorn did not converge. You might want to increase the number of iterations `numItermax` or the regularization parameter `reg`. warnings.warn( It. |Loss |Relative loss|Absolute loss ------------------------------------------------ 0|1.064341e+01|0.000000e+00|0.000000e+00 1|2.868980e+00|2.709824e+00|7.774431e+00 2|2.690688e+00|6.626282e-02|1.782926e-01 3|2.646585e+00|1.666386e-02|4.410232e-02 4|2.633040e+00|5.144397e-03|1.354540e-02 5|2.628282e+00|1.810087e-03|4.757420e-03 6|2.624381e+00|1.486685e-03|3.901627e-03 7|2.620584e+00|1.448936e-03|3.797059e-03 8|2.616258e+00|1.653263e-03|4.325364e-03 9|2.613513e+00|1.050565e-03|2.745664e-03 10|2.611843e+00|6.391688e-04|1.669409e-03 11|2.609710e+00|8.173051e-04|2.132930e-03 12|2.607492e+00|8.507842e-04|2.218413e-03 13|2.606103e+00|5.331207e-04|1.389367e-03 14|2.605431e+00|2.577170e-04|6.714638e-04 15|2.605062e+00|1.416359e-04|3.689704e-04 16|2.604321e+00|2.846667e-04|7.413634e-04 17|2.603740e+00|2.232437e-04|5.812684e-04 18|2.603369e+00|1.422147e-04|3.702375e-04 19|2.602792e+00|2.218513e-04|5.774328e-04 It. |Loss |Relative loss|Absolute loss ------------------------------------------------ 20|2.602433e+00|1.379022e-04|3.588812e-04 .. GENERATED FROM PYTHON SOURCE LINES 62-64 Fig 1 : plots source and target samples --------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 64-82 .. 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 83-85 Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 85-172 .. code-block:: Python param_img = {"interpolation": "nearest", "cmap": "gray_r"} 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 1.728 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 ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_otda_classes.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_