.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_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. .. code-block:: default # Authors: Remi Flamary # Stanislas Chambon # # 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=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 Out: .. code-block:: none It. |Loss |Relative loss|Absolute loss ------------------------------------------------ 0|9.484039e+00|0.000000e+00|0.000000e+00 1|1.976107e+00|3.799355e+00|7.507932e+00 2|1.749871e+00|1.292876e-01|2.262365e-01 3|1.692667e+00|3.379504e-02|5.720374e-02 4|1.676256e+00|9.790077e-03|1.641068e-02 5|1.667458e+00|5.276422e-03|8.798212e-03 6|1.661775e+00|3.419693e-03|5.682762e-03 7|1.658009e+00|2.271789e-03|3.766646e-03 8|1.655167e+00|1.716870e-03|2.841707e-03 9|1.651825e+00|2.023380e-03|3.342270e-03 10|1.649431e+00|1.451076e-03|2.393450e-03 11|1.648649e+00|4.742894e-04|7.819369e-04 12|1.647901e+00|4.538219e-04|7.478538e-04 13|1.647356e+00|3.313134e-04|5.457909e-04 14|1.646923e+00|2.627246e-04|4.326871e-04 15|1.646038e+00|5.375014e-04|8.847478e-04 16|1.645629e+00|2.483240e-04|4.086492e-04 17|1.645616e+00|8.248172e-06|1.357332e-05 18|1.645377e+00|1.452648e-04|2.390153e-04 19|1.644745e+00|3.838976e-04|6.314139e-04 It. |Loss |Relative loss|Absolute loss ------------------------------------------------ 20|1.644164e+00|3.538439e-04|5.817773e-04 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_classes_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, 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:: /auto_examples/images/sphx_glr_plot_otda_classes_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none /home/circleci/project/examples/plot_otda_classes.py:149: 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 1.137 seconds) .. _sphx_glr_download_auto_examples_plot_otda_classes.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_classes.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_classes.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_