.. _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:: python # Authors: Remi Flamary # Stanislas Chambon # # License: MIT License import matplotlib.pylab as pl import ot generate data ############################################################################# .. code-block:: python n_source_samples = 150 n_target_samples = 150 Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) Instantiate the different transport algorithms and fit them ############################################################################# .. 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 Out:: It. |Loss |Delta loss -------------------------------- 0|9.456043e+00|0.000000e+00 1|2.059035e+00|-3.592463e+00 2|1.839814e+00|-1.191540e-01 3|1.787860e+00|-2.905942e-02 4|1.766582e+00|-1.204485e-02 5|1.760573e+00|-3.413038e-03 6|1.755288e+00|-3.010556e-03 7|1.749124e+00|-3.523968e-03 8|1.744159e+00|-2.846760e-03 9|1.741007e+00|-1.810862e-03 10|1.739839e+00|-6.710130e-04 11|1.737221e+00|-1.507260e-03 12|1.736011e+00|-6.970742e-04 13|1.734948e+00|-6.126425e-04 14|1.733901e+00|-6.038775e-04 15|1.733768e+00|-7.618542e-05 16|1.732821e+00|-5.467723e-04 17|1.732678e+00|-8.226843e-05 18|1.731934e+00|-4.300066e-04 19|1.731850e+00|-4.848002e-05 It. |Loss |Delta loss -------------------------------- 20|1.731699e+00|-8.729590e-05 Fig 1 : plots source and target samples ############################################################################# .. 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:: /auto_examples/images/sphx_glr_plot_otda_classes_001.png :align: center Fig 2 : plot optimal couplings and transported samples ############################################################################# .. code-block:: python param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} 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_003.png :align: center **Total running time of the script:** ( 0 minutes 1.906 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_otda_classes.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_otda_classes.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_