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diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst new file mode 100644 index 0000000..a5ab285 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -0,0 +1,258 @@ + + +.. _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 <remi.flamary@unice.fr> + # Stanislas Chambon <stan.chambon@gmail.com> + # + # 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|1.003747e+01|0.000000e+00 + 1|1.953263e+00|-4.138821e+00 + 2|1.744456e+00|-1.196969e-01 + 3|1.689268e+00|-3.267022e-02 + 4|1.666355e+00|-1.374998e-02 + 5|1.656125e+00|-6.177356e-03 + 6|1.651753e+00|-2.646960e-03 + 7|1.647261e+00|-2.726957e-03 + 8|1.642274e+00|-3.036672e-03 + 9|1.639926e+00|-1.431818e-03 + 10|1.638750e+00|-7.173837e-04 + 11|1.637558e+00|-7.281753e-04 + 12|1.636248e+00|-8.002067e-04 + 13|1.634555e+00|-1.036074e-03 + 14|1.633547e+00|-6.166646e-04 + 15|1.633531e+00|-1.022614e-05 + 16|1.632957e+00|-3.510986e-04 + 17|1.632853e+00|-6.380944e-05 + 18|1.632704e+00|-9.122988e-05 + 19|1.632237e+00|-2.861276e-04 + It. |Loss |Delta loss + -------------------------------- + 20|1.632174e+00|-3.896483e-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 2.308 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_classes.py <plot_otda_classes.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_classes.ipynb <plot_otda_classes.ipynb>` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ |