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diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst new file mode 100644 index 0000000..088da31 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -0,0 +1,231 @@ + + +.. _sphx_glr_auto_examples_plot_otda_mapping.py: + + +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8] + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + + +.. code-block:: python + + + # Authors: Remi Flamary <remi.flamary@unice.fr> + # Stanislas Chambon <stan.chambon@gmail.com> + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + + + + + +generate data +############################################################################# + + + +.. code-block:: python + + + n_source_samples = 100 + n_target_samples = 100 + theta = 2 * np.pi / 20 + noise_level = 0.1 + + Xs, ys = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) + Xs_new, _ = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) + Xt, yt = ot.datasets.get_data_classif( + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) + + # one of the target mode changes its variance (no linear mapping) + Xt[yt == 2] *= 3 + Xt = Xt + 4 + + + + + + + + +Instantiate the different transport algorithms and fit them +############################################################################# + + + +.. code-block:: python + + + # MappingTransport with linear kernel + ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + + ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + + # for original source samples, transform applies barycentric mapping + transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + + # for out of source samples, transform applies the linear mapping + transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + + # MappingTransport with gaussian kernel + ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) + ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + + # for original source samples, transform applies barycentric mapping + transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + + # for out of source samples, transform applies the gaussian mapping + transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|4.273804e+03|0.000000e+00 + 1|4.264510e+03|-2.174580e-03 + 2|4.264209e+03|-7.047095e-05 + 3|4.264078e+03|-3.069822e-05 + 4|4.264018e+03|-1.412924e-05 + 5|4.263961e+03|-1.341165e-05 + 6|4.263946e+03|-3.586522e-06 + It. |Loss |Delta loss + -------------------------------- + 0|4.294523e+02|0.000000e+00 + 1|4.247737e+02|-1.089443e-02 + 2|4.245516e+02|-5.228765e-04 + 3|4.244430e+02|-2.557417e-04 + 4|4.243724e+02|-1.663904e-04 + 5|4.243196e+02|-1.244111e-04 + 6|4.242808e+02|-9.132500e-05 + 7|4.242497e+02|-7.331710e-05 + 8|4.242271e+02|-5.326612e-05 + 9|4.242063e+02|-4.916026e-05 + 10|4.241906e+02|-3.699617e-05 + + +plot data +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, (10, 5)) + pl.clf() + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.legend(loc=0) + pl.title('Source and target distributions') + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png + :align: center + + + + +plot transported samples +############################################################################# + + + +.. code-block:: python + + + pl.figure(2) + pl.clf() + pl.subplot(2, 2, 1) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', + label='Mapped source samples') + pl.title("Bary. mapping (linear)") + pl.legend(loc=0) + + pl.subplot(2, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], + c=ys, marker='+', label='Learned mapping') + pl.title("Estim. mapping (linear)") + + pl.subplot(2, 2, 3) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, + marker='+', label='barycentric mapping') + pl.title("Bary. mapping (kernel)") + + pl.subplot(2, 2, 4) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') + pl.title("Estim. mapping (kernel)") + pl.tight_layout() + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_003.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.853 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_mapping.py <plot_otda_mapping.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_mapping.ipynb <plot_otda_mapping.ipynb>` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ |