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diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst deleted file mode 100644 index 1d95fc6..0000000 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ /dev/null @@ -1,235 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_otda_mapping.py: - - -=========================================== -OT mapping estimation for domain adaptation -=========================================== - -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.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) - Xs_new, _ = ot.datasets.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) - Xt, yt = ot.datasets.make_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 - - - - - - - -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 - - - - -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|4.299275e+03|0.000000e+00 - 1|4.290443e+03|-2.054271e-03 - 2|4.290040e+03|-9.389994e-05 - 3|4.289876e+03|-3.830707e-05 - 4|4.289783e+03|-2.157428e-05 - 5|4.289724e+03|-1.390941e-05 - 6|4.289706e+03|-4.051054e-06 - It. |Loss |Delta loss - -------------------------------- - 0|4.326465e+02|0.000000e+00 - 1|4.282533e+02|-1.015416e-02 - 2|4.279473e+02|-7.145955e-04 - 3|4.277941e+02|-3.580104e-04 - 4|4.277069e+02|-2.039229e-04 - 5|4.276462e+02|-1.418698e-04 - 6|4.276011e+02|-1.054172e-04 - 7|4.275663e+02|-8.145802e-05 - 8|4.275405e+02|-6.028774e-05 - 9|4.275191e+02|-5.005886e-05 - 10|4.275019e+02|-4.021935e-05 - - -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 0.795 seconds) - - - -.. only :: html - - .. 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>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |