.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/domain-adaptation/plot_otda_mapping.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_domain-adaptation_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. .. GENERATED FROM PYTHON SOURCE LINES 15-28 .. code-block:: Python # Authors: Remi Flamary # Stanislas Chambon # # License: MIT License # sphinx_gallery_thumbnail_number = 2 import numpy as np import matplotlib.pylab as pl import ot .. GENERATED FROM PYTHON SOURCE LINES 29-31 Generate data ------------- .. GENERATED FROM PYTHON SOURCE LINES 31-48 .. 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 .. GENERATED FROM PYTHON SOURCE LINES 49-51 Plot data --------- .. GENERATED FROM PYTHON SOURCE LINES 51-60 .. 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_mapping_001.png :alt: Source and target distributions :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_mapping_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(0.5, 1.0, 'Source and target distributions') .. GENERATED FROM PYTHON SOURCE LINES 61-63 Instantiate the different transport algorithms and fit them ----------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 63-91 .. 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 .. code-block:: none It. |Loss |Delta loss -------------------------------- 0|4.190105e+03|0.000000e+00 1|4.170411e+03|-4.700201e-03 2|4.169845e+03|-1.356805e-04 3|4.169664e+03|-4.344581e-05 4|4.169558e+03|-2.549048e-05 5|4.169490e+03|-1.619901e-05 6|4.169453e+03|-8.982881e-06 It. |Loss |Delta loss -------------------------------- 0|4.208325e+02|0.000000e+00 1|4.153391e+02|-1.305356e-02 2|4.150638e+02|-6.628072e-04 3|4.149220e+02|-3.416721e-04 4|4.148278e+02|-2.270372e-04 5|4.147579e+02|-1.685396e-04 6|4.147070e+02|-1.226155e-04 7|4.146654e+02|-1.002728e-04 8|4.146340e+02|-7.575830e-05 9|4.146055e+02|-6.889490e-05 10|4.145822e+02|-5.605308e-05 .. GENERATED FROM PYTHON SOURCE LINES 92-94 Plot transported samples ------------------------ .. GENERATED FROM PYTHON SOURCE LINES 94-128 .. 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-sg:: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_mapping_002.png :alt: Bary. mapping (linear), Estim. mapping (linear), Bary. mapping (kernel), Estim. mapping (kernel) :srcset: /auto_examples/domain-adaptation/images/sphx_glr_plot_otda_mapping_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.132 seconds) .. _sphx_glr_download_auto_examples_domain-adaptation_plot_otda_mapping.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_mapping.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_mapping.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_