.. _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 # Stanislas Chambon # # 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 ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_otda_mapping.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_