From ab5918b2e2dc88a3520c059e6a79a6f81959381e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 17:02:59 +0200 Subject: add files and notebooks --- docs/source/auto_examples/plot_otda_d2.rst | 265 +++++++++++++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 docs/source/auto_examples/plot_otda_d2.rst (limited to 'docs/source/auto_examples/plot_otda_d2.rst') diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst new file mode 100644 index 0000000..20b76ba --- /dev/null +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -0,0 +1,265 @@ + + +.. _sphx_glr_auto_examples_plot_otda_d2.py: + + +============================== +OT for empirical distributions +============================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. + + + +.. code-block:: python + + + # Authors: Remi Flamary + # Stanislas Chambon + # + # License: MIT License + + import matplotlib.pylab as pl + import ot + + + + + + + + +generate data +############################################################################# + + + +.. code-block:: python + + + n_samples_source = 150 + n_samples_target = 150 + + Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + + # Cost matrix + M = ot.dist(Xs, Xt, metric='sqeuclidean') + + + + + + + + +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) + + # 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) + + + + + + + + +Fig 1 : plots source and target samples + matrix of pairwise distance +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, figsize=(10, 10)) + pl.subplot(2, 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(2, 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.subplot(2, 2, 3) + pl.imshow(M, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Matrix of pairwise distances') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_001.png + :align: center + + + + +Fig 2 : plots optimal couplings for the different methods +############################################################################# + + + +.. code-block:: python + + + pl.figure(2, figsize=(10, 6)) + + pl.subplot(2, 3, 1) + pl.imshow(ot_emd.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nEMDTransport') + + pl.subplot(2, 3, 2) + pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornTransport') + + pl.subplot(2, 3, 3) + pl.imshow(ot_lpl1.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornLpl1Transport') + + pl.subplot(2, 3, 4) + ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) + 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.xticks([]) + pl.yticks([]) + pl.title('Main coupling coefficients\nEMDTransport') + + pl.subplot(2, 3, 5) + ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) + 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.xticks([]) + pl.yticks([]) + pl.title('Main coupling coefficients\nSinkhornTransport') + + pl.subplot(2, 3, 6) + ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) + 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.xticks([]) + pl.yticks([]) + pl.title('Main coupling coefficients\nSinkhornLpl1Transport') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png + :align: center + + + + +Fig 3 : plot transported samples +############################################################################# + + + +.. code-block:: python + + + # display transported samples + pl.figure(4, figsize=(10, 4)) + pl.subplot(1, 3, 1) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nEmdTransport') + pl.legend(loc=0) + pl.xticks([]) + pl.yticks([]) + + pl.subplot(1, 3, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nSinkhornTransport') + pl.xticks([]) + pl.yticks([]) + + pl.subplot(1, 3, 3) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nSinkhornLpl1Transport') + pl.xticks([]) + pl.yticks([]) + + pl.tight_layout() + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_006.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 46.009 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_d2.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_d2.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ -- cgit v1.2.3