diff options
Diffstat (limited to 'docs/source/auto_examples/plot_otda_d2.rst')
| -rw-r--r-- | docs/source/auto_examples/plot_otda_d2.rst | 269 |
1 files changed, 0 insertions, 269 deletions
diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst deleted file mode 100644 index 80cc34c..0000000 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ /dev/null @@ -1,269 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_otda_d2.py: - - -=================================================== -OT for domain adaptation on 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 <remi.flamary@unice.fr> - # Stanislas Chambon <stan.chambon@gmail.com> - # - # License: MIT License - - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - -generate data -------------- - - - -.. code-block:: python - - - n_samples_source = 150 - n_samples_target = 150 - - Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) - Xt, yt = ot.datasets.make_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 35.515 seconds) - - - -.. only :: html - - .. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_otda_d2.py <plot_otda_d2.py>` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_otda_d2.ipynb <plot_otda_d2.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |