diff options
Diffstat (limited to 'docs/source/auto_examples/plot_otda_classes.rst')
| -rw-r--r-- | docs/source/auto_examples/plot_otda_classes.rst | 287 |
1 files changed, 0 insertions, 287 deletions
diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst deleted file mode 100644 index 9cf31ee..0000000 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ /dev/null @@ -1,287 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here <sphx_glr_download_auto_examples_plot_otda_classes.py>` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_classes.py: - - -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and the 4 OTDA -approaches currently supported in POT. - - - -.. code-block:: default - - - # Authors: Remi Flamary <remi.flamary@unice.fr> - # Stanislas Chambon <stan.chambon@gmail.com> - # - # License: MIT License - - import matplotlib.pylab as pl - import ot - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n_source_samples = 150 - n_target_samples = 150 - - Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) - Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - - - - - - - - -Instantiate the different transport algorithms and fit them ------------------------------------------------------------ - - -.. code-block:: default - - - # 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) - - # Sinkhorn Transport with Group lasso regularization l1l2 - ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, - verbose=True) - ot_l1l2.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) - transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) - - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|9.484039e+00|0.000000e+00|0.000000e+00 - 1|1.976107e+00|3.799355e+00|7.507932e+00 - 2|1.749871e+00|1.292876e-01|2.262365e-01 - 3|1.692667e+00|3.379504e-02|5.720374e-02 - 4|1.676256e+00|9.790077e-03|1.641068e-02 - 5|1.667458e+00|5.276422e-03|8.798212e-03 - 6|1.661775e+00|3.419693e-03|5.682762e-03 - 7|1.658009e+00|2.271789e-03|3.766646e-03 - 8|1.655167e+00|1.716870e-03|2.841707e-03 - 9|1.651825e+00|2.023380e-03|3.342270e-03 - 10|1.649431e+00|1.451076e-03|2.393450e-03 - 11|1.648649e+00|4.742894e-04|7.819369e-04 - 12|1.647901e+00|4.538219e-04|7.478538e-04 - 13|1.647356e+00|3.313134e-04|5.457909e-04 - 14|1.646923e+00|2.627246e-04|4.326871e-04 - 15|1.646038e+00|5.375014e-04|8.847478e-04 - 16|1.645629e+00|2.483240e-04|4.086492e-04 - 17|1.645616e+00|8.248172e-06|1.357332e-05 - 18|1.645377e+00|1.452648e-04|2.390153e-04 - 19|1.644745e+00|3.838976e-04|6.314139e-04 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 20|1.644164e+00|3.538439e-04|5.817773e-04 - - - - -Fig 1 : plots source and target samples ---------------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(10, 5)) - pl.subplot(1, 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(1, 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.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_001.png - :class: sphx-glr-single-img - - - - - -Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------- - - -.. code-block:: default - - - param_img = {'interpolation': 'nearest'} - - pl.figure(2, figsize=(15, 8)) - pl.subplot(2, 4, 1) - pl.imshow(ot_emd.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nEMDTransport') - - pl.subplot(2, 4, 2) - pl.imshow(ot_sinkhorn.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornTransport') - - pl.subplot(2, 4, 3) - pl.imshow(ot_lpl1.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornLpl1Transport') - - pl.subplot(2, 4, 4) - pl.imshow(ot_l1l2.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornL1l2Transport') - - pl.subplot(2, 4, 5) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nEmdTransport') - pl.legend(loc="lower left") - - pl.subplot(2, 4, 6) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nSinkhornTransport') - - pl.subplot(2, 4, 7) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nSinkhornLpl1Transport') - - pl.subplot(2, 4, 8) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nSinkhornL1l2Transport') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_classes.py:149: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 2.083 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_classes.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_classes.py <plot_otda_classes.py>` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_classes.ipynb <plot_otda_classes.ipynb>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_ |