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+
+
+.. _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:: python
+
+
+ # 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:: python
+
+
+ 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:: 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)
+
+ # 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::
+
+ It. |Loss |Delta loss
+ --------------------------------
+ 0|9.566309e+00|0.000000e+00
+ 1|2.169680e+00|-3.409088e+00
+ 2|1.914989e+00|-1.329986e-01
+ 3|1.860251e+00|-2.942498e-02
+ 4|1.838073e+00|-1.206621e-02
+ 5|1.827064e+00|-6.025122e-03
+ 6|1.820899e+00|-3.386082e-03
+ 7|1.817290e+00|-1.985705e-03
+ 8|1.814644e+00|-1.458223e-03
+ 9|1.812661e+00|-1.093816e-03
+ 10|1.810239e+00|-1.338121e-03
+ 11|1.809100e+00|-6.296940e-04
+ 12|1.807939e+00|-6.420646e-04
+ 13|1.806965e+00|-5.389118e-04
+ 14|1.806822e+00|-7.889599e-05
+ 15|1.806193e+00|-3.482356e-04
+ 16|1.805735e+00|-2.536930e-04
+ 17|1.805321e+00|-2.292667e-04
+ 18|1.804389e+00|-5.170222e-04
+ 19|1.803908e+00|-2.661907e-04
+ It. |Loss |Delta loss
+ --------------------------------
+ 20|1.803696e+00|-1.178279e-04
+
+
+Fig 1 : plots source and target samples
+---------------------------------------
+
+
+
+.. code-block:: python
+
+
+ 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
+ :align: center
+
+
+
+
+Fig 2 : plot optimal couplings and transported samples
+------------------------------------------------------
+
+
+
+.. code-block:: python
+
+
+ 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_003.png
+ :align: center
+
+
+
+
+**Total running time of the script:** ( 0 minutes 1.423 seconds)
+
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+
+
+ .. container:: sphx-glr-download
+
+ :download:`Download Python source code: plot_otda_classes.py <plot_otda_classes.py>`
+
+
+
+ .. container:: sphx-glr-download
+
+ :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.readthedocs.io>`_
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-17 19:14:13 +0000