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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>`_
|
