.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_plot_otda_mapping.py:
===========================================
OT mapping estimation for domain adaptation
===========================================
This example presents how to use MappingTransport to estimate at the same
time both the coupling transport and approximate the transport map with either
a linear or a kernelized mapping as introduced in [8].
[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,
"Mapping estimation for discrete optimal transport",
Neural Information Processing Systems (NIPS), 2016.
.. code-block:: default
# Authors: Remi Flamary
# Stanislas Chambon
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
import ot
Generate data
-------------
.. code-block:: default
n_source_samples = 100
n_target_samples = 100
theta = 2 * np.pi / 20
noise_level = 0.1
Xs, ys = ot.datasets.make_data_classif(
'gaussrot', n_source_samples, nz=noise_level)
Xs_new, _ = ot.datasets.make_data_classif(
'gaussrot', n_source_samples, nz=noise_level)
Xt, yt = ot.datasets.make_data_classif(
'gaussrot', n_target_samples, theta=theta, nz=noise_level)
# one of the target mode changes its variance (no linear mapping)
Xt[yt == 2] *= 3
Xt = Xt + 4
Plot data
---------
.. code-block:: default
pl.figure(1, (10, 5))
pl.clf()
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.legend(loc=0)
pl.title('Source and target distributions')
.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Text(0.5, 1.0, 'Source and target distributions')
Instantiate the different transport algorithms and fit them
-----------------------------------------------------------
.. code-block:: default
# MappingTransport with linear kernel
ot_mapping_linear = ot.da.MappingTransport(
kernel="linear", mu=1e0, eta=1e-8, bias=True,
max_iter=20, verbose=True)
ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
# for original source samples, transform applies barycentric mapping
transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)
# for out of source samples, transform applies the linear mapping
transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)
# MappingTransport with gaussian kernel
ot_mapping_gaussian = ot.da.MappingTransport(
kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1,
max_iter=10, verbose=True)
ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
# for original source samples, transform applies barycentric mapping
transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)
# for out of source samples, transform applies the gaussian mapping
transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
It. |Loss |Delta loss
--------------------------------
0|4.212661e+03|0.000000e+00
1|4.198567e+03|-3.345626e-03
2|4.198198e+03|-8.797101e-05
3|4.198027e+03|-4.059527e-05
4|4.197928e+03|-2.355659e-05
5|4.197886e+03|-1.002352e-05
6|4.197853e+03|-7.873125e-06
It. |Loss |Delta loss
--------------------------------
0|4.231694e+02|0.000000e+00
1|4.185911e+02|-1.081889e-02
2|4.182717e+02|-7.631953e-04
3|4.181271e+02|-3.455908e-04
4|4.180328e+02|-2.255461e-04
5|4.179645e+02|-1.634435e-04
6|4.179136e+02|-1.216359e-04
7|4.178752e+02|-9.198108e-05
8|4.178465e+02|-6.870868e-05
9|4.178243e+02|-5.321390e-05
10|4.178054e+02|-4.521725e-05
Plot transported samples
------------------------
.. code-block:: default
pl.figure(2)
pl.clf()
pl.subplot(2, 2, 1)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
label='Target samples', alpha=.2)
pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',
label='Mapped source samples')
pl.title("Bary. mapping (linear)")
pl.legend(loc=0)
pl.subplot(2, 2, 2)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
label='Target samples', alpha=.2)
pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],
c=ys, marker='+', label='Learned mapping')
pl.title("Estim. mapping (linear)")
pl.subplot(2, 2, 3)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
label='Target samples', alpha=.2)
pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,
marker='+', label='barycentric mapping')
pl.title("Bary. mapping (kernel)")
pl.subplot(2, 2, 4)
pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',
label='Target samples', alpha=.2)
pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,
marker='+', label='Learned mapping')
pl.title("Estim. mapping (kernel)")
pl.tight_layout()
pl.show()
.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_002.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/home/rflamary/PYTHON/POT/examples/plot_otda_mapping.py:125: 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 0.843 seconds)
.. _sphx_glr_download_auto_examples_plot_otda_mapping.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_mapping.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_otda_mapping.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_