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.. _sphx_glr_auto_examples_plot_OTDA_classes.py:
========================
OT for domain adaptation
========================
.. rst-class:: sphx-glr-horizontal
*
.. image:: /auto_examples/images/sphx_glr_plot_OTDA_classes_001.png
:scale: 47
*
.. image:: /auto_examples/images/sphx_glr_plot_OTDA_classes_004.png
:scale: 47
.. rst-class:: sphx-glr-script-out
Out::
It. |Loss |Delta loss
--------------------------------
0|9.171271e+00|0.000000e+00
1|2.133783e+00|-3.298127e+00
2|1.895941e+00|-1.254484e-01
3|1.844628e+00|-2.781709e-02
4|1.824983e+00|-1.076467e-02
5|1.815453e+00|-5.249337e-03
6|1.808104e+00|-4.064733e-03
7|1.803558e+00|-2.520475e-03
8|1.801061e+00|-1.386155e-03
9|1.799391e+00|-9.279565e-04
10|1.797176e+00|-1.232778e-03
11|1.795465e+00|-9.529479e-04
12|1.795316e+00|-8.322362e-05
13|1.794523e+00|-4.418932e-04
14|1.794444e+00|-4.390599e-05
15|1.794395e+00|-2.710318e-05
16|1.793713e+00|-3.804028e-04
17|1.793110e+00|-3.359479e-04
18|1.792829e+00|-1.569563e-04
19|1.792621e+00|-1.159469e-04
It. |Loss |Delta loss
--------------------------------
20|1.791334e+00|-7.187689e-04
|
.. code-block:: python
import matplotlib.pylab as pl
import ot
#%% parameters
n=150 # nb samples in source and target datasets
xs,ys=ot.datasets.get_data_classif('3gauss',n)
xt,yt=ot.datasets.get_data_classif('3gauss2',n)
#%% plot samples
pl.figure(1)
pl.subplot(2,2,1)
pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')
pl.legend(loc=0)
pl.title('Source distributions')
pl.subplot(2,2,2)
pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')
pl.legend(loc=0)
pl.title('target distributions')
#%% OT estimation
# LP problem
da_emd=ot.da.OTDA() # init class
da_emd.fit(xs,xt) # fit distributions
xst0=da_emd.interp() # interpolation of source samples
# sinkhorn regularization
lambd=1e-1
da_entrop=ot.da.OTDA_sinkhorn()
da_entrop.fit(xs,xt,reg=lambd)
xsts=da_entrop.interp()
# non-convex Group lasso regularization
reg=1e-1
eta=1e0
da_lpl1=ot.da.OTDA_lpl1()
da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta)
xstg=da_lpl1.interp()
# True Group lasso regularization
reg=1e-1
eta=2e0
da_l1l2=ot.da.OTDA_l1l2()
da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True)
xstgl=da_l1l2.interp()
#%% plot interpolated source samples
pl.figure(4,(15,8))
param_img={'interpolation':'nearest','cmap':'jet'}
pl.subplot(2,4,1)
pl.imshow(da_emd.G,**param_img)
pl.title('OT matrix')
pl.subplot(2,4,2)
pl.imshow(da_entrop.G,**param_img)
pl.title('OT matrix sinkhorn')
pl.subplot(2,4,3)
pl.imshow(da_lpl1.G,**param_img)
pl.title('OT matrix non-convex Group Lasso')
pl.subplot(2,4,4)
pl.imshow(da_l1l2.G,**param_img)
pl.title('OT matrix Group Lasso')
pl.subplot(2,4,5)
pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)
pl.title('Interp samples')
pl.legend(loc=0)
pl.subplot(2,4,6)
pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)
pl.title('Interp samples Sinkhorn')
pl.subplot(2,4,7)
pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)
pl.title('Interp samples non-convex Group Lasso')
pl.subplot(2,4,8)
pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)
pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30)
pl.title('Interp samples Group Lasso')
**Total running time of the script:** ( 0 minutes 2.225 seconds)
.. 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>`
.. rst-class:: sphx-glr-signature
`Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
|
