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diff --git a/docs/source/auto_examples/plot_OTDA_mapping.rst b/docs/source/auto_examples/plot_OTDA_mapping.rst new file mode 100644 index 0000000..18da90d --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping.rst @@ -0,0 +1,186 @@ + + +.. _sphx_glr_auto_examples_plot_OTDA_mapping.py: + + +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_002.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|4.009366e+03|0.000000e+00 + 1|3.999933e+03|-2.352753e-03 + 2|3.999520e+03|-1.031984e-04 + 3|3.999362e+03|-3.936391e-05 + 4|3.999281e+03|-2.032868e-05 + 5|3.999238e+03|-1.083083e-05 + 6|3.999229e+03|-2.125291e-06 + It. |Loss |Delta loss + -------------------------------- + 0|4.026841e+02|0.000000e+00 + 1|3.990791e+02|-8.952439e-03 + 2|3.987954e+02|-7.107124e-04 + 3|3.986554e+02|-3.512453e-04 + 4|3.985721e+02|-2.087997e-04 + 5|3.985141e+02|-1.456184e-04 + 6|3.984729e+02|-1.034624e-04 + 7|3.984435e+02|-7.366943e-05 + 8|3.984199e+02|-5.922497e-05 + 9|3.984016e+02|-4.593063e-05 + 10|3.983867e+02|-3.733061e-05 + + + + +| + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + #%% dataset generation + + np.random.seed(0) # makes example reproducible + + n=100 # nb samples in source and target datasets + theta=2*np.pi/20 + nz=0.1 + xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) + xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) + + # one of the target mode changes its variance (no linear mapping) + xt[yt==2]*=3 + xt=xt+4 + + + #%% plot samples + + pl.figure(1,(8,5)) + 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') + + + + #%% OT linear mapping estimation + + eta=1e-8 # quadratic regularization for regression + mu=1e0 # weight of the OT linear term + bias=True # estimate a bias + + ot_mapping=ot.da.OTDA_mapping_linear() + ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + + xst=ot_mapping.predict(xs) # use the estimated mapping + xst0=ot_mapping.interp() # use barycentric mapping + + + pl.figure(2,(10,7)) + pl.clf() + pl.subplot(2,2,1) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) + pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') + pl.title("barycentric mapping") + + pl.subplot(2,2,2) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) + pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') + pl.title("Learned mapping") + + + + #%% Kernel mapping estimation + + eta=1e-5 # quadratic regularization for regression + mu=1e-1 # weight of the OT linear term + bias=True # estimate a bias + sigma=1 # sigma bandwidth fot gaussian kernel + + + ot_mapping_kernel=ot.da.OTDA_mapping_kernel() + ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + + xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping + xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping + + + #%% Plotting the mapped samples + + pl.figure(2,(10,7)) + pl.clf() + pl.subplot(2,2,1) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) + pl.scatter(xst0[:,0],xst0[:,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(xst[:,0],xst[:,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(xst0_kernel[:,0],xst0_kernel[:,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(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') + pl.title("Estim. mapping (kernel)") + +**Total running time of the script:** ( 0 minutes 0.882 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OTDA_mapping.py <plot_OTDA_mapping.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OTDA_mapping.ipynb <plot_OTDA_mapping.ipynb>` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ |