.. _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 ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_OTDA_mapping.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_