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plot_OTDA_classes.py
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plot_OTDA_classes.py
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# -*- coding: utf-8 -*-
"""
========================
OT for domain adaptation
========================
"""
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')