add domain adaptation demo

Author: rflamary

examples/demo_OTDA_classes.py

@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+"""
+demo of Optimal transport for domain adaptation
+"""
+
+import numpy as np
+import matplotlib.pylab as pl
+import ot
+
+
+
+#%% parameters
+
+n=150 # nb bins
+
+xs,ys=ot.datasets.get_data_classif('3gauss',n)
+xt,yt=ot.datasets.get_data_classif('3gauss2',n)
+
+a,b = ot.unif(n),ot.unif(n)
+# loss matrix
+M=ot.dist(xs,xt)
+#M/=M.max()
+
+#%% 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
+
+# EMD
+
+
+da_emd=ot.da.OTDA()
+da_emd.fit(xs,xt)
+
+# interpolate samples
+xst0=da_emd.interp()
+
+
+# sinkhorn
+lambd=1e-1
+da_entrop=ot.da.OTDA_sinkhorn()
+da_entrop.fit(xs,xt,reg=lambd)
+xsts=da_entrop.interp()
+
+# Group lasso regularization
+reg=1e-1
+eta=1e0
+da_lpl1=ot.da.OTDA_lpl1()
+da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta)
+xstg=da_lpl1.interp()
+
+#%% plot interpolated source samples
+pl.figure(4)
+
+param_img={'interpolation':'nearest','cmap':'jet'}
+
+pl.subplot(2,3,1)
+pl.imshow(da_emd.G,**param_img)
+pl.title('OT matrix')
+
+
+pl.subplot(2,3,2)
+pl.imshow(da_entrop.G,**param_img)
+pl.title('OT matrix sinkhorn')
+
+pl.subplot(2,3,3)
+pl.imshow(da_lpl1.G,**param_img)
+pl.title('OT matrix Group Lasso')
+
+pl.subplot(2,3,4)
+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(2,3,5)
+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(2,3,6)
+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 Group Lasso')