diff options
Diffstat (limited to 'examples/plot_OTDA_classes.py')
| -rw-r--r-- | examples/plot_OTDA_classes.py | 129 |
1 files changed, 65 insertions, 64 deletions
diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py index 089b45b..4d3846a 100644 --- a/examples/plot_OTDA_classes.py +++ b/examples/plot_OTDA_classes.py @@ -10,29 +10,25 @@ 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) - +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.figure(1, figsize=(6.4, 3)) -pl.subplot(2,2,1) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.subplot(1, 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.subplot(1, 2, 2) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') pl.legend(loc=0) pl.title('target distributions') @@ -40,73 +36,78 @@ 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 - +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() +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() - +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() - +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'} +param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} -pl.subplot(2,4,1) -pl.imshow(da_emd.G,**param_img) +pl.figure(2, figsize=(8, 4.5)) +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\nsinkhorn') -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, 3) +pl.imshow(da_lpl1.G, **param_img) +pl.title('OT matrix\nnon-convex Group Lasso') +pl.subplot(2, 4, 4) +pl.imshow(da_l1l2.G, **param_img) +pl.title('OT matrix\nGroup 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.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')
\ No newline at end of file +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\nSinkhorn') + +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\nnon-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\nGroup Lasso') +pl.tight_layout() +pl.show() |