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Diffstat (limited to 'examples/plot_fgw.py')
| -rw-r--r-- | examples/plot_fgw.py | 138 |
1 files changed, 69 insertions, 69 deletions
diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py index bfa7fb4..ae3c487 100644 --- a/examples/plot_fgw.py +++ b/examples/plot_fgw.py @@ -20,132 +20,132 @@ This example illustrates the computation of FGW for 1D measures[18]. import matplotlib.pyplot as pl import numpy as np import ot -from ot.gromov import gromov_wasserstein,fused_gromov_wasserstein +from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein #%% parameters -# We create two 1D random measures -n=20 -n2=30 -sig=1 -sig2=0.1 +# We create two 1D random measures +n = 20 +n2 = 30 +sig = 1 +sig2 = 0.1 np.random.seed(0) -phi=np.arange(n)[:,None] -xs=phi+sig*np.random.randn(n,1) -ys=np.vstack((np.ones((n//2,1)),0*np.ones((n//2,1))))+sig2*np.random.randn(n,1) +phi = np.arange(n)[:, None] +xs = phi + sig * np.random.randn(n, 1) +ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) -phi2=np.arange(n2)[:,None] -xt=phi2+sig*np.random.randn(n2,1) -yt=np.vstack((np.ones((n2//2,1)),0*np.ones((n2//2,1))))+sig2*np.random.randn(n2,1) -yt= yt[::-1,:] +phi2 = np.arange(n2)[:, None] +xt = phi2 + sig * np.random.randn(n2, 1) +yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) +yt = yt[::-1, :] -p=ot.unif(n) -q=ot.unif(n2) +p = ot.unif(n) +q = ot.unif(n2) #%% plot the distributions pl.close(10) -pl.figure(10,(7,7)) +pl.figure(10, (7, 7)) -pl.subplot(2,1,1) +pl.subplot(2, 1, 1) -pl.scatter(ys,xs,c=phi,s=70) -pl.ylabel('Feature value a',fontsize=20) -pl.title('$\mu=\sum_i \delta_{x_i,a_i}$',fontsize=25, usetex=True, y=1) +pl.scatter(ys, xs, c=phi, s=70) +pl.ylabel('Feature value a', fontsize=20) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) pl.xticks(()) pl.yticks(()) -pl.subplot(2,1,2) -pl.scatter(yt,xt,c=phi2,s=70) -pl.xlabel('coordinates x/y',fontsize=25) -pl.ylabel('Feature value b',fontsize=20) -pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$',fontsize=25, usetex=True, y=1) +pl.subplot(2, 1, 2) +pl.scatter(yt, xt, c=phi2, s=70) +pl.xlabel('coordinates x/y', fontsize=25) +pl.ylabel('Feature value b', fontsize=20) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) pl.yticks(()) pl.tight_layout() pl.show() #%% Structure matrices and across-features distance matrix -C1=ot.dist(xs) -C2=ot.dist(xt).T -M=ot.dist(ys,yt) -w1=ot.unif(C1.shape[0]) -w2=ot.unif(C2.shape[0]) -Got=ot.emd([],[],M) +C1 = ot.dist(xs) +C2 = ot.dist(xt).T +M = ot.dist(ys, yt) +w1 = ot.unif(C1.shape[0]) +w2 = ot.unif(C2.shape[0]) +Got = ot.emd([], [], M) #%% -cmap='Reds' +cmap = 'Reds' pl.close(10) -pl.figure(10,(5,5)) -fs=15 -l_x=[0,5,10,15] -l_y=[0,5,10,15,20,25] +pl.figure(10, (5, 5)) +fs = 15 +l_x = [0, 5, 10, 15] +l_y = [0, 5, 10, 15, 20, 25] gs = pl.GridSpec(5, 5) -ax1=pl.subplot(gs[3:,:2]) +ax1 = pl.subplot(gs[3:, :2]) -pl.imshow(C1,cmap=cmap,interpolation='nearest') -pl.title("$C_1$",fontsize=fs) -pl.xlabel("$k$",fontsize=fs) -pl.ylabel("$i$",fontsize=fs) +pl.imshow(C1, cmap=cmap, interpolation='nearest') +pl.title("$C_1$", fontsize=fs) +pl.xlabel("$k$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) pl.xticks(l_x) pl.yticks(l_x) -ax2=pl.subplot(gs[:3,2:]) +ax2 = pl.subplot(gs[:3, 2:]) -pl.imshow(C2,cmap=cmap,interpolation='nearest') -pl.title("$C_2$",fontsize=fs) -pl.ylabel("$l$",fontsize=fs) +pl.imshow(C2, cmap=cmap, interpolation='nearest') +pl.title("$C_2$", fontsize=fs) +pl.ylabel("$l$", fontsize=fs) #pl.ylabel("$l$",fontsize=fs) pl.xticks(()) pl.yticks(l_y) ax2.set_aspect('auto') -ax3=pl.subplot(gs[3:,2:],sharex=ax2,sharey=ax1) -pl.imshow(M,cmap=cmap,interpolation='nearest') +ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) +pl.imshow(M, cmap=cmap, interpolation='nearest') pl.yticks(l_x) pl.xticks(l_y) -pl.ylabel("$i$",fontsize=fs) -pl.title("$M_{AB}$",fontsize=fs) -pl.xlabel("$j$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.title("$M_{AB}$", fontsize=fs) +pl.xlabel("$j$", fontsize=fs) pl.tight_layout() ax3.set_aspect('auto') pl.show() #%% Computing FGW and GW -alpha=1e-3 - +alpha = 1e-3 + ot.tic() -Gwg,logw=fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=alpha,verbose=True,log=True) +Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) ot.toc() -#%reload_ext WGW -Gg,log=gromov_wasserstein(C1,C2,p,q,loss_fun='square_loss',verbose=True,log=True) - +#%reload_ext WGW +Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + #%% visu OT matrix -cmap='Blues' -fs=15 -pl.figure(2,(13,5)) +cmap = 'Blues' +fs = 15 +pl.figure(2, (13, 5)) pl.clf() -pl.subplot(1,3,1) -pl.imshow(Got,cmap=cmap,interpolation='nearest') +pl.subplot(1, 3, 1) +pl.imshow(Got, cmap=cmap, interpolation='nearest') #pl.xlabel("$y$",fontsize=fs) -pl.ylabel("$i$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) pl.xticks(()) pl.title('Wasserstein ($M$ only)') -pl.subplot(1,3,2) -pl.imshow(Gg,cmap=cmap,interpolation='nearest') +pl.subplot(1, 3, 2) +pl.imshow(Gg, cmap=cmap, interpolation='nearest') pl.title('Gromov ($C_1,C_2$ only)') pl.xticks(()) -pl.subplot(1,3,3) -pl.imshow(Gwg,cmap=cmap,interpolation='nearest') +pl.subplot(1, 3, 3) +pl.imshow(Gwg, cmap=cmap, interpolation='nearest') pl.title('FGW ($M+C_1,C_2$)') -pl.xlabel("$j$",fontsize=fs) -pl.ylabel("$i$",fontsize=fs) +pl.xlabel("$j$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) pl.tight_layout() -pl.show()
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