diff options
| -rw-r--r-- | examples/plot_OTDA_mapping.py | 118 |
1 files changed, 64 insertions, 54 deletions
diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py index 78b57e7..a5c2b21 100644 --- a/examples/plot_OTDA_mapping.py +++ b/examples/plot_OTDA_mapping.py @@ -4,8 +4,9 @@ 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. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. """ import numpy as np @@ -13,98 +14,107 @@ import matplotlib.pylab as pl import ot - #%% dataset generation -np.random.seed(0) # makes example reproducible +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) +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 +xt[yt == 2] *= 3 +xt = xt + 4 #%% plot samples -pl.figure(1,(8,5)) +pl.figure(1, (6.4, 3)) 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.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 +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) +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 +xst = ot_mapping.predict(xs) # use the estimated mapping +xst0 = ot_mapping.interp() # use barycentric mapping -pl.figure(2,(10,7)) +pl.figure(2) 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.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.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") - - +pl.tight_layout() #%% 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 +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) +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 +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.figure(2) 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.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.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.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.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)") +pl.tight_layout() + +pl.show() |