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Author: agramfort

examples/plot_OTDA_mapping.py

@@ -4,107 +4,117 @@
 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
 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()