From a54775103541ea37f54269de1ba1e1396a6d7b30 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 17:32:57 +0200 Subject: exmaples in sections --- examples/domain-adaptation/README.txt | 5 + examples/domain-adaptation/plot_otda_classes.py | 149 ++++++++++++++++++ .../domain-adaptation/plot_otda_color_images.py | 166 ++++++++++++++++++++ examples/domain-adaptation/plot_otda_d2.py | 174 +++++++++++++++++++++ examples/domain-adaptation/plot_otda_jcpot.py | 171 ++++++++++++++++++++ examples/domain-adaptation/plot_otda_laplacian.py | 127 +++++++++++++++ .../domain-adaptation/plot_otda_linear_mapping.py | 146 +++++++++++++++++ examples/domain-adaptation/plot_otda_mapping.py | 127 +++++++++++++++ .../plot_otda_mapping_colors_images.py | 173 ++++++++++++++++++++ .../domain-adaptation/plot_otda_semi_supervised.py | 150 ++++++++++++++++++ 10 files changed, 1388 insertions(+) create mode 100644 examples/domain-adaptation/README.txt create mode 100644 examples/domain-adaptation/plot_otda_classes.py create mode 100644 examples/domain-adaptation/plot_otda_color_images.py create mode 100644 examples/domain-adaptation/plot_otda_d2.py create mode 100644 examples/domain-adaptation/plot_otda_jcpot.py create mode 100644 examples/domain-adaptation/plot_otda_laplacian.py create mode 100644 examples/domain-adaptation/plot_otda_linear_mapping.py create mode 100644 examples/domain-adaptation/plot_otda_mapping.py create mode 100644 examples/domain-adaptation/plot_otda_mapping_colors_images.py create mode 100644 examples/domain-adaptation/plot_otda_semi_supervised.py (limited to 'examples/domain-adaptation') diff --git a/examples/domain-adaptation/README.txt b/examples/domain-adaptation/README.txt new file mode 100644 index 0000000..81dd8d2 --- /dev/null +++ b/examples/domain-adaptation/README.txt @@ -0,0 +1,5 @@ + + + +Domain adaptation examples +-------------------------- \ No newline at end of file diff --git a/examples/domain-adaptation/plot_otda_classes.py b/examples/domain-adaptation/plot_otda_classes.py new file mode 100644 index 0000000..f028022 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_classes.py @@ -0,0 +1,149 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and the 4 OTDA +approaches currently supported in POT. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization l1l2 +ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, + verbose=True) +ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) +transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ + +param_img = {'interpolation': 'nearest'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 4, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 4, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 4, 3) +pl.imshow(ot_lpl1.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 4) +pl.imshow(ot_l1l2.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornL1l2Transport') + +pl.subplot(2, 4, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 4, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 4, 7) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 8) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornL1l2Transport') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_color_images.py b/examples/domain-adaptation/plot_otda_color_images.py new file mode 100644 index 0000000..7e0afee --- /dev/null +++ b/examples/domain-adaptation/plot_otda_color_images.py @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +""" +============================= +OT for image color adaptation +============================= + +This example presents a way of transferring colors between two images +with Optimal Transport as introduced in [6] + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import matplotlib.pylab as pl +import ot + + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts an image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +# ------------- + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Plot original image +# ------------------- + +pl.figure(1, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + + +############################################################################## +# Scatter plot of colors +# ---------------------- + +pl.figure(2, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + +############################################################################## +# Plot new images +# --------------- + +pl.figure(3, figsize=(8, 4)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(2, 3, 2) +pl.imshow(I1t) +pl.axis('off') +pl.title('Image 1 Adapt') + +pl.subplot(2, 3, 3) +pl.imshow(I1te) +pl.axis('off') +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +pl.subplot(2, 3, 5) +pl.imshow(I2t) +pl.axis('off') +pl.title('Image 2 Adapt') + +pl.subplot(2, 3, 6) +pl.imshow(I2te) +pl.axis('off') +pl.title('Image 2 Adapt (reg)') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_d2.py b/examples/domain-adaptation/plot_otda_d2.py new file mode 100644 index 0000000..f49a570 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_d2.py @@ -0,0 +1,174 @@ +# -*- coding: utf-8 -*- +""" +=================================================== +OT for domain adaptation on empirical distributions +=================================================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import matplotlib.pylab as pl +import ot +import ot.plot + +############################################################################## +# generate data +# ------------- + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) + +# Cost matrix +M = ot.dist(Xs, Xt, metric='sqeuclidean') + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +# --------------------------------------------------------------------- + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(M, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Matrix of pairwise distances') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +# --------------------------------------------------------- +pl.figure(2, figsize=(10, 6)) + +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_lpl1.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) +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.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nEMDTransport') + +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) +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.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornTransport') + +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) +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.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornLpl1Transport') +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +# -------------------------------- + +# display transported samples +pl.figure(4, figsize=(10, 4)) +pl.subplot(1, 3, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornLpl1Transport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/examples/domain-adaptation/plot_otda_jcpot.py b/examples/domain-adaptation/plot_otda_jcpot.py new file mode 100644 index 0000000..c495690 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_jcpot.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for multi-source target shift +======================== + +This example introduces a target shift problem with two 2D source and 1 target domain. + +""" + +# Authors: Remi Flamary +# Ievgen Redko +# +# License: MIT License + +import pylab as pl +import numpy as np +import ot +from ot.datasets import make_data_classif + +############################################################################## +# Generate data +# ------------- +n = 50 +sigma = 0.3 +np.random.seed(1985) + +p1 = .2 +dec1 = [0, 2] + +p2 = .9 +dec2 = [0, -2] + +pt = .4 +dect = [4, 0] + +xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) +xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) +xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) + +all_Xr = [xs1, xs2] +all_Yr = [ys1, ys2] +# %% + +da = 1.5 + + +def plot_ax(dec, name): + pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) + pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) + pl.text(dec[0] - .5, dec[1] + 2, name) + + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, + label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, + label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, + label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) +pl.title('Data') + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Instantiate Sinkhorn transport algorithm and fit them for all source domains +# ---------------------------------------------------------------------------- +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') + + +def print_G(G, xs, ys, xt): + for i in range(G.shape[0]): + for j in range(G.shape[1]): + if G[i, j] > 5e-4: + if ys[i]: + c = 'b' + else: + c = 'r' + pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ +pl.figure(2) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) +print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('Independent OT') + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Instantiate JCPOT adaptation algorithm and fit it +# ---------------------------------------------------------------------------- +otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) +otda.fit(all_Xr, all_Yr, xt) + +ws1 = otda.proportions_.dot(otda.log_['D2'][0]) +ws2 = otda.proportions_.dot(otda.log_['D2'][1]) + +pl.figure(3) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Run oracle transport algorithm with known proportions +# ---------------------------------------------------------------------------- +h_res = np.array([1 - pt, pt]) + +ws1 = h_res.dot(otda.log_['D2'][0]) +ws2 = h_res.dot(otda.log_['D2'][1]) + +pl.figure(4) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') +pl.show() diff --git a/examples/domain-adaptation/plot_otda_laplacian.py b/examples/domain-adaptation/plot_otda_laplacian.py new file mode 100644 index 0000000..67c8f67 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_laplacian.py @@ -0,0 +1,127 @@ +# -*- coding: utf-8 -*- +""" +====================================================== +OT with Laplacian regularization for domain adaptation +====================================================== + +This example introduces a domain adaptation in a 2D setting and OTDA +approach with Laplacian regularization. + +""" + +# Authors: Ievgen Redko + +# License: MIT License + +import matplotlib.pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# EMD Transport with Laplacian regularization +ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) +ot_emd_laplace.fit(Xs=Xs, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ + +param_img = {'interpolation': 'nearest'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_emd_laplace.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDLaplaceTransport') + +pl.subplot(2, 3, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 3, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 3, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEMDLaplaceTransport') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_linear_mapping.py b/examples/domain-adaptation/plot_otda_linear_mapping.py new file mode 100644 index 0000000..36ccb56 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_linear_mapping.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +============================ +Linear OT mapping estimation +============================ + + +""" + +# Author: Remi Flamary +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n = 1000 +d = 2 +sigma = .1 + +# source samples +angles = np.random.rand(n, 1) * 2 * np.pi +xs = np.concatenate((np.sin(angles), np.cos(angles)), + axis=1) + sigma * np.random.randn(n, 2) +xs[:n // 2, 1] += 2 + + +# target samples +anglet = np.random.rand(n, 1) * 2 * np.pi +xt = np.concatenate((np.sin(anglet), np.cos(anglet)), + axis=1) + sigma * np.random.randn(n, 2) +xt[:n // 2, 1] += 2 + + +A = np.array([[1.5, .7], [.7, 1.5]]) +b = np.array([[4, 2]]) +xt = xt.dot(A) + b + +############################################################################## +# Plot data +# --------- + +pl.figure(1, (5, 5)) +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') + + +############################################################################## +# Estimate linear mapping and transport +# ------------------------------------- + +Ae, be = ot.da.OT_mapping_linear(xs, xt) + +xst = xs.dot(Ae) + be + + +############################################################################## +# Plot transported samples +# ------------------------ + +pl.figure(1, (5, 5)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') +pl.plot(xst[:, 0], xst[:, 1], '+') + +pl.show() + +############################################################################## +# Load image data +# --------------- + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +############################################################################## +# Estimate mapping and adapt +# ---------------------------- + +mapping = ot.da.LinearTransport() + +mapping.fit(Xs=X1, Xt=X2) + + +xst = mapping.transform(Xs=X1) +xts = mapping.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(xst, I1.shape)) +I2t = minmax(mat2im(xts, I2.shape)) + +# %% + + +############################################################################## +# Plot transformed images +# ----------------------- + +pl.figure(2, figsize=(10, 7)) + +pl.subplot(2, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 2, 3) +pl.imshow(I1t) +pl.axis('off') +pl.title('Mapping Im. 1') + +pl.subplot(2, 2, 4) +pl.imshow(I2t) +pl.axis('off') +pl.title('Inverse mapping Im. 2') diff --git a/examples/domain-adaptation/plot_otda_mapping.py b/examples/domain-adaptation/plot_otda_mapping.py new file mode 100644 index 0000000..ded2bdf --- /dev/null +++ b/examples/domain-adaptation/plot_otda_mapping.py @@ -0,0 +1,127 @@ +# -*- coding: utf-8 -*- +""" +=========================================== +OT mapping estimation for domain adaptation +=========================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8]. + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 100 +n_target_samples = 100 +theta = 2 * np.pi / 20 +noise_level = 0.1 + +Xs, ys = ot.datasets.make_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xs_new, _ = ot.datasets.make_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xt, yt = ot.datasets.make_data_classif( + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) + +# one of the target mode changes its variance (no linear mapping) +Xt[yt == 2] *= 3 +Xt = Xt + 4 + +############################################################################## +# Plot data +# --------- + +pl.figure(1, (10, 5)) +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.legend(loc=0) +pl.title('Source and target distributions') + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# MappingTransport with linear kernel +ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + +# for out of source samples, transform applies the linear mapping +transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + +# MappingTransport with gaussian kernel +ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + +# for out of source samples, transform applies the gaussian mapping +transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + +############################################################################## +# Plot transported samples +# ------------------------ + +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(transp_Xs_linear[:, 0], transp_Xs_linear[:, 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(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 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(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 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(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') +pl.title("Estim. mapping (kernel)") +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py new file mode 100644 index 0000000..1276714 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +===================================================== +OT for image color adaptation with mapping estimation +===================================================== + +OT for domain adaptation with image color adaptation [6] with mapping +estimation [8]. + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized +discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for +discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 3 + +import numpy as np +import matplotlib.pylab as pl +import ot + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +# ------------- + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Domain adaptation for pixel distribution transfer +# ------------------------------------------------- + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) +transp_Xs_emd = ot_emd.transform(Xs=X1) +Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + +ot_mapping_linear = ot.da.MappingTransport( + mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +X1tl = ot_mapping_linear.transform(Xs=X1) +Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) + +ot_mapping_gaussian = ot.da.MappingTransport( + mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping +Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + + +############################################################################## +# Plot original images +# -------------------- + +pl.figure(1, figsize=(6.4, 3)) +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# Plot pixel values distribution +# ------------------------------ + +pl.figure(2, figsize=(6.4, 5)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# Plot transformed images +# ----------------------- + +pl.figure(2, figsize=(10, 5)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 3, 2) +pl.imshow(Image_emd) +pl.axis('off') +pl.title('EmdTransport') + +pl.subplot(2, 3, 5) +pl.imshow(Image_sinkhorn) +pl.axis('off') +pl.title('SinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(Image_mapping_linear) +pl.axis('off') +pl.title('MappingTransport (linear)') + +pl.subplot(2, 3, 6) +pl.imshow(Image_mapping_gaussian) +pl.axis('off') +pl.title('MappingTransport (gaussian)') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_semi_supervised.py b/examples/domain-adaptation/plot_otda_semi_supervised.py new file mode 100644 index 0000000..478c3b8 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_semi_supervised.py @@ -0,0 +1,150 @@ +# -*- coding: utf-8 -*- +""" +============================================ +OTDA unsupervised vs semi-supervised setting +============================================ + +This example introduces a semi supervised domain adaptation in a 2D setting. +It explicits the problem of semi supervised domain adaptation and introduces +some optimal transport approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 3 + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) + + +############################################################################## +# Transport source samples onto target samples +# -------------------------------------------- + + +# unsupervised domain adaptation +ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) + +# semi-supervised domain adaptation +ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) +transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) + +# semi supervised DA uses available labaled target samples to modify the cost +# matrix involved in the OT problem. The cost of transporting a source sample +# of class A onto a target sample of class B != A is set to infinite, or a +# very large value + +# note that in the present case we consider that all the target samples are +# labeled. For daily applications, some target sample might not have labels, +# in this case the element of yt corresponding to these samples should be +# filled with -1. + +# Warning: we recall that -1 cannot be used as a class label + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +# --------------------------------------------------------------------- + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - unsupervised DA') + +pl.subplot(2, 2, 4) +pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - semisupervised DA') + +pl.tight_layout() + +# the optimal coupling in the semi-supervised DA case will exhibit " shape +# similar" to the cost matrix, (block diagonal matrix) + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +# --------------------------------------------------------- + +pl.figure(2, figsize=(8, 4)) + +pl.subplot(1, 2, 1) +pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nUnsupervised DA') + +pl.subplot(1, 2, 2) +pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSemi-supervised DA') + +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +# -------------------------------- + +# display transported samples +pl.figure(4, figsize=(8, 4)) +pl.subplot(1, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() -- cgit v1.2.3