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Diffstat (limited to 'examples/domain-adaptation/plot_otda_jcpot.py')
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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 <remi.flamary@unice.fr> +# Ievgen Redko <ievgen.redko@univ-st-etienne.fr> +# +# 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() |