Regularization path of l2-penalized unbalanced optimal transport

This example illustrate the regularization path for 2D unbalanced optimal transport. We present here both the fully relaxed case and the semi-relaxed case.

[Chapel et al., 2021] Chapel, L., Flamary, R., Wu, H., Févotte, C., and Gasso, G. (2021). Unbalanced optimal transport through non-negative penalized linear regression.

# Author: Haoran Wu <haoran.wu@univ-ubs.fr>
# License: MIT License

# sphinx_gallery_thumbnail_number = 2

import numpy as np
import matplotlib.pylab as pl
import ot
import matplotlib.animation as animation

Generate data

n = 50  # nb samples

mu_s = np.array([-1, -1])
cov_s = np.array([[1, 0], [0, 1]])

mu_t = np.array([4, 4])
cov_t = np.array([[1, -.8], [-.8, 1]])

np.random.seed(0)
xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)
xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)

a, b = np.ones((n,)) / n, np.ones((n,)) / n  # uniform distribution on samples

# loss matrix
M = ot.dist(xs, xt)
M /= M.max()

Plot data

pl.figure(1)
pl.scatter(xs[:, 0], xs[:, 1], c='C0', label='Source')
pl.scatter(xt[:, 0], xt[:, 1], c='C1', label='Target')
pl.legend(loc=2)
pl.title('Source and target distributions')
pl.show()

Compute semi-relaxed and fully relaxed regularization paths

final_gamma = 1e-8
t, t_list, g_list = ot.regpath.regularization_path(a, b, M, reg=final_gamma,
                                                   semi_relaxed=False)
t2, t_list2, g_list2 = ot.regpath.regularization_path(a, b, M, reg=final_gamma,
                                                      semi_relaxed=True)

Plot the regularization path

The OT plan is ploted as a function of $gamma$ that is the inverse of the weight on the marginal relaxations.

pl.figure(2)
selected_gamma = [2e-1, 1e-1, 5e-2, 1e-3]
for p in range(4):
    tp = ot.regpath.compute_transport_plan(selected_gamma[p], g_list,
                                           t_list)
    P = tp.reshape((n, n))
    pl.subplot(2, 2, p + 1)
    if P.sum() > 0:
        P = P / P.max()
    for i in range(n):
        for j in range(n):
            if P[i, j] > 0:
                pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], color='C2',
                        alpha=P[i, j] * 0.3)
    pl.scatter(xs[:, 0], xs[:, 1], c='C0', alpha=0.2)
    pl.scatter(xt[:, 0], xt[:, 1], c='C1', alpha=0.2)
    pl.scatter(xs[:, 0], xs[:, 1], c='C0', s=P.sum(1).ravel() * (1 + p) * 2,
               label='Re-weighted source', alpha=1)
    pl.scatter(xt[:, 0], xt[:, 1], c='C1', s=P.sum(0).ravel() * (1 + p) * 2,
               label='Re-weighted target', alpha=1)
    pl.plot([], [], color='C2', alpha=0.8, label='OT plan')
    pl.title(r'$\ell_2$ UOT $\gamma$={}'.format(selected_gamma[p]),
             fontsize=11)
    if p < 2:
        pl.xticks(())
pl.show()

Animation of the regpath for UOT l2

nv = 100
g_list_v = np.logspace(-.5, -2.5, nv)

pl.figure(3)


def _update_plot(iv):
    pl.clf()
    tp = ot.regpath.compute_transport_plan(g_list_v[iv], g_list,
                                           t_list)
    P = tp.reshape((n, n))
    if P.sum() > 0:
        P = P / P.max()
    for i in range(n):
        for j in range(n):
            if P[i, j] > 0:
                pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], color='C2',
                        alpha=P[i, j] * 0.5)
    pl.scatter(xs[:, 0], xs[:, 1], c='C0', alpha=0.2)
    pl.scatter(xt[:, 0], xt[:, 1], c='C1', alpha=0.2)
    pl.scatter(xs[:, 0], xs[:, 1], c='C0', s=P.sum(1).ravel() * (1 + p) * 4,
               label='Re-weighted source', alpha=1)
    pl.scatter(xt[:, 0], xt[:, 1], c='C1', s=P.sum(0).ravel() * (1 + p) * 4,
               label='Re-weighted target', alpha=1)
    pl.plot([], [], color='C2', alpha=0.8, label='OT plan')
    pl.title(r'$\ell_2$ UOT $\gamma$={:1.3f}'.format(g_list_v[iv]),
             fontsize=11)
    return 1


i = 0
_update_plot(i)

ani = animation.FuncAnimation(pl.gcf(), _update_plot, nv, interval=50, repeat_delay=2000)

Once Loop Reflect