Dual OT solvers for entropic and quadratic regularized OT with Pytorch

# Author: Remi Flamary <remi.flamary@polytechnique.edu>
#
# License: MIT License

# sphinx_gallery_thumbnail_number = 3

import numpy as np
import matplotlib.pyplot as pl
import torch
import ot
import ot.plot

Data generation

torch.manual_seed(1)

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)
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], marker='+', label='Source samples')
pl.scatter(Xt[:, 0], Xt[:, 1], marker='o', label='Target samples')
pl.legend(loc=0)
pl.title('Source and target distributions')

Text(0.5, 1.0, 'Source and target distributions')

Convert data to torch tensors

xs = torch.tensor(Xs)
xt = torch.tensor(Xt)

Estimating dual variables for entropic OT

u = torch.randn(n_source_samples, requires_grad=True)
v = torch.randn(n_source_samples, requires_grad=True)

reg = 0.5

optimizer = torch.optim.Adam([u, v], lr=1)

# number of iteration
n_iter = 200


losses = []

for i in range(n_iter):

    # generate noise samples

    # minus because we maximize te dual loss
    loss = -ot.stochastic.loss_dual_entropic(u, v, xs, xt, reg=reg)
    losses.append(float(loss.detach()))

    if i % 10 == 0:
        print("Iter: {:3d}, loss={}".format(i, losses[-1]))

    loss.backward()
    optimizer.step()
    optimizer.zero_grad()


pl.figure(2)
pl.plot(losses)
pl.grid()
pl.title('Dual objective (negative)')
pl.xlabel("Iterations")

Ge = ot.stochastic.plan_dual_entropic(u, v, xs, xt, reg=reg)

Iter:   0, loss=0.20204949002247302
Iter:  10, loss=-19.545724121626748
Iter:  20, loss=-30.990987796849776
Iter:  30, loss=-35.01055298550316
Iter:  40, loss=-37.28067085437584
Iter:  50, loss=-38.56571908690233
Iter:  60, loss=-39.02123573970508
Iter:  70, loss=-39.1873453193487
Iter:  80, loss=-39.28292694482585
Iter:  90, loss=-39.32018928658712
Iter: 100, loss=-39.337288170061974
Iter: 110, loss=-39.34580003000355
Iter: 120, loss=-39.35143309374103
Iter: 130, loss=-39.35459003550905
Iter: 140, loss=-39.356434516488875
Iter: 150, loss=-39.35761692260319
Iter: 160, loss=-39.35839567723641
Iter: 170, loss=-39.35893056004739
Iter: 180, loss=-39.359316934366554
Iter: 190, loss=-39.35960481807062

Plot the estimated entropic OT plan

pl.figure(3, (10, 5))
pl.clf()
ot.plot.plot2D_samples_mat(Xs, Xt, Ge.detach().numpy(), alpha=0.1)
pl.scatter(Xs[:, 0], Xs[:, 1], marker='+', label='Source samples', zorder=2)
pl.scatter(Xt[:, 0], Xt[:, 1], marker='o', label='Target samples', zorder=2)
pl.legend(loc=0)
pl.title('Source and target distributions')

Text(0.5, 1.0, 'Source and target distributions')

Estimating dual variables for quadratic OT

u = torch.randn(n_source_samples, requires_grad=True)
v = torch.randn(n_source_samples, requires_grad=True)

reg = 0.01

optimizer = torch.optim.Adam([u, v], lr=1)

# number of iteration
n_iter = 200


losses = []


for i in range(n_iter):

    # generate noise samples

    # minus because we maximize te dual loss
    loss = -ot.stochastic.loss_dual_quadratic(u, v, xs, xt, reg=reg)
    losses.append(float(loss.detach()))

    if i % 10 == 0:
        print("Iter: {:3d}, loss={}".format(i, losses[-1]))

    loss.backward()
    optimizer.step()
    optimizer.zero_grad()


pl.figure(4)
pl.plot(losses)
pl.grid()
pl.title('Dual objective (negative)')
pl.xlabel("Iterations")

Gq = ot.stochastic.plan_dual_quadratic(u, v, xs, xt, reg=reg)

Iter:   0, loss=-0.0018442196020623663
Iter:  10, loss=-19.432684976667407
Iter:  20, loss=-30.704734675782557
Iter:  30, loss=-34.653899852917014
Iter:  40, loss=-37.07142385298934
Iter:  50, loss=-38.31586367842154
Iter:  60, loss=-38.80630988859148
Iter:  70, loss=-39.03457107701675
Iter:  80, loss=-39.13937703389973
Iter:  90, loss=-39.18323238806764
Iter: 100, loss=-39.20488737118959
Iter: 110, loss=-39.21636041075263
Iter: 120, loss=-39.22280732082113
Iter: 130, loss=-39.22659265699188
Iter: 140, loss=-39.22891667846451
Iter: 150, loss=-39.23031025926827
Iter: 160, loss=-39.231130828396495
Iter: 170, loss=-39.23159207012832
Iter: 180, loss=-39.231838356108845
Iter: 190, loss=-39.231973223174855

Plot the estimated quadratic OT plan

pl.figure(5, (10, 5))
pl.clf()
ot.plot.plot2D_samples_mat(Xs, Xt, Gq.detach().numpy(), alpha=0.1)
pl.scatter(Xs[:, 0], Xs[:, 1], marker='+', label='Source samples', zorder=2)
pl.scatter(Xt[:, 0], Xt[:, 1], marker='o', label='Target samples', zorder=2)
pl.legend(loc=0)
pl.title('OT plan with quadratic regularization')

Text(0.5, 1.0, 'OT plan with quadratic regularization')