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# -*- coding: utf-8 -*-
r"""
=================================
Wasserstein unmixing with PyTorch
=================================
In this example we estimate mixing parameters from distributions that minimize
the Wasserstein distance. In other words we suppose that a target
distribution :math:`\mu^t` can be expressed as a weighted sum of source
distributions :math:`\mu^s_k` with the following model:
.. math::
\mu^t = \sum_{k=1}^K w_k\mu^s_k
where :math:`\mathbf{w}` is a vector of size :math:`K` and belongs in the
distribution simplex :math:`\Delta_K`.
In order to estimate this weight vector we propose to optimize the Wasserstein
distance between the model and the observed :math:`\mu^t` with respect to
the vector. This leads to the following optimization problem:
.. math::
\min_{\mathbf{w}\in\Delta_K} \quad W \left(\mu^t,\sum_{k=1}^K w_k\mu^s_k\right)
This minimization is done in this example with a simple projected gradient
descent in PyTorch. We use the automatic backend of POT that allows us to
compute the Wasserstein distance with :any:`ot.emd2` with
differentiable losses.
"""
# Author: Remi Flamary <remi.flamary@polytechnique.edu>
#
# License: MIT License
# sphinx_gallery_thumbnail_number = 2
import numpy as np
import matplotlib.pylab as pl
import ot
import torch
##############################################################################
# Generate data
# -------------
# %% Data
nt = 100
nt1 = 10 #
ns1 = 50
ns = 2 * ns1
rng = np.random.RandomState(2)
xt = rng.randn(nt, 2) * 0.2
xt[:nt1, 0] += 1
xt[nt1:, 1] += 1
xs1 = rng.randn(ns1, 2) * 0.2
xs1[:, 0] += 1
xs2 = rng.randn(ns1, 2) * 0.2
xs2[:, 1] += 1
xs = np.concatenate((xs1, xs2))
# Sample reweighting matrix H
H = np.zeros((ns, 2))
H[:ns1, 0] = 1 / ns1
H[ns1:, 1] = 1 / ns1
# each columns sums to 1 and has weights only for samples form the
# corresponding source distribution
M = ot.dist(xs, xt)
##############################################################################
# Plot data
# ---------
# %% plot the distributions
pl.figure(1)
pl.scatter(xt[:, 0], xt[:, 1], label="Target $\mu^t$", alpha=0.5)
pl.scatter(xs1[:, 0], xs1[:, 1], label="Source $\mu^s_1$", alpha=0.5)
pl.scatter(xs2[:, 0], xs2[:, 1], label="Source $\mu^s_2$", alpha=0.5)
pl.title("Sources and Target distributions")
pl.legend()
##############################################################################
# Optimization of the model wrt the Wasserstein distance
# ------------------------------------------------------
# %% Weights optimization with gradient descent
# convert numpy arrays to torch tensors
H2 = torch.tensor(H)
M2 = torch.tensor(M)
# weights for the source distributions
w = torch.tensor(ot.unif(2), requires_grad=True)
# uniform weights for target
b = torch.tensor(ot.unif(nt))
lr = 2e-3 # learning rate
niter = 500 # number of iterations
losses = [] # loss along the iterations
# loss for the minimal Wasserstein estimator
def get_loss(w):
a = torch.mv(H2, w) # distribution reweighting
return ot.emd2(a, b, M2) # squared Wasserstein 2
for i in range(niter):
loss = get_loss(w)
losses.append(float(loss))
loss.backward()
with torch.no_grad():
w -= lr * w.grad # gradient step
w[:] = ot.utils.proj_simplex(w) # projection on the simplex
w.grad.zero_()
##############################################################################
# Estimated weights and convergence of the objective
# --------------------------------------------------
we = w.detach().numpy()
print("Estimated mixture:", we)
pl.figure(2)
pl.semilogy(losses)
pl.grid()
pl.title("Wasserstein distance")
pl.xlabel("Iterations")
##############################################################################
# Plotting the reweighted source distribution
# -------------------------------------------
pl.figure(3)
# compute source weights
ws = H.dot(we)
pl.scatter(xt[:, 0], xt[:, 1], label="Target $\mu^t$", alpha=0.5)
pl.scatter(
xs[:, 0],
xs[:, 1],
color="C3",
s=ws * 20 * ns,
label="Weighted sources $\sum_{k} w_k\mu^s_k$",
alpha=0.5,
)
pl.title("Target and reweighted source distributions")
pl.legend()
|
