Note

Go to the end to download the full example code.

How to sample with MYULA#

The recommended way to define a posterior distribution in CUQIpy is to use the JointDistribution class to define the joint distribution of the parameters and the data and then condition on observed data to obtain the posterior distribution as shown in the examples below.

import cuqi
import numpy as np
from cuqi.implicitprior import RestorationPrior, MoreauYoshidaPrior
from cuqi.experimental.mcmc import ULA, MYULA
from cuqi.distribution import Posterior
import matplotlib.pyplot as plt

from skimage.metrics import normalized_root_mse as nrmse
from skimage.metrics import mean_squared_error as mse

A simple Bayesian inverse problem#

Consider a deconvolution inverse problem given by

\[\mathbf{y} = \mathbf{A}\mathbf{x}.\]

See Deconvolution1D for more details.

A, y_obs, info = cuqi.testproblem.Deconvolution1D().get_components()

Principles behind MYULA#

The goal is to solve this inverse problem by sampling from the posterior distribution given by \(\pi(x|y) \propto \pi(x) \pi(y|x)\). We assume a Gaussian likelihood, ie \(- \log \pi(y|x) = \|Ax-y \|_2^2/2 \texttt{sigma2}\) and a prior such that \(- \log \pi (x) = g(x)\) with \(g\) convex. To sample from \(\pi(x|y)\), we are going to apply a ULA based algorithm, MYULA (https://arxiv.org/pdf/1612.07471). We recall that ULA

\[x_{k+1} = x_k + \texttt{scale} \nabla \log \pi(x_k |y) + \sqrt{2 \texttt{scale}} z_{k+1}\]

\[x_{k+1} = x_k + \texttt{scale} \nabla \log \pi(y | x_k) + \texttt{scale} \nabla \log \pi(x_k) + \sqrt{2 \texttt{scale}} z_{k+1}\]

with \((z_k)_{k \in \mathbb{N}^*}\) a sequence of independent and identically distributed Gaussian random variables with zero mean and identity covariance.

In the case where \(\log \pi(x)\) is not differentiable we can unfortunately not apply ULA. The idea is to consider a surrogate posterior density \(\pi_{\texttt{smoothing_strength}} (x|y) \propto \pi(y|x) \pi_{\texttt{smoothing_strength}} (x)\) where

\[\pi_{\texttt{smoothing_strength}}(x) \propto \exp(- g_{\texttt{smoothing_strength}} (x))\]

and \(g_{\texttt{smoothing_strength}}\) is the \(\texttt{smoothing_strength}\)-Moreau envelope of \(g\), ie

\[g_\texttt{smoothing_strength}(x) = \operatorname{inf}_z \| x- z \|_2^2/2\texttt{smoothing_strength} + g(z).\]

\(g_{\texttt{smoothing_strength}}\) is continuously differentiable with \(1/\texttt{smoothing_strength}\)-Lipschitz gradient and s.t

\[\nabla g_{\texttt{smoothing_strength}} (x) = (x- \operatorname{prox}_g^{\texttt{smoothing_strength}} (x))/\texttt{smoothing_strength}\]

with

\[\operatorname{prox}_g^{\texttt{smoothing_strength}} (x) = \operatorname{argmin}_z \|x-z \|_2^2/2\texttt{smoothing_strength} + g(z)\]

See https://link.springer.com/chapter/10.1007/978-3-319-48311-5_31 for more details.

MYULA consists in applying ULA to a smoothed target distribution. It reads

\[\begin{split}\begin{align*} x_{k+1} &= x_k + \texttt{scale} \nabla \log \pi_{\texttt{smoothing_strength}}(x_k |y) + \sqrt{2 \texttt{scale}} z_{k+1}\\ &= x_k + \texttt{scale} \nabla \log \pi(y | x_k) + \texttt{scale} \nabla \log \pi_{\texttt{smoothing_strength}}(x_k) + \sqrt{2 \texttt{scale}} z_{k+1}\\ &= x_k + \texttt{scale} \nabla \log \pi(y | x_k) - \texttt{scale} (x_k - \operatorname{prox}_g^{\texttt{smoothing_strength}} (x_k))/{\texttt{smoothing_strength}} + \sqrt{2 \texttt{scale}} z_{k+1}. \end{align*}\end{split}\]

where \(\texttt{smoothing_strength}\) corresponds to the smoothing strength of \(g\).

To illustrate MYULA, we will consider \(g(x) = \texttt{regularization_strength} \ TV(x) = \texttt{regularization_strength} \|\nabla x \|_{2, 1}\), where \(\texttt{regularization_strength}\) is the regularization parameter which controls the regularization strength induced by TV.

Bayesian model definition#

Then consider the following Bayesian Inverse Problem:

\[\begin{split}\begin{align*} \mathbf{x} &\sim \exp (- \texttt{regularization_strength} \|\nabla x \|_{2,1})\\ \mathbf{y}_{obs} &\sim \mathcal{N}(\mathbf{A}\mathbf{x}, \texttt{sigma2}\,\mathbf{I}) \ , \end{align*}\end{split}\]

with \(\texttt{sigma2}=0.05^2\).

Likelihood definition#

We first specify the data distribution as follows:

sigma2 = 0.05**2
y = cuqi.distribution.Gaussian(A, sigma2)

Then we can define the likelihood with

likelihood = y(y=y_obs)

RestorationPrior and MoreauYoshidaPrior#

To apply MYULA, we need to define the Moreau-Yoshida prior \(\pi_{\texttt{smoothing_stength}}(x)\). Evaluating this surrogate prior is doable but too intensive from a computational point of view as it requires to solve an optimization problem. However to apply MYULA, we only require access to \(\operatorname{prox}_{\texttt{regularization_strength}\ TV}^{\texttt{smoothing_strength}}\). \(\operatorname{prox}_{\texttt{regularization_strength}\ TV}^{\texttt{smoothing_strength}}\) is a denoising operator (also called denoiser), which takes a signal as input and returns a less noisy signal. In CUQIPy, we talk about restoration operators (also called restorators). Denoisers are an example of restorators. Restorators are at the core of a specific type of priors called RestorationPrior. We cannot sample from these priors but they allow us to define other types of priors.

RestorationPrior definition#

A restorator, is associated with a parameter called \(\texttt{restoration_strength}\). This parameter indicates how strong is the restoration. For example, when this restorator is a denoiser, an operator taking an signal as input and returning a less noisy signal, \(\texttt{restoration_strength}\) can correspond to the denoising level. In the following, we consider the denoiser \(\operatorname{prox}_{\texttt{regularization_strength}\ TV}^{\texttt{restoration_strength}}\). We use the implementation provided by Scikit-Image. But we can use any solver to compute this quantity. We emphasize that we have for any \(g\)

\[\operatorname{prox}_{\texttt{regularization_strength}\ g}^{\texttt{smoothing_strength}} = \operatorname{prox}_{g}^{\texttt{weight}} ,\]

with \(\texttt{weight} = \texttt{regularization_strength} \times \texttt{smoothing_strength}\).

regularization_strength = 10
restoration_strength = 0.5 * sigma2
from skimage.restoration import denoise_tv_chambolle


def prox_g(x, regularization_strength=None, restoration_strength=None):
    weight = regularization_strength * restoration_strength
    return denoise_tv_chambolle(x, weight=weight, max_num_iter=100), None

We save all the important variables into the variable \(\texttt{restorator_kwargs}\).

restorator_kwargs = {}
restorator_kwargs["regularization_strength"] = regularization_strength

Now we can define our RestorationPrior.

restorator = RestorationPrior(
    prox_g,
    restorator_kwargs=restorator_kwargs,
    geometry=likelihood.model.domain_geometry,
)

We first apply the restorate method of our denoiser to \(\mathbf{y}_{obs}\). This operator should restore \(\mathbf{y}_{obs}\) and generate a signal close to \(\mathbf{A}\mathbf{x}\).

res = restorator.restore(y_obs, restoration_strength)

In this cell, we show the effect of the restorator both from a visual and quantitative point of view. We use the relative error and the mean-squared error. The smaller are these quantities, the better it is.

plt.figure(figsize=(10, 10))
y_obs.plot(
    label="observation (Relative error={:.5f})".format(nrmse(info.exactData, y_obs))
)
res.plot(label="restoration (Relative error={:.5f})".format(nrmse(info.exactData, res)))
info.exactData.plot(label="groundtruth")
plt.legend()

print(
    "MSE(Ax, y_obs) is ",
    mse(info.exactData, y_obs) / mse(info.exactData, res),
    " times larger than MSE(Ax, res).",
)

MSE(Ax, y_obs) is  2.678588923130995  times larger than MSE(Ax, res).

Definition of the Moreau-Yoshida prior#

It is a smoothed version from the target prior. Its definition requires a prior of type RestorationPrior and a scalar parameter \(\texttt{smoothing_strength}\) which controls the strength of the smoothing. We must have \(\texttt{smoothing_strength}=\texttt{restoration_strength}\).

As suggested by Durmus et al. (https://arxiv.org/pdf/1612.07471), we set the smoothing parameter \(\texttt{smoothing_strength} \approx \texttt{sigma2}\), ie \(\texttt{smoothing_strength}= 0.5 \ \texttt{sigma2}\).

myprior = MoreauYoshidaPrior(prior=restorator, smoothing_strength=restoration_strength)

Implicitly defined posterior distribution#

We can now define the implicitly defined smoothed posterior distribution as follows:

smoothed_posterior = Posterior(likelihood, myprior)

Parameters of the MYULA sampler#

We let run MYULA for \(\texttt{Ns}=10^4\) iterations. We discard the \(\texttt{Nb}=1000\) first burn-in samples of the Markov chain. Furthermore, as MCMC methods generate correlated samples, we also perform a thinning: we only consider 1 samples every \(\texttt{Nt}=20\) samples to compute our quantities of interest. \(\texttt{scale}\) is set wrt the recommendation of Durmus et al. (https://arxiv.org/pdf/1612.07471). It must be smaller than the inverse of the Lipschitz constant of the gradient of the log-posterior density. In this setting, The Lipschitz constant of the gradient of likelihood log-density is \(\|A^TA \|_2^2/\texttt{sigma2}\) and the one of the log-prior is \(1/\texttt{smoothing_strength}\).

Ns = 10000
Nb = 1000
Nt = 20
# Step-size of MYULA
scale = 0.9 / (1 / sigma2 + 1 / restoration_strength)

In order to get reproducible results, we set the seed parameter to 0.

np.random.seed(0)

ULA sampler#

Definition of the ULA sampler which aims at sampling from the smoothed posterior.

ula_sampler = ULA(target=smoothed_posterior, scale=scale)

Sampling with ULA from the smoothed target posterior.

ula_sampler.sample(Ns=Ns)

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Sampler: ULA
 Target:
     Posterior(
    Equation:
     p(None|y) ∝ L(None|y)p(None)
    Densities:
    y ~ CUQI Gaussian Likelihood function. Parameters ['x'].
    None ~ CUQI MoreauYoshidaPrior.
 )
 Current state:
     current_point: [ 0.26959595  0.28805556  0.21900513  0.17745268  0.20982209  0.29381254
  0.3225685   0.29484028  0.00749467  0.25224436 -0.14943859 -0.02199037
 -0.05068024 -0.08491722 -0.32409728 -0.18177866 -0.24304668 -0.18090171
 -0.34663305 -0.26650029 -0.19409893  0.04682783  0.14650357  0.18078335
  0.3173639   0.35133372  0.16776523  0.25854342  0.31571516  0.18300628
 -0.06180181 -0.13237555  0.08728485 -0.16163948 -0.06350261 -0.0957537
 -0.17099896 -0.22665902 -0.13937368 -0.28919086 -0.06020695  0.03370317
  0.05895713  0.02133216 -0.17153663 -0.16075283 -0.17003814 -0.10410509
  0.03110535  0.08652787  0.17640484  0.34869425  0.33752583  0.10757776
  0.46316809  0.48342557  0.53261257  0.56255649  0.49533486  0.53415702
  0.5892304   0.54979957  0.67466642  0.89084248  0.83107253  0.78923318
  0.70425537  0.80557699  0.72718545  0.9905476   0.95124696  0.9088791
  0.49527008  0.1129193   0.0433889   0.0277241   0.01432876  0.01967801
  0.02023923 -0.08458431  0.0856739  -0.02657085  0.1356114   0.02383313
  0.04523565 -0.00523621 -0.06013594 -0.09686712 -0.02977352 -0.23326135
 -0.28629351 -0.05598171 -0.22960653 -0.06412503  0.02392903  0.32658756
  0.06318032 -0.01963858 -0.10241153 -0.1741789  -0.13642925 -0.17036851
 -0.16301887 -0.11423767  0.0678934   0.01636863  0.24905501  0.30204475
  0.29270519  0.44529988  0.36536459  0.26059073  0.15742518 -0.10992124
 -0.09378136 -0.19074705 -0.22763376 -0.11306765 -0.05058491 -0.22839122
 -0.19596032 -0.29693095 -0.11026662  0.11379565  0.01321443  0.01932809
 -0.16614513 -0.02995388]
     current_target_grad: [-1.04843058e+00 -1.57820399e+01 -3.32716949e+00  1.64286187e+01
 -3.05938361e+00 -3.60045521e+00 -2.23343603e+01 -3.54959865e+00
  1.69797312e+01 -2.29342661e+01  1.71616159e+01 -2.21859887e+01
 -3.15809038e+00 -2.49442625e+00  1.76461237e+01 -2.21334661e+01
  1.78081646e+01 -2.18284114e+01  1.82778963e+01 -1.54644713e+00
 -1.36706408e+00 -1.18532733e+00 -1.00687009e+00 -8.20284902e-01
 -8.25907003e-01 -2.03051306e+01  1.96745275e+01 -1.85245185e-01
 -2.00507899e+01  5.59070921e-02  1.47126029e-01  2.02161912e+01
 -1.97338495e+01  2.02948788e+01 -1.94465127e+01  4.95399202e-02
  2.80095148e-01  2.02382502e+01 -1.97948190e+01  2.01620353e+01
  1.22108394e-01 -7.69242081e-01 -1.90175645e+01  1.32262149e-03
  9.99723123e+00  1.41087470e+00  8.91681662e+00  2.29822275e-01
  3.25901814e-01  4.42065189e-01  5.73226946e-01 -1.37502059e+01
 -4.66964088e+00  2.10122506e+01  1.12093486e+00  1.31028083e+00
  9.96697323e-01 -1.81358773e+01  2.14138942e+01  1.56607216e+00
 -1.43175940e+01  1.71367614e+01  1.21800235e+00 -1.89731245e+01
  7.81108636e-01  4.93671999e-01  2.01668426e+01 -2.01900110e+01
  1.94132124e+01 -2.09183899e+01 -1.47038392e+00 -1.81535392e+00
 -2.21459012e+00 -2.59379514e+00 -2.82534867e+00 -2.78212960e+00
  5.85715080e-01 -5.14796577e+00 -7.17480660e+00  1.60002279e+01
 -2.40483796e+01  1.59570586e+01 -2.39864615e+01  4.67974358e+00
 -1.23055768e+01 -3.57161913e+00 -3.27461116e+00  1.67135442e+01
 -2.29238670e+01 -2.72860882e+00  1.74600630e+01 -2.23522384e+01
  1.78036237e+01 -2.07148402e+00 -1.98134279e+00 -2.19283622e+01
 -1.91330149e+00 -1.93557895e+00 -1.99280953e+00  1.39475719e+01
 -1.64418425e+01  9.73547749e+00  3.63884187e+00 -2.66251954e+00
 -2.21890523e+01  1.63455197e+01 -3.17308773e+00 -7.15632826e+00
  3.65580923e-01 -2.35857705e+01 -3.68692860e+00 -3.76638563e+00
 -3.82483400e+00  2.59504923e+00 -1.03397843e+01 -3.78404175e+00
  1.60276925e+01 -3.85020864e+00 -2.38148120e+01  9.19414360e+00
 -1.67089839e+01  1.63061341e+01 -3.65116503e+00 -2.36100167e+01
 -8.74586566e-01 -6.23088788e+00  1.65004438e+01 -1.34659644e+01]
     scale: 0.0007500000000000001

Retrieve the samples. We apply the burnin and perform thinning to the Markov chain.

samples = ula_sampler.get_samples()
samples_warm = samples.burnthin(Nb=Nb, Nt=Nt)

Results#

Mean and CI plot.

plt.figure(figsize=(10, 10))
y_obs.plot(label="Observation")
samples_warm.plot_ci(exact=info.exactSolution)
plt.legend()

<matplotlib.legend.Legend object at 0x7f2c28ea2890>

Standard deviation plot to estimate the uncertainty.

plt.figure(figsize=(10, 10))
samples_warm.plot_std()

[<matplotlib.lines.Line2D object at 0x7f2c28fd4c10>]

Samples autocorrelation plot.

samples_warm.plot_autocorrelation(max_lag=100)

Selecting 5 randomly chosen variables

array([[<Axes: title={'center': 'v26'}>, <Axes: title={'center': 'v60'}>,
        <Axes: title={'center': 'v63'}>],
       [<Axes: title={'center': 'v73'}>,
        <Axes: title={'center': 'v108'}>, <Axes: >]], dtype=object)

Other way to sample with MYULA#

To sample with MYULA, we can also define an implicit posterior with a RestorationPrior object (instead of MoreauYoshidaPrior object) and then automatically perform the Moreau-Yoshida smoothing when defining the MYULA sampler.

posterior = Posterior(likelihood, restorator)

Definition of the MYULA sampler#

Again, we must have \(\texttt{smoothing_strength}=\texttt{restoration_strength}\).

myula_sampler = MYULA(
    target=posterior, scale=scale, smoothing_strength=restoration_strength
)

We then sample using the MYULA sampler. It targets the same smoothed distribution as the ULA sampler applied with the smoothed posterior distribution. If the samples generated by ULA and MYULA are the same, then a message “MYULA samples of the posterior are the same ULA samples from the smoothed posterior”

np.random.seed(0)
myula_sampler.sample(Ns=Ns)
samples_myula = myula_sampler.get_samples()
samples_myula_warm = samples_myula.burnthin(Nb=Nb, Nt=Nt)
assert np.allclose(samples_warm.samples, samples_myula_warm.samples)
print(
    "MYULA samples of the posterior are the same ULA samples from the smoothed \
posterior"
)

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MYULA samples of the posterior are the same ULA samples from the smoothed posterior

Total running time of the script: (0 minutes 20.161 seconds)

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