Source

BayesOpt / doxygen / models.dox

/*! \page modelopt Models and functions
\tableofcontents

This library was originally developed for as part of a robotics
research project \cite MartinezCantin09AR \cite MartinezCantin07RSS,
where a Gaussian process with hyperpriors on the mean and signal
covariance parameters. Then, the metamodel was constructed using the
Maximum a Posteriory (MAP) of the parameters. By that time, it only
supported one kernel function, one mean function and one criterion.

However, the library now has grown to support many more surrogate
models, with different distributions (Gaussian processes,
Student's-t processes, etc.), with many kernels and mean
functions. It also provides different criteria (even some combined
criteria) so the library can be used to any problem involving some
bounded optimization, stochastic bandits, active learning for
regression, etc.

\section surrmod Surrogate models

As seen in Section \ref modopt this library implements only one
general regression model. However, we can assign a set of priors on
the parameters of the model \f$\mathbf{w}\f$, \f$\sigma_s^2\f$ (the
kernel hyperparameter will be discussed in Section \ref
learnker). Thus, the options are:

\li "sGaussianProcess": a standard Gaussian process where the
hyperparameters are known.
\li "sGaussianProcessML": a standard Gaussian process where the
hyperparameters are estimated directly from data using maximum
likelihood estimates.
\li "sGaussianProcessNormal": a Gaussian process with a Normal 
prior on the mean function parameters \f$\mathbf{w}\f$ and known 
\f$\sigma_s^2\f$.
\li "sStudentTProcessJef": in this case we use the Jeffreys prior 
for \f$\mathbf{w}\f$ and \f$\sigma_s^2\f$. This is a kind of 
uninformative prior which is invariant to reparametrizations. Once
we set a prior on \f$\sigma_s^2\f$ the posterior becomes a Student's
t Process.
\li "sStudentTProcessNIG": in this case we standard conjugate priors,
that is, a Normal prior on \f$\mathbf{w}\f$ and a Inverse Gamma on 
\f$\sigma_s^2\f$. Therefore, the posterior is again a Student's t process.

Gaussian processes are a very general model that can achieve good
performance with a reasonable computational cost. However, Student's t
processes, thanks to the hierarchical structure of priors, provide an
even more general setup for a minor extra cost. Furthermore, the
Student's t distribution is robust to outliers and heavy tails in the
data.

\section kermod Kernel (covariance) models

One of the critical components of Gaussian and Student's t processes
is the definition of the kernel function, which defines the
correlation between points in the input space. As a correlation
function, the kernel must satisfy a set of properties (e.g.: being
positive definite). All the kernel models available and its
combinations satisfy the kernel restrictions.

The functions with \b "ISO" in their name are \a isotropic function,
that is, they share a single set of parameters for all the dimensions
of the input space.

The functions with \b "ARD" in their name use <em>Automatic Relevance
Determination</em>, that is, they use independent parameters for every
dimension of the problem. Therefore, they can be use to find the \a
relevance of the each feature in the input space. In the limit, this
can be used for feature selection.

\subsection singker Atomic kernels
\li "kConst": a simple constant function.
\li "kLinear", "kLinearARD": a linear function.
\li "kMaternISO1",
"kMaternISO3","kMaternISO5","kMaternARD1","kMaternARD3","kMaternARD5":
Matern kernel functions. The number divided by 2 represents the order
of the function. See \cite Rasmussen:2006 for a description.
\li "kPolyX": Polynomial kernel function. X is a number 1-6 which
represents the exponent of the function.
\li "kSEARD","kSEISO": Squared exponential kernel, also known as
Gaussian kernel.
\li "kRQISO": Rational quadratic kernel, also known as Student's t
kernel.

\subsection combker Binary kernels
This kernels allow to combine some of the previous kernels.
\li "kSum": Sum of kernels.
\li "kProd": Product of kernels.

Note that the binary kernels only admits two terms. However, we can
combine them for more complex operations. For example if we write:

"kSum(kMaternISO3,kSum(kRQISO,kProd(kPoly4,kConst))"

it represents the expresion: Matern(3) + RationalQuadratic + C*Polynomial^4 

In this case, the vector of parameters is splited from left to right:
1 for the Matern function, 2 for the RQ function, 2 for polynomial
function and 1 for the constant. If the vector of parameters have more
or less than 6 elements, the system complains.

\section parmod Parametric (mean) functions

Although the nonparametric process is able to model a large amount of
funtions, we can model the expected value of the nonparametric process
as a parametric function. This parametric model will help to capture
large offsets and global trends. 

The usage is analogous to the kernel functions.

\li "mZero","mOne","mConst": constant functions. For simplicity and
because they are largely used, we provide special cases f(x) = 0 and
f(x) = 1.
\li "mLinear": linear function.
\li "mSum": binary function which can be used to combine other functions.

\section critmod Selection criteria

As discussed in \ref introbopt, one of the critical aspects for
Bayesian optimization is the decision (loss) function. Unfortunately,
the functions described there are unavailable, because they assume
knowledge of the optimal value \f$x^*\f$. However, we can define proxies for
those functions.

Some criteria, such as the expected improvement and the lower
confidence bound admits an annealed version "cXXXa". In that version,
the parameter that is used to trade off exploration and exploitation
changes over time to priorize exploration at the begining and
exploitation at the end.

Many criteria depends on the prediction function, which can be a
Gaussian or a Student's t distribution, depending on the surrogate
model. However, the library includes all the criteria for both
distributions, and the system automatically selected the correct one.

\subsection atomcri Atomic criteria

\li "cEI","cBEI","cEIa": The most extended and reliable algorithm is
the Expected Improvement algorithm \cite Mockus78. In this case we
provide the general version from \cite Schonlau98 which includes an
exponent to trade off exploration and exploitation "cEI". Whe also includes
a variation from \cite Mockus1989 which add a \a bias or \a threshold
to the improvement "cBEI".
\li "cLCB", "cLCBa": Another popular algorithm is the Lower Confidence
Bound (LCB), or UCB in case of maximization. Introduced by 
\cite cox1992statistical as Sequential Design for Optimization (SDO).
\li "cPOI": Probability of improvement, by \cite Kushner:1964
\li "cExpReturn","cThompsonSampling","cOptimisticSampling": This
criteria are related with the predicted return of the function. The
first one is literally the expected return of the function (mean
value). The second one is based on the Thompson sampling (drawing a
random sample from the predicted distribution). Finally, the
optimistic sampling takes the minimum of the other two (mean vs random).

\li "cAopt": This is based on the A-optimality criteria. It is the
predicted variance at the query point. Thus, this criteria is intended
for \b exploration of the input space, not for optimization.
\li "cDistance": This criteria adds a cost to a query point based on
the distance with respect to the previous evaluation. Combined with other
criteria functions, it might provide a more realistic setup for certain
applications \cite Marchant2012


\subsection combcri Combined criteria

\li "cSum","cProd": Sum and product of different criteria functions.
\li "cHedge", "cHedgeRandom": Bandit based selection of the best
criteria based on the GP-Hedge algorithm \cite Hoffman2011. It
automatically learns based on the behaviour of the criteria during the
optimization process. The original version "cHedge" uses the maximum
expected return as a \a reward for each criteria. We add a variant
"cHedgeRandom" where the \a reward is defined in terms of Thompson
sampling.

In this case, the combined criteria admits more that two
functions. For example:

"cHedge(cSum(cEI,cDistance),cLCB,cPOI,cOptimisticSampling)"

\section learnmod Methods for learning the kernel parameters  

As commented before, we consider that the prior of the kernel
hyperparameters \f$\theta\f$ --if available-- is independent of other
variables. Thus, either if we are going to use maximum likelihood,
maximum a posteriori or a fully Bayesian approach, we need to find the
likelihood function of the observed data for the parameters. Depending
on the model, this function will be a multivariate Gaussian
distribution or multivariate t distribution. In general, we present
the likelihood function up to a constant factor, that is, we remove
the terms independent of \f$\theta\f$ from the log likelihood. In
practice, wether we use ML or MAP point estimates or full Bayes MCMC
posterior, the constant factor is not needed.

We are going to consider the following algorithms to learn the kernel
hyperparameters:

\li Cross-validation (L_LOO): In this case, we try to maximize the
average predicted log probability by the <em>leave one out</em> (LOO)
strategy. This is sometimes called a pseudo-likelihood.

\li Maximum Likelihood (L_ML) For any of the models presented, one
approach to learn the hyperparameters is to maximize the likelihood of
all the parameters \f$\mathbf{w}\f$, \f$\sigma_s^2\f$ and
\f$\theta\f$. Then, the likelihood function is a multivariate Gaussian
distribution. We can obtain a better estimate if we adjust the number
of degrees of freedom, this is called <em>restricted maximum
likelihood</em>. The library automatically selects the restricted
version, if it is suitable.
\li Posterior maximum likelihood (L_MAP): In this case, the likelihood
function is modified to consider the posterior estimate of
\f$(\mathbf{w},\sigma_s^2)\f$ based on the different cases defined in
Section \ref surrmods. In this case, the function will be a
multivariate Gaussian or t distribution, depending on the kind of
prior used for \f$\sigma_s^2\f$.

\li Maximum a posteriori (L_ML or L_MAP): We can modify any of the
previous algorithms by adding a prior distribution \f$p(\theta)\f$. By
default, we add a normal prior on the kernel hyperparameters. However,
if the variance of the prior \a hp_std is invalid (<=0), then we
assume no prior. Since we assume that the hyperparameters are independent,
we can apply priors selectively only to a small set.

\section initdes Initial design methods

In order to build a suitable surrogate function, we a need a
preliminar set of samples. In Bayesian optimization this is typically
performed using alternative experimental design criteria. In this
first step, usually the main criteria is space filling. Thus, we have
implemented the subsequent designs:

\li Latin hypercube sampling: Each dimension of the space is divided
in several intervals. Samples are then taken according to a
generalization of the Latin square
scheme. http://en.wikipedia.org/wiki/Latin_hypercube_sampling

\li Sobol sequences: It is a set of quasi-random low-discrepancy
sequences. Thus the space is sampled more evenly than with uniform
sampling. http://en.wikipedia.org/wiki/Sobol_sequence

\li Uniform sampling: The search space is sampled uniformly.

Note: Since we do not assume any struture in the set of discrete
points during discrete optimization, only uniform sampling of the
discrete set is available in that case.

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