SPOT (version 2.11.10)

spot: spot

Description

Sequential Parameter Optimization. This is one of the main interfaces for using the SPOT package. Based on a user-given objective function and configuration, spot finds the parameter setting that yields the lowest objective value (minimization). To that end, it uses methods from the fields of design of experiment, statistical modeling / machine learning and optimization.

Usage

spot(x = NULL, fun, lower, upper, control = list(), ...)

Arguments

x

is an optional start point (or set of start points), specified as a matrix. One row for each point, and one column for each optimized parameter.

fun

is the objective function. It should receive a matrix x and return a matrix y. In case the function uses external code and is noisy, an additional seed parameter may be used, see the control$seedFun argument below for details. Mostly, fun must have format y = f(x, ...). If a noisy function requires some specific seed handling, e.g., in some other non-R code, a seed can be passed to fun. For that purpose, the user must specify control$noise = TRUE and fun should be fun(x, seed, ...)

lower

is a vector that defines the lower boundary of search space. This determines also the dimensionality of the problem.

upper

is a vector that defines the upper boundary of search space.

control

is a list with control settings for spot. See spotControl.

...

additional parameters passed to fun.

Value

This function returns a list with:

xbest

Parameters of the best found solution (matrix).

ybest

Objective function value of the best found solution (matrix).

x

Archive of all evaluation parameters (matrix).

y

Archive of the respective objective function values (matrix).

count

Number of performed objective function evaluations.

msg

Message specifying the reason of termination.

modelFit

The fit of the last build model, i.e., an object returned by the last call to the function specified by control$model.

Examples

# NOT RUN {
## Only a few examples. More examples can be found in the vignette and in
## the paper "In a Nutshell -- The Sequential Parameter Optimization Toolbox",
## see https://arxiv.org/abs/1712.04076

## 1. Most simple example: Kriging + LHS search + predicted mean optimization
## (not expected improvement)
set.seed(1)
res <- spot(x=NULL,funSphere,c(-2,-3),c(1,2),
             control=list(funEvals=15))
res$xbest
res$ybest

## 2. With expected improvement
set.seed(1)
res <- spot(x=NULL,funSphere,c(-2,-3),c(1,2),
            control=list(funEvals=15,
                         modelControl=list(target="ei")))
res$xbest
res$ybest

### 3. Use local optimization instead of LHS search
set.seed(1)
res <- spot(,funSphere,c(-2,-3),c(1,2),
            control=list(funEvals=15,
                         modelControl=list(target="ei"),
                         optimizer=optimLBFGSB))
res$xbest
res$ybest

### 4. Use transformed input values
set.seed(1)
f2 <- function(x){2^x}
lower <- c(-100, -100)
upper <- c(100, 100)
transformFun <- rep("f2", length(lower))
res <- spot(x=NULL,funSphere,lower=lower, upper=upper,
             control=list(funEvals=15,
                          modelControl=list(target="ei"),
                          optimizer=optimLBFGSB,
                          transformFun=transformFun))
res$xbest
res$ybest

# }