R/CCConfig_Class.R

In dominikkirchhoff/CCKriging: Kriging for Mixed Continuous and Categorical Input Variables

#' @title Make Object of Class \code{CCConfig}
#'
#' @description A \code{CCConfig} object contains the configurations for building
#'   a \code{CCKriging} model, i.e., the methods and (fixed) parameters to be used.
#'
#' @param cat.type [\code{character(1)}]\cr
#'   Which method should be used for the categorical inputs?
#'   Possible choices:\cr
#'   \tabular{ll}{
#'     \dQuote{EC}: \tab Exchangeable Correlation (DEFAULT)\cr
#'     \dQuote{MC}: \tab Multiplicative Correlation\cr
#'     \dQuote{UC}: \tab Hypersphere Decomposition-Based Unrestrictive Correlation\cr
#'     \dQuote{TMC}: \tab Toeplitz Matrix Multiplication-based Correlation\cr
#'     \dQuote{GMC}: \tab General Matrix Multiplication-based Correlation
#'   }
#' @param cat.interaction [\code{logical(1)}]\cr
#'   Should interactions between categorical inputs be considered? Default is \code{TRUE}.
#'   Note that for some methods and many level combinations of the categorical inputs,
#'   this can lead to extraordinary high computational effort. See details.
#' @param cont.type [\code{character(1)}]\cr
#'   Which correlation function should be used for the continuous inputs?
#'   Possible choices:\cr
#'   \dQuote{matern} : Matern correlation function (DEFAULT)
#' @param cont.par [\code{named list}]\cr
#'   A named list of one or more fixed parameters of the correlation function. For the
#'   Matern correlation function, this would be \dQuote{nu}, which defaults to \code{2.5}.
#'   See the details for a list of parameters of different correlation functions.
#' @param info [\code{logical(1)}]\cr
#'   Should extra information be printed to the console? Default is \code{TRUE}.
#'
#' @return [\code{CCKriging}]
#' @export
makeCCConfig = function(cat.type = "EC", cont.type = "matern",
  cat.par = list(), cont.par = list(nu = 5/2), cat.interaction = TRUE, info = TRUE) {

  checkmate::assertChoice(cat.type, choices = c("EC", "MC", "UC", "GK", "CD", "TMC", "GMC"))
  checkmate::assertChoice(cont.type, choices = c("matern"))
  checkmate::assertList(cat.par, any.missing = FALSE, names = "unique")
  if (cat.type == "GMC") {
    checkmate::assertSubset(names(cat.par), choices = c("perm"))
    checkmate::assertCharacter(cat.par$perm, any.missing = FALSE)
  }
  checkmate::assertList(cont.par, any.missing = FALSE, names = "unique")
  if (cont.type == "matern") {
    checkmate::assertSubset(names(cont.par), choices = c("nu"))
    checkmate::assertNumeric(cont.par$nu, len = 1L, lower = 0.5, finite = TRUE, any.missing = FALSE)
  }
  checkmate::assertFlag(cat.interaction)
  checkmate::assertFlag(info)
  ## generate S3 object
  config = list(
    cat.type = cat.type,
    cont.type = cont.type,
    cat.par = cat.par,
    cont.par = cont.par,
    cat.interaction = cat.interaction,
    info = info
  )
  class(config) = "CCConfig"

  return(config)
}

#' @export
print.CCConfig = function(x, ...) {
  cat('\n--- CCConfig Object ---\n')
  cat.string = switch(x$cat.type,
    "EC"  = "EC - Exchangeable Correlation",
    "MC"  = "MC - Multiplicative Correlation",
    "UC"  = "UC - Hypersphere-based Unrestrictive Correlation",
    "GK"  = "GK - Gower Kriging",
    "CD"  = "CD - Categorical Distances",
    "TMC" = "TMC - Toeplitz Matrix Multiplication-based Correlation",
    "GMC" = "GMC - General Matrix Multiplication-based Correlation")
  BBmisc::catf('  CATEGORICAL   :: %s ', cat.string)
  if (x$cat.type == "GMC") {
    BBmisc::catf(
               '    perm        ::   %s', collapse(x$cat.par$perm, sep = " "))
  }
  BBmisc::catf(
               '    interaction ::   %s', x$cat.interaction)
  BBmisc::catf('  CONTINUOUS    :: %s', x$cont.type)
  if (x$cont.type == "matern") {
    BBmisc::catf(
               '    nu          ::   %s', x$cont.par$nu)
  }
  BBmisc::catf('  INFO          :: %s', ifelse(x$info, "yes", "no"))
}

## FIXME: add class CCPerm for permutations having a separate print method?
## print(matrix(as.numeric(config$cat.par$perm), byrow = TRUE, nrow = 4))