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#' @title Convert a machine learning benchmark / demo object from package mlbench to a task.
#'
#' @description
#' We auto-set the target column, drop any column which is called \dQuote{Id} and
#' convert logicals to factors.
#'
#' @param x (`character(1)`)\cr
#' Name of an mlbench function or dataset.
#' @param n (`integer(1)`)\cr
#' Number of observations for data simul functions.
#' Note that for a few mlbench function this setting is not exactly respected by mlbench.
#' Default is 100.
#' @param ... (any)\cr
#' Passed on to data simul functions.
#' @export
#' @examples
#' \dontshow{ if (requireNamespace("mlbench")) \{ }
#' print(convertMLBenchObjToTask("Ionosphere"))
#' print(convertMLBenchObjToTask("mlbench.spirals", n = 100, sd = 0.1))
#' \dontshow{ \} }
convertMLBenchObjToTask = function(x, n = 100L, ...) {
assertString(x)
requirePackages("mlbench")
id = x
datasets = data(package = "mlbench")
datasets = datasets$results[, "Item"]
targets = c(
Soybean = "Class",
BostonHousing = "medv",
BostonHousing2 = "medv",
BreastCancer = "Class",
DNA = "Class",
Glass = "Type",
HouseVotes84 = "Class",
Ionosphere = "Class",
LetterRecognition = "lettr",
Ozone = "V4",
PimaIndiansDiabetes = "diabetes",
PimaIndiansDiabetes2 = "diabetes",
Satellite = "classes",
Servo = "Class",
Shuttle = "Class",
Sonar = "Class",
Soybean = "Class",
Vehicle = "Class",
Vowel = "Class",
Zoo = "type"
)
if (x %in% datasets) {
# we load a data set
ee = new.env()
data(list = x, envir = ee)
d = ee[[x]]
d$Id = NULL
target = targets[[x]]
d = convertDfCols(d, logicals.as.factor = TRUE)
} else {
x = getFromNamespace(x, "mlbench")
n = asCount(n)
z = x(n = n, ...)
d = as.data.frame(z)
target = if (!is.null(z$classes)) "classes" else "y"
}
task = if (is.factor(d[, target])) {
makeClassifTask(id = id, data = d, target = target)
} else {
makeRegrTask(id = id, data = d, target = target)
}
return(task)
}
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