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getLearnerTable = function() {
ids = as.character(methods("makeRLearner"))
ids = ids[!stri_detect_fixed(ids, "__mlrmocklearners__")]
ids = stri_replace_first_fixed(ids, "makeRLearner.", "")
slots = c("cl", "name", "short.name", "package", "properties", "note")
tab = rbindlist(lapply(ids, function(id) {
makefuns = c("makeRLearnerClassif", "makeRLearnerMulticlass",
"makeRLearnerCluster", "makeRLearnerCostSens",
"makeRLearnerMultilabel", "makeRLearnerRegr",
"makeRLearnerSurv") # I hope I didn't forget any...
fun = getS3method("makeRLearner", id)
environment(fun) = new.env(parent = environment(fun))
for (mf in makefuns) {
environment(fun)[[mf]] = list
}
row = fun()
data.table(
id = row$cl,
name = row$name,
short.name = row$short.name,
package = list(stri_replace_first_regex(row$package, "^[!_]", "")),
properties = list(row$properties),
note = row$note %??% ""
)
}), use.names = TRUE)
# set learner type (classif, regr, surv, ...)
tab$type = vcapply(stri_split_fixed(tab$id, ".", n = 2L), head, 1L)
# check if all requirements are installed
pkgs = unique(unlist(tab$package))
pkgs = pkgs[vlapply(pkgs, function(x) length(find.package(x, quiet = TRUE)) > 0L)]
tab$installed = vlapply(tab$package, function(x) all(x %in% pkgs))
return(tab)
}
filterLearnerTable = function(tab = getLearnerTable(), types = character(0L), properties = character(0L), check.packages = FALSE) {
contains = function(lhs, rhs) all(lhs %in% rhs)
if (check.packages) {
tab = tab[tab$installed]
}
if (length(types) > 0L && !isScalarNA(types)) {
tab = tab[tab$type %in% types]
}
if (length(properties) > 0L) {
i = vlapply(tab$properties, contains, lhs = properties)
tab = tab[i]
}
return(tab)
}
#' @title Find matching learning algorithms.
#'
#' @description
#' Returns learning algorithms which have specific characteristics, e.g.
#' whether they support missing values, case weights, etc.
#'
#' Note that the packages of all learners are loaded during the search if you create them.
#' This can be a lot. If you do not create them we only inspect properties of the S3 classes.
#' This will be a lot faster.
#'
#' Note that for general cost-sensitive learning, mlr currently supports mainly
#' \dQuote{wrapper} approaches like [CostSensWeightedPairsWrapper],
#' which are not listed, as they are not basic R learning algorithms.
#' The same applies for many multilabel methods, see, e.g., [makeMultilabelBinaryRelevanceWrapper].
#'
#' @template arg_task_or_type
#' @param properties ([character])\cr
#' Set of required properties to filter for. Default is `character(0)`.
#' @param quiet (`logical(1)`)\cr
#' Construct learners quietly to check their properties, shows no package startup messages.
#' Turn off if you suspect errors.
#' Default is `TRUE`.
#' @param warn.missing.packages (`logical(1)`)\cr
#' If some learner cannot be constructed because its package is missing,
#' should a warning be shown?
#' Default is `TRUE`.
#' @param check.packages (`logical(1)`)\cr
#' Check if required packages are installed. Calls `find.package()`.
#' If `create` is `TRUE`, this is done implicitly and the value of this parameter is ignored.
#' If `create` is `FALSE` and `check.packages` is `TRUE` the returned table only
#' contains learners whose dependencies are installed.
#' If `check.packages` set to `FALSE`, learners that cannot actually be constructed because
#' of missing packages may be returned.
#' Default is `FALSE`.
#' @param create (`logical(1)`)\cr
#' Instantiate objects (or return info table)?
#' Packages are loaded if and only if this option is `TRUE`.
#' Default is `FALSE`.
#' @return ([data.frame` | `list` of [Learner]).
#' Either a descriptive data.frame that allows access to all properties of the learners
#' or a list of created learner objects (named by ids of listed learners).
#' @examples
#' \dontrun{
#' listLearners("classif", properties = c("multiclass", "prob"))
#' data = iris
#' task = makeClassifTask(data = data, target = "Species")
#' listLearners(task)
#' }
#' @export
listLearners = function(obj = NA_character_, properties = character(0L),
quiet = TRUE, warn.missing.packages = TRUE, check.packages = FALSE, create = FALSE) {
assertSubset(properties, listLearnerProperties())
assertFlag(quiet)
assertFlag(warn.missing.packages)
assertFlag(check.packages)
assertFlag(create)
UseMethod("listLearners", obj)
}
#' @export
#' @rdname listLearners
listLearners.default = function(obj = NA_character_, properties = character(0L),
quiet = TRUE, warn.missing.packages = TRUE, check.packages = FALSE, create = FALSE) {
listLearners.character(obj = NA_character_, properties, quiet, warn.missing.packages, check.packages, create)
}
#' @export
#' @rdname listLearners
listLearners.character = function(obj = NA_character_, properties = character(0L), quiet = TRUE, warn.missing.packages = TRUE, check.packages = FALSE, create = FALSE) {
if (!isScalarNA(obj)) {
assertSubset(obj, listTaskTypes())
}
tab = getLearnerTable()
if (warn.missing.packages && !all(tab$installed)) {
warningf("The following learners could not be constructed, probably because their packages are not installed:\n%s\nCheck ?learners to see which packages you need or install mlr with all suggestions.", collapse(tab[!tab$installed]$id))
}
tab = filterLearnerTable(tab, types = obj, properties = properties, check.packages = check.packages && !create)
if (create) {
return(lapply(tab$id[tab$installed], makeLearner))
}
tab$package = vcapply(tab$package, collapse)
properties = listLearnerProperties()
tab = cbind(tab, rbindlist(lapply(tab$properties, function(x) setNames(as.list(properties %in% x), properties)), use.names = TRUE))
tab$properties = NULL
setnames(tab, "id", "class")
setDF(tab)
addClasses(tab, "ListLearners")
return(tab)
}
#' @export
#' @rdname listLearners
listLearners.Task = function(obj = NA_character_, properties = character(0L),
quiet = TRUE, warn.missing.packages = TRUE, check.packages = TRUE, create = FALSE) {
task = obj
td = getTaskDesc(task)
props = character(0L)
if (td$n.feat["numerics"] > 0L) props = c(props, "numerics")
if (td$n.feat["factors"] > 0L) props = c(props, "factors")
if (td$n.feat["ordered"] > 0L) props = c(props, "ordered")
if (td$has.missings) props = c(props, "missings")
if (td$type == "classif") {
if (length(td$class.levels) == 1L) props = c(props, "oneclass")
if (length(td$class.levels) == 2L) props = c(props, "twoclass")
if (length(td$class.levels) >= 3L) props = c(props, "multiclass")
}
listLearners.character(td$type, union(props, properties), quiet, warn.missing.packages, check.packages, create)
}
#' @export
print.ListLearners = function(x, ...) {
printHead(as.data.frame(dropNamed(x, drop = "note")), ...)
}
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