attic/createOpenMLImplementationForMLRLearner.R

In openml/r: Open Machine Learning and Open Data Platform

#' createOpenMLImplementationForMlrLearner.
#'
#' Create an OpenML implementation description object for an mlr learner.
#' Required if you want to upload an mlr learner.
#'
#' @param lrn [\code{\link[mlr]{Learner}}]\cr
#'   The mlr learner.
#' @param name [\code{character(1)}]\cr
#'   The name of the implementation object. Default is the learner's ID.
#' @param description [\code{character(1)}]\cr
#'   An optional description of the learner.
#'   Default is a short specification of the learner and the associated package.
#' @param ... [\code{any}]\cr
#'   Further optional parameters that are passed to \code{\link{makeOpenMLImplementation}}.
#' @return [\code{\link{OpenMLImplementation}}].
#' @export
createOpenMLImplementationForMlrLearner = function(lrn, name = lrn$id, description, ...) {
  assertClass(lrn, "Learner")
  assertString(name)

  if (!missing(description))
    assertString(description)
  else
    description = sprintf("Learner %s from package(s) %s.", name, collapse(lrn$package, sep = ", "))

  impl = makeOpenMLImplementation(
    name = name,
    # set package version of the "last" learner as the flow's external.version
    external.version = packageDescription(lrn$package[1L])$Version,
    description = description,
    parameter = makeImplementationParameterList(lrn),
    ...
  )
  if (!is.null(lrn$next.learner)) {
    identifier = str_split(lrn$next.learner$id, '[.]')[[1L]][2L]
    impl$components = list(createOpenMLImplementationForMlrLearner(lrn$next.learner))
    names(impl$components) = identifier
  }

  return(impl)
}