R/LearnerOrdinal.R

In mlr-org/mlr3ordinal: Ordinal regression for mlr3

#' @title Ordinal Regression Learner
#'
#' @name LearnerOrdinal
#' @format [R6Class] object inheriting from [Learner].
#' @description
#' This Learner specializes [Learner] for ordinal regression tasks.
#'
#' @section Usage:
#' See [Learner].
#'
#' @family Learner
#' @examples
#' library(mlr3)
#' ids = mlr_learners$keys("^ordinal")
#' lrns = mlr_learners$mget(ids)
#' names(lrns)
#'
#' # get a specific learner from mlr_learners:
#' lrn = mlr_learners$get("ordinal.clm")
#' print(lrn)
NULL

#' @export
LearnerOrdinal = R6Class("LearnerOrdinal", inherit = Learner,
  public = list(
    initialize = function(id, param_set = ParamSet$new(), predict_types = "response", feature_types = character(), properties = character(), packages = character()) {
      super$initialize(id = id, task_type = "ordinal", param_set = param_set, predict_types = predict_types,
        feature_types = feature_types, properties = properties, packages = packages)
    }
  )
)

# ,
#     new_prediction = function(row_ids, truth, response = NULL, prob = NULL) {
#       row_ids = assert_row_ids(row_ids)
#       n = length(row_ids)
#       lvls = levels(truth)

#       if (!is.null(response)) {
#         response = as_factor(response, levels = lvls)
#         assert_factor(response, len = n)
#       }

#       if (!is.null(prob)) {
#         assert_matrix(prob, nrows = n, ncols = length(lvls))
#         assert_numeric(prob, lower = 0, upper = 1)
#         assert_names(colnames(prob), permutation.of = lvls)
#         if (!is.null(rownames(prob))) {
#           rownames(prob) = NULL
#         }

#         if (is.null(response)) {
#           # calculate response from prob
#           i = max.col(prob, ties.method = "random")
#           response = factor(colnames(prob)[i], levels = lvls, ordered = TRUE)
#         }
#       }

#       PredictionOrdinal$new(row_ids = row_ids, truth = truth, response = response, prob = prob)
#     }
#   )
# )