R/PipeOpMode_B.R

In NADIA: NA Data Imputation Algorithms

#' @title PipeOpMode_B
#'
#' @name PipeOpMode_B
#'
#' @description
#' Impute features by their mode in approach B (independently during the training and prediction phase).
#'
#' @section Input and Output Channels:
#' Input and output channels are inherited from \code{\link{PipeOpImpute}}.
#'
#' @section Parameters:
#' The parameters include inherited from [`PipeOpImpute`], as well as: \cr
#' \itemize{
#' \item \code{id} :: \code{character(1)}\cr
#' Identifier of resulting object, default `"impute_mode_B"`.
#' }
#' @examples
#' {
#'  # Using debug learner for example purpose
#'
#'   graph <- PipeOpMode_B$new() %>>% LearnerClassifDebug$new()
#'   graph_learner <- GraphLearner$new(graph)
#'
#'   # Task with NA
#'    set.seed(1)
#'   resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))
#' }
#' @importFrom data.table .N
#' @export
PipeOpMode_B = R6::R6Class("Mode_B_imputation",
  inherit = PipeOpImpute,
  public = list(
    initialize = function(id = "impute_mode_B", param_vals = list()) {
      super$initialize(id, param_vals = param_vals, packages = "data.table", feature_types = c("factor", "integer", "logical", "numeric", "ordered"))
    }
  ),
  private = list(
    .train_imputer = function(feature, type, context) {
      NULL
    },
    .impute = function(feature, type, model, context) {
      feature_no_na = feature[!is.na(feature)]

      feature[is.na(feature)] <- data.table::data.table(feature_no_na)[, .N, by = list(feature_no_na)][get("N") == max(get("N"))]$feature_no_na[1]
      feature
    }
  )
)