R/ResamplingSpCVEnv.R

In mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'

#' @title (blockCV) "Environmental blocking" resampling
#'
#' @template rox_spcv_env
#' @name mlr_resamplings_spcv_env
#'
#' @references
#' `r format_bib("valavi2018")`
#'
#' @importFrom stats kmeans
#' @export
#' @examples
#' \donttest{
#' if (mlr3misc::require_namespaces(c("sf", "blockCV"), quietly = TRUE)) {
#'   library(mlr3)
#'   task = tsk("ecuador")
#'
#'   # Instantiate Resampling
#'   rcv = rsmp("spcv_env", folds = 4)
#'   rcv$instantiate(task)
#'
#'   # Individual sets:
#'   rcv$train_set(1)
#'   rcv$test_set(1)
#'   intersect(rcv$train_set(1), rcv$test_set(1))
#'
#'   # Internal storage:
#'   rcv$instance
#' }
#' }
ResamplingSpCVEnv = R6Class("ResamplingSpCVEnv",
  inherit = mlr3::Resampling,

  public = list(
    #' @description
    #' Create an "Environmental Block" resampling instance.
    #'
    #' For a list of available arguments, please see [blockCV::cv_cluster].
    #' @param id `character(1)`\cr
    #'   Identifier for the resampling strategy.
    initialize = function(id = "spcv_env") {
      ps = ParamSet$new(params = list(
        ParamInt$new("folds", lower = 1L, default = 10L, tags = "required"),
        ParamUty$new("features")
      ))
      ps$values = list(folds = 10L)
      super$initialize(
        id = id,
        param_set = ps,
        label = "Spatial 'environmental blocking' resampling",
        man = "mlr3spatiotempcv::mlr_resamplings_spcv_env"
      )
    },

    #' @description
    #'  Materializes fixed training and test splits for a given task.
    #' @param task [Task]\cr
    #'  A task to instantiate.
    instantiate = function(task) {

      mlr3misc::require_namespaces(c("blockCV", "sf"))

      mlr3::assert_task(task)
      assert_spatial_task(task)
      pv = self$param_set$values

      # Set values to default if missing
      if (is.null(pv$features)) {
        pv$features = task$feature_names
      }

      groups = task$groups

      if (!is.null(groups)) {
        stopf("Grouping is not supported for spatial resampling methods")
      }

      # Remove non-numeric features, target and coordinates
      columns = task$col_info[!id %in%
        c(task$target_names, "x", "y")][type == "numeric"]

      # Check for selected features that are not in task
      diff = setdiff(pv$features, columns[, id])
      if (length(diff) > 0) {
        stopf("'spcv_env' requires numeric features for clustering.
              Feature '%s' is either non-numeric or does not exist in the data.",
          diff,
          wrap = TRUE)
      }
      columns = columns[id %in% pv$features]
      columns = columns[, id]

      data = task$data()[, columns, with = FALSE]

      instance = private$.sample(task$row_ids, data)

      self$instance = instance
      self$task_hash = task$hash
      self$task_nrow = task$nrow
      invisible(self)
    }
  ),
  active = list(
    #' @field iters `integer(1)`\cr
    #'   Returns the number of resampling iterations, depending on the
    #'   values stored in the `param_set`.
    iters = function() {
      as.integer(self$param_set$values$folds)
    }
  ),
  private = list(
    .sample = function(ids, data) {
      inds = stats::kmeans(data, centers = self$param_set$values$folds)

      data.table(
        row_id = ids,
        fold = inds$cluster,
        key = "fold"
      )
    },
    # private get funs for train and test which are used by
    # Resampling$.get_set()
    .get_train = function(i) {
      self$instance[!list(i), "row_id", on = "fold"][[1L]]
    },
    .get_test = function(i) {
      self$instance[list(i), "row_id", on = "fold"][[1L]]
    }
  )
)