R/predict_repeat_ensemble.R

In tidysdm: Species Distribution Models with Tidymodels

#' Predict for a repeat ensemble set
#'
#' Predict for a new dataset by using a repeat ensemble. Predictions from
#' individual models are combined according to `fun`
#' @param object an repeat_ensemble object
#' @param new_data a data frame in which to look for variables with which to
#'   predict.
#' @param type the type of prediction, "prob" or "class".
#' @param fun string defining the aggregating function. It can take values
#'   `mean`, `median`, `weighted_mean`, `weighted_median` and `none`. It is
#'   possible to combine multiple functions, except for "none". If it is set to
#'   "none", only the individual member predictions are returned (this
#'   automatically sets `member` to TRUE)
#' @param metric_thresh a vector of length 2 giving a metric and its threshold,
#'   which will be used to prune which models in the ensemble will be used for
#'   the prediction. The 'metrics' need to have been computed when the workflow
#'   was tuned. Examples are c("accuracy",0.8) or c("boyce_cont",0.7)
#' @param class_thresh probability threshold used to convert probabilities into
#'   classes. It can be a number (between 0 and 1), or a character metric
#'   (currently "tss_max" or "sensitivity"). For sensitivity, an additional
#'   target value is passed along as a second element of a vector, e.g.
#'   c("sensitivity",0.8).
#' @param members boolean defining whether individual predictions for each
#'   member should be added to the ensemble prediction. The columns for
#'   individual members have the name of the workflow a a prefix, separated by
#'   "." from the usual column names of the predictions.
#' @param ... not used in this method.
#' @returns a tibble of predictions
#' @method predict repeat_ensemble
#' @export
#' @keywords predict

predict.repeat_ensemble <-
  function(object,
           new_data,
           type = "prob",
           fun = "mean",
           metric_thresh = NULL,
           class_thresh = NULL,
           members = FALSE,
           ...) {
    # we change the names of the workflows to combine with the repeat ids
    object$workflow_id <- paste(object$rep_id, object$wflow_id, sep = ".")
    class(object)[1] <- "simple_ensemble"
    # now predict the object as if it was a simple ensemble
    stats::predict(
      object = object,
      new_data = new_data,
      type = type,
      fun = fun,
      metric_thresh = metric_thresh,
      class_thresh = class_thresh,
      members = members
    )
  }