R/model2DE_cluster.R

In leylabmpi/endoR: Interpret and Visualize Stable Tree Ensemble Models

#' Run model2DE on several bootstrap resamples in parallel.
#'
#' Function to with the Q() function from the clustermq R-package with the following arguments in export:
#' data, target, exec, classPos, dummy_var, prune, maxDecay, typeDecay, filter, in_parallel, n_cores.
#' See preCluster() to obtain the list of boostraps resamples, the discretized data and exec dataframe with decisions.
#'
#' @param partition a vector with row numbers to subset data.
#' @example examples/iris_bootstraps.R
#' @export
model2DE_cluster <- function(partition) {
  #library(data.table)
  res <- model2DE(
    data = data[partition, ], target = target[partition],
    exec = exec,
    classPos = classPos, dummy_var = dummy_var,
    prune = prune, maxDecay = maxDecay, typeDecay = typeDecay,
    filter = filter, min_imp = 1,
    in_parallel = in_parallel, n_cores = n_cores,
    light = TRUE
  )

  # get the position of the last set of computed rules
  tmp <- str_which(names(res), pattern = 'rules')
  tmp <- tmp[length(tmp)]

  res <- list(
    "pdecisions" = res$n_decisions,
    "rules" = res$rules,
    "nodes_agg" = res$nodes_agg, "edges_agg" = res$edges_agg
  )
  return(res)
}