R/R_honestRFTree.R

In theo-s/Rforestry_R: Random Forests

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### Honest Random Forest Tree Constructor ###
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#' @title honstRFTree Constructor
#' @name honestRFTree-class
#' @rdname honestRFTree-class
#' @description `honestRFTree` inherits `RFTree`, which serves as a modified
#' version of `RFTree`. The major change is that when `honestRFTree` determines
#' a valid split point, it looks at whether or not it is valid for both
#' averaging and splitting dataset.
#' @exportClass honestRFTree
setClass(Class = "honestRFTree",
         contains = "RFTree")

#' @title honstRFTree Constructor
#' @name honestRFTree-constructor
#' @rdname honestRFTree-class
#' @description Create a honestRFTree by making specifc observatios as splitting
#' and averaging dataset.
#' @param x A data frame of all training predictors.
#' @param y A vector of all training responses.
#' @param mtry The number of variables randomly selected at each split point.
#' The default value is set to be one third of total number of features of the
#' training data.
#' @param nodesize The minimum observations contained in terminal nodes. The
#' default value is 5.
#' @param sampleIndex A list of the index of observations that are used as
#' averaging dataset. The index are based on the original dataset `x` and `y`
#' from forest. Essentially, `x[sampleIndex]` generates the whole splitting
#' dataset.
#' @param splitrule A string to specify how to find the best split among all
#' candidate feature values. The current version only supports `variance` which
#' minimizes the overall MSE after splitting. The default value is `variance`.
#' @param categoricalFeatureCols A list of index for all categorical data. Used
#' for trees to detect categorical columns.
#' @export honestRFTree
setGeneric(
  name = "honestRFTree",
  def = function(x,
                 y,
                 mtry,
                 nodesize,
                 sampleIndex,
                 splitrule,
                 categoricalFeatureCols) {
    standardGeneric("honestRFTree")
  }
)


#' @title honstRFTree Input Checker
#' @description Checks that the input parameters for an honest RF make sense
#' @param nodesize The minimum observations contained in terminal nodes. The
#' default value is 5.
#' @param sampleIndex A list of the index of observations that are used as
#' averaging dataset. The index are based on the original dataset `x` and `y`
#' from forest. Essentially, `x[sampleIndex]` generates the whole splitting
#' dataset.
honestRFTree_input_checker <- function(nodesize, sampleIndex) {
  if (nodesize$averagingNodeSize > length(sampleIndex$averagingSampleIndex)) {
    warning(
      "For at least one tree, the averagingNodeSize is chosen to be bigger
      than the entier data set. It is not set to be equal to the
      length of the entier data set."
    )
    nodesize$averagingNodeSize <-
      length(sampleIndex$averagingSampleIndex)
  }
  if (nodesize$splittingNodeSize > length(sampleIndex$splittingSampleIndex)) {
    warning(
      "For at least one tree, the splittingNodeSize is chosen to be bigger
      than the entier data set. It is not set to be equal to the
      length of the entier data set."
    )
    nodesize$splittingNodeSize <-
      length(sampleIndex$splittingSampleIndex)
  }
  if (length(sampleIndex$splittingSampleIndex) == 0 |
      length(sampleIndex$averagingSampleIndex) == 0) {
    stop("Either splitting or averaging set is empty.")
  }
}





#' @title honstRFTree Constructor
#' @rdname honestRFTree-honestRFTree
#' @description Create a honestRFTree by making specifc observatios as splitting
#' and averaging dataset.
#' @param x A data frame of all training predictors.
#' @param y A vector of all training responses.
#' @param mtry The number of variables randomly selected at each split point.
#' The default value is set to be one third of total number of features of the
#' training data.
#' @param nodesize The minimum observations contained in terminal nodes. The
#' default value is 5.
#' @param sampleIndex A list of the index of observations that are used as
#' averaging dataset. The index are based on the original dataset `x` and `y`
#' from forest. Essentially, `x[sampleIndex]` generates the whole splitting
#' dataset.
#' @param splitrule A string to specify how to find the best split among all
#' candidate feature values. The current version only supports `variance` which
#' minimizes the overall MSE after splitting. The default value is `variance`.
#' @param categoricalFeatureCols A list of index for all categorical data. Used
#' for trees to detect categorical columns.
#' @aliases honestRFTree, honestRFTree-method
#' @return a `honestRFTree` object
honestRFTree <- function(x,
                         y,
                         mtry = max(floor(ncol(x) / 3), 1),
                         nodesize = list("averagingNodeSize" = 5,
                                         "splittingNodeSize" = 5),
                         sampleIndex = list(
                           "averagingSampleIndex" = 1:length(y),
                           "splittingSampleIndex" = 1:length(y)
                         ),
                         splitrule = "variance",
                         categoricalFeatureCols = list()) {
  # ----------------------------------------------------------------------------
  # Check for errors
  honestRFTree_input_checker(nodesize, sampleIndex)

  # ----------------------------------------------------------------------------
  tree <- new("honestRFTree",
              sampleIndex = sampleIndex,
              root = list())

  # Grow the tree.
  root <- recursivePartition(
    x = x,
    y = y,
    mtry = mtry,
    nodesize = nodesize,
    sampleIndex = sampleIndex,
    splitrule = splitrule,
    categoricalFeatureCols = categoricalFeatureCols
  )

  tree@root <- list("node" = root)
  return(tree)
}