#' @title Evaluating the performance of the RPART Decision Tree.

#' @description This function evaluates the performance of the generated trees

#'   for error estimation by ten-fold cross validation assessment.

#' @export

#' @param data The resulted data from running the function J48DT.

#' @param num.folds A numeric value of the number of folds for the cross

#'   validation assessment. Default is 10.

#' @param First A string vector showing the first target cluster.  Default is

#'   "CL1"

#' @param Second A string vector showing the second target cluster.  Default is

#'   "CL2"

#' @param quiet If `TRUE`, suppresses intermediary output

#' @importFrom stats predict

#' @return Performance statistics of the model

RpartEVAL <- function(data, num.folds = 10, First = "CL1", Second = "CL2",

                      quiet = FALSE) {

  exp.imput.df <- as.data.frame(t(data))

  num.instances <- nrow(exp.imput.df)

  indices <- 1:num.instances

  classVector <- factor(colnames(data))

  cv.segments <- split(

    sample(indices), rep(1:num.folds, length = num.instances)

  )

  Rpart.performance <- cross.val(

    exp.imput.df, classVector, cv.segments, Rpart.performance, "rpart", quiet

  )

  if (!quiet) print(Rpart.performance)

  Rpart.confusion.matrix <- matrix(Rpart.performance, nrow = 2)

  rownames(Rpart.confusion.matrix) <- c(

    paste0("Predicted", First), paste0("Predicted", Second)

  )

  colnames(Rpart.confusion.matrix) <- c(First, Second)

  if (!quiet) print(Rpart.confusion.matrix)

  Rpart.sn <- round(SN(Rpart.confusion.matrix), digits = 2)

  Rpart.sp <- round(SP(Rpart.confusion.matrix), digits = 2)

  Rpart.acc <- round(ACC(Rpart.confusion.matrix), digits = 2)

  Rpart.mcc <- round(MCC(Rpart.confusion.matrix), digits = 2)

  if (!quiet) {

    message(

      "Rpart SN: ", Rpart.sn, "\n",

      "Rpart SP: ", Rpart.sp, "\n",

      "Rpart ACC: ", Rpart.acc, "\n",

      "Rpart MCC: ", Rpart.mcc, "\n"

    )

  }

  return(Rpart.performance)

}