R/s_EVTree.R

In egenn/rtemis: Machine Learning and Visualization

# s_EVTree.R
# ::rtemis::
# 2017 E.D. Gennatas www.lambdamd.org

#' Evolutionary Learning of Globally Optimal Trees (C, R)
#'
#' Train a EVTree for regression or classification using `evtree`
#'
#' @inheritParams s_GLM
#' @param control Passed to `evtree::evtree`
#' @param ... Additional arguments to be passed to `evtree::evtree`
#' 
#' @return Object of class `rtMod`
#' @author E.D. Gennatas
#' @seealso [train_cv] for external cross-validation
#' @family Supervised Learning
#' @family Tree-based methods
#' @export

s_EVTree <- function(x, y = NULL,
                     x.test = NULL, y.test = NULL,
                     x.name = NULL, y.name = NULL,
                     weights = NULL,
                     ifw = TRUE,
                     ifw.type = 2,
                     upsample = FALSE,
                     downsample = FALSE,
                     resample.seed = NULL,
                     control = evtree::evtree.control(),
                     na.action = na.exclude,
                     print.plot = FALSE,
                     plot.fitted = NULL,
                     plot.predicted = NULL,
                     plot.theme = rtTheme,
                     question = NULL,
                     verbose = TRUE,
                     outdir = NULL,
                     save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...) {

  # Intro ----
  if (missing(x)) {
    print(args(s_EVTree))
    return(invisible(9))
  }
  if (!is.null(outdir)) outdir <- normalizePath(outdir, mustWork = FALSE)
  logFile <- if (!is.null(outdir)) {
    paste0(outdir, "/", sys.calls()[[1]][[1]], ".", format(Sys.time(), "%Y%m%d.%H%M%S"), ".log")
  } else {
    NULL
  }
  start.time <- intro(verbose = verbose, logFile = logFile)
  mod.name <- "EVTree"

  # Dependencies ----
  dependency_check("evtree")

  # Arguments ----
  if (is.null(y) && NCOL(x) < 2) {
    print(args(s_EVTree))
    stop("y is missing")
  }
  if (is.null(x.name)) x.name <- getName(x, "x")
  if (is.null(y.name)) y.name <- getName(y, "y")
  prefix <- paste0(y.name, "~", x.name)
  if (!verbose) print.plot <- FALSE
  verbose <- verbose | !is.null(logFile)
  if (save.mod && is.null(outdir)) outdir <- paste0("./s.", mod.name)
  if (!is.null(outdir)) outdir <- paste0(normalizePath(outdir, mustWork = FALSE), "/")

  # Data ----
  dt <- prepare_data(x, y,
                    x.test, y.test,
                    ifw = ifw,
                    ifw.type = ifw.type,
                    upsample = upsample,
                    downsample = downsample,
                    resample.seed = resample.seed,
                    verbose = verbose)
  x <- dt$x
  y <- dt$y
  x.test <- dt$x.test
  y.test <- dt$y.test
  xnames <- dt$xnames
  type <- dt$type
  checkType(type, c("Classification", "Regression"), mod.name)
  if (is.null(weights) && ifw) weights <- dt$weights
  if (verbose) dataSummary(x, y, x.test, y.test, type)
  if (print.plot) {
    if (is.null(plot.fitted)) plot.fitted <- if (is.null(y.test)) TRUE else FALSE
    if (is.null(plot.predicted)) plot.predicted <- if (!is.null(y.test)) TRUE else FALSE
  } else {
    plot.fitted <- plot.predicted <- FALSE
  }
  df.train <- data.frame(y = y, x)

  # Formula ----
  features <- paste(xnames, collapse = " + ")
  .formula <- as.formula(paste0(y.name, " ~ ", features))

  # evtree::evtree ----
  if (verbose) msg2("Training EVTree...", newline.pre = TRUE)
  mod <- evtree::evtree(formula = .formula,
                        data = df.train,
                        weights = weights,
                        control = control,
                        na.action = na.action, ...)

  # Fitted ----
  if (type == "Regression" || type == "Survival") {
    fitted <- predict(mod, x, type = "response")
    fitted.prob <- NULL
  } else if (type == "Classification") {
    fitted.prob <- predict(mod, x, type = "prob")
    fitted <- predict(mod, x, type = "response")
  }

  attr(fitted, "names") <- NULL
  error.train <- mod_error(y, fitted)
  if (verbose) errorSummary(error.train, mod.name)

  # Predicted ----
  if (!is.null(x.test)) {
    if (type == "Regression" || type == "Survival") {
      predicted <- predict(mod, x.test, type = "response")
      predicted.prob <- NULL
    } else if (type == "Classification") {
      predicted.prob <- predict(mod, x.test, type = "prob")
      predicted <- predict(mod, x.test, type = "response")
    }
    attr(predicted, "names") <- NULL
    if (!is.null(y.test)) {
      error.test <- mod_error(y.test, predicted)
      if (verbose) errorSummary(error.test, mod.name)
    } else {
      error.test <- NULL
    }
  } else {
    predicted <- predicted.prob <- error.test <- NULL
  }

  # Outro ----
  extra <- list(fitted.prob = fitted.prob,
                prdicted.prob = predicted.prob)
  rt <- rtModSet(rtclass = "rtMod",
                 mod = mod,
                 mod.name = mod.name,
                 type = type,
                 y.train = y,
                 y.test = y.test,
                 x.name = x.name,
                 y.name = y.name,
                 xnames = xnames,
                 fitted = fitted,
                 se.fit = NULL,
                 error.train = error.train,
                 predicted = predicted,
                 se.prediction = NULL,
                 error.test = error.test,
                 question = question,
                 extra = extra)

  rtMod.out(rt,
            print.plot,
            plot.fitted,
            plot.predicted,
            y.test,
            mod.name,
            outdir,
            save.mod,
            verbose,
            plot.theme)

  outro(start.time, verbose = verbose, sinkOff = ifelse(is.null(logFile), FALSE, TRUE))
  rt

} # rtemis::s_EVTree