R/s_CTree.R

In egenn/rtemis: Machine Learning and Visualization

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

#' Conditional Inference Trees \[C, R, S\]
#'
#' Train a conditional inference tree using {partykit::ctree}
#'
#' @inheritParams s_GLM
#' @param control List of parameters for the CTree algorithms. Set using
#' `partykit::ctree_control`
#' 
#' @return `rtMod` object
#' @author E.D. Gennatas
#' @seealso [train_cv]
#' @family Supervised Learning
#' @family Tree-based methods
#' @export

s_CTree <- function(x, y = NULL,
                    x.test = NULL, y.test = NULL,
                    weights = NULL,
                    control = partykit::ctree_control(),
                    ifw = TRUE,
                    ifw.type = 2,
                    upsample = FALSE,
                    downsample = FALSE,
                    resample.seed = NULL,
                    x.name = NULL,
                    y.name = NULL,
                    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_CTree))
    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 <- "CTree"

  # Dependencies ----
  dependency_check("partykit")

  # Arguments ----
  if (is.null(x.name)) x.name <- getName(x, "x")
  if (is.null(y.name)) y.name <- getName(y, "y")
  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
  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
  }

  # Formula ----
  df.train <- data.frame(y = y, x)
  features <- paste(xnames, collapse = " + ")
  .formula <- as.formula(paste(y.name, "~", features))

  # CTree ----
  if (verbose) msg2("Training Conditional Inference Tree...", newline.pre = TRUE)
  # Instead of loading the whole package
  # because partykit::ctree does this:
  # mf[[1L]] <- quote(extree_data)
  # d <- eval(mf, parent.frame())
  extree_data <- partykit::extree_data
  mod <- partykit::ctree(formula = .formula,
                         data = df.train,
                         weights = weights,
                         control = control, ...)

  # Fitted ----
  if (type == "Classification") {
    fitted.prob <- predict(mod, x, type = "prob")
  }
  fitted <- predict(mod, x, type = "response")
  error.train <- mod_error(y, fitted)
  if (verbose) errorSummary(error.train, mod.name)

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

  # Outro ----
  extra <- list(formula = .formula,
                weights = weights)
  if (type == "Classification") {
    extra$fitted.prob <- fitted.prob
    extra$predicted.prob <- predicted.prob
  }
  rt <- rtMod$new(mod.name = mod.name,
                  y.train = y,
                  y.test = y.test,
                  x.name = x.name,
                  xnames = xnames,
                  mod = mod,
                  type = type,
                  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_CTree