R/s_KNN.R

In egenn/rtemis: Machine Learning and Visualization

# s_KNN.R
# ::rtemis::
# 2017 E.D. Gennatas www.lambdamd.org
# TODO: Consider replacing knn fn
# FNN's KNN does not have a predict function

#' k-Nearest Neighbors Classification and Regression (C, R)
#'
#' Train a k-Nearest Neighbors learner for regression or classification using `FNN`
#'
#' @inheritParams s_CART
#' @param k Integer: Number of neighbors considered
#' @param algorithm Character: Algorithm to use. Options: "kd_tree", "cover_tree", "brute"
#' @param outdir Optional. Path to directory to save output
#' @return Object of class `rtMod`
#' @author E.D. Gennatas
#' @seealso [train_cv] for external cross-validation
#' @family Supervised Learning
#' @export

s_KNN <- function(x, y = NULL,
                  x.test = NULL, y.test = NULL,
                  x.name = NULL, y.name = NULL,
                  k = 3,
                  algorithm = "kd_tree",
                  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_KNN))
    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 <- "KNN"

  # Dependencies ----
  dependency_check("FNN")

  # Arguments ----
  if (is.null(y) && NCOL(x) < 2) {
    print(args(s_KNN))
    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 (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
  }
  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)
  x <- dt$x
  y <- dt$y
  x.test <- dt$x.test
  y.test <- dt$y.test
  xnames <- dt$xnames
  type <- dt$type
  if (verbose) dataSummary(x, y, x.test, y.test, type)
  if (verbose) parameterSummary(k, algorithm,
                                newline.pre = TRUE)

  # FNN::knn/knn.reg ----
  if (verbose) msg2("Running k-Nearest Neighbors", type, "...", newline.pre = TRUE)
  .x.test <- if (is.null(x.test)) x else x.test
  if (type == "Classification") {
    mod <- FNN::knn(train = x, test = .x.test, cl = y,
                    k = k, prob = FALSE, algorithm = algorithm)
  } else {
    mod <- FNN::knn.reg(train = x, test = .x.test, y = y,
                        k = k, algorithm = algorithm)
  }

  # Fitted / Predicted ----
  # TODO: write & incorporate predict.knn / replace KNN fn
  if (type == "Classification") {
    if (is.null(x.test)) {
      fitted <- factor(mod)
      error.train <- mod_error(y, fitted)
      if (verbose) errorSummary(error.train, mod.name)
      predicted <- NULL
      error.test <- NULL
    } else {
      fitted <- NULL
      error.train <- NULL
      predicted <- factor(mod)
      error.test <- mod_error(y.test, predicted)
      if (verbose) errorSummary(error.test, mod.name)
    }
  } else {
    if (is.null(x.test)) {
      fitted <- mod$pred
      error.train <- mod_error(y, fitted)
      if (verbose) errorSummary(error.train, mod.name)
      predicted <- NULL
      error.test <- NULL
    } else {
      fitted <- NULL
      error.train <- NULL
      predicted <- mod$pred
      error.test <- mod_error(y.test, predicted)
      if (verbose) errorSummary(error.test, mod.name)
    }
  }

  # Outro ----
  extra <- list()
  rt <- rtModSet(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_KNN