R/enspls.od.R

In road2stat/enpls: Ensemble Partial Least Squares Regression

#' Ensemble Sparse Partial Least Squares for Outlier Detection
#'
#' Outlier detection with ensemble sparse partial least squares.
#'
#' @param x Predictor matrix.
#' @param y Response vector.
#' @param maxcomp Maximum number of components included within each model.
#' If not specified, will use \code{5} by default.
#' @param cvfolds Number of cross-validation folds used in each model
#' for automatic parameter selection, default is \code{5}.
#' @param alpha Parameter (grid) controlling sparsity of the model.
#' If not specified, default is \code{seq(0.2, 0.8, 0.2)}.
#' @param reptimes Number of models to build with Monte-Carlo resampling
#' or bootstrapping.
#' @param method Resampling method. \code{"mc"} (Monte-Carlo resampling)
#' or \code{"boot"} (bootstrapping). Default is \code{"mc"}.
#' @param ratio Sampling ratio used when \code{method = "mc"}.
#' @param parallel Integer. Number of CPU cores to use.
#' Default is \code{1} (not parallelized).
#'
#' @return A list containing four components:
#' \itemize{
#' \item \code{error.mean} - error mean for all samples (absolute value)
#' \item \code{error.median} - error median for all samples
#' \item \code{error.sd} - error sd for all samples
#' \item \code{predict.error.matrix} - the original prediction error matrix
#' }
#'
#' @author Nan Xiao <\url{https://nanx.me}>
#'
#' @note To maximize the probablity that each observation can
#' be selected in the test set (thus the prediction uncertainty
#' can be measured), please try setting a large \code{reptimes}.
#'
#' @seealso See \code{\link{enspls.fs}} for measuring feature importance
#' with ensemble sparse partial least squares regressions.
#' See \code{\link{enspls.fit}} for fitting ensemble sparse
#' partial least squares regression models.
#'
#' @export enspls.od
#'
#' @importFrom doParallel registerDoParallel
#' @importFrom foreach foreach "%dopar%"
#'
#' @examples
#' data("logd1k")
#' x <- logd1k$x
#' y <- logd1k$y
#'
#' set.seed(42)
#' od <- enspls.od(
#'   x, y,
#'   reptimes = 5, maxcomp = 3,
#'   alpha = c(0.3, 0.6, 0.9)
#' )
#' plot(od, prob = 0.1)
#' plot(od, criterion = "sd", sdtimes = 1)
enspls.od <- function(
  x, y,
  maxcomp = 5L,
  cvfolds = 5L,
  alpha = seq(0.2, 0.8, 0.2),
  reptimes = 500L,
  method = c("mc", "boot"),
  ratio = 0.8,
  parallel = 1L) {
  if (missing(x) | missing(y)) stop("Please specify both x and y")

  method <- match.arg(method)

  x.row <- nrow(x)
  samp.idx <- vector("list", reptimes)
  samp.idx.remain <- vector("list", reptimes)

  if (method == "mc") {
    for (i in 1L:reptimes) {
      samp.idx[[i]] <- sample(1L:x.row, round(x.row * ratio))
      samp.idx.remain[[i]] <- setdiff(1L:x.row, samp.idx[[i]])
    }
  }

  if (method == "boot") {
    for (i in 1L:reptimes) {
      samp.idx[[i]] <- sample(1L:x.row, x.row, replace = TRUE)
      samp.idx.remain[[i]] <- setdiff(1L:x.row, unique(samp.idx[[i]]))
    }
  }

  if (parallel < 1.5) {
    errorlist <- vector("list", reptimes)
    for (i in 1L:reptimes) {
      x.sample <- x[samp.idx[[i]], ]
      x.remain <- x[samp.idx.remain[[i]], ]
      y.sample <- y[samp.idx[[i]]]
      y.remain <- y[samp.idx.remain[[i]]]
      errorlist[[i]] <- enspls.od.core(
        x.sample, y.sample, x.remain, y.remain,
        maxcomp, cvfolds, alpha
      )
    }
  } else {
    registerDoParallel(parallel)
    errorlist <- foreach(i = 1L:reptimes) %dopar% {
      x.sample <- x[samp.idx[[i]], ]
      x.remain <- x[samp.idx.remain[[i]], ]
      y.sample <- y[samp.idx[[i]]]
      y.remain <- y[samp.idx.remain[[i]]]
      enspls.od.core(
        x.sample, y.sample, x.remain, y.remain,
        maxcomp, cvfolds, alpha
      )
    }
  }

  prederrmat <- matrix(NA, ncol = x.row, nrow = reptimes)
  for (i in 1L:reptimes) {
    for (j in 1L:length(samp.idx.remain[[i]])) {
      prederrmat[i, samp.idx.remain[[i]][j]] <- errorlist[[i]][j]
    }
  }

  errmean <- abs(colMeans(prederrmat, na.rm = TRUE))
  errmedian <- apply(prederrmat, 2L, median, na.rm = TRUE)
  errsd <- apply(prederrmat, 2L, sd, na.rm = TRUE)

  res <- list(
    "error.mean" = errmean,
    "error.median" = errmedian,
    "error.sd" = errsd,
    "predict.error.matrix" = prederrmat
  )
  class(res) <- "enspls.od"

  res
}

#' core function for enspls.od
#'
#' select the best ncomp and alpha with cross-validation,
#' then use them to fit the complete training set,
#' and predict on the test set. scale = TRUE
#'
#' @importFrom spls cv.spls spls
#'
#' @return the error vector between predicted y and real y
#'
#' @keywords internal

enspls.od.core <- function(
  x.sample, y.sample, x.remain, y.remain,
  maxcomp, cvfolds, alpha) {
  invisible(capture.output(
    spls.cvfit <- cv.spls(
      x.sample,
      y.sample,
      fold = cvfolds,
      K = maxcomp,
      eta = alpha,
      scale.x = TRUE,
      scale.y = FALSE,
      plot.it = FALSE
    )
  ))

  # select best component number and alpha using adjusted CV
  cv.bestcomp <- spls.cvfit$"K.opt"
  cv.bestalpha <- spls.cvfit$"eta.opt"

  spls.fit <- spls(
    x.sample,
    y.sample,
    K = cv.bestcomp,
    eta = cv.bestalpha,
    scale.x = TRUE,
    scale.y = FALSE
  )

  pred <- predict(spls.fit, newx = x.remain)

  error <- y.remain - pred
  names(error) <- NULL

  error
}