R/enpls.fit.R

In road2stat/enpls: Ensemble Partial Least Squares Regression

#' Ensemble Partial Least Squares Regression
#'
#' Ensemble partial least squares regression.
#'
#' @param x Predictor matrix.
#' @param y Response vector.
#' @param maxcomp Maximum number of components included within each model.
#' If not specified, will use the maximum number possible (considering
#' cross-validation and special cases where n is smaller than p).
#' @param cvfolds Number of cross-validation folds used in each model
#' for automatic parameter selection, default is \code{5}.
#' @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 all partial least squares model objects.
#'
#' @author Nan Xiao <\url{https://nanx.me}>
#'
#' @seealso See \code{\link{enpls.fs}} for measuring feature importance
#' with ensemble partial least squares regressions.
#' See \code{\link{enpls.od}} for outlier detection with ensemble
#' partial least squares regressions.
#'
#' @export enpls.fit
#'
#' @importFrom doParallel registerDoParallel
#' @importFrom foreach foreach "%dopar%"
#'
#' @examples
#' data("alkanes")
#' x <- alkanes$x
#' y <- alkanes$y
#'
#' set.seed(42)
#' fit <- enpls.fit(x, y, reptimes = 50)
#' print(fit)
#' predict(fit, newx = x)
enpls.fit <- function(
  x, y,
  maxcomp = NULL,
  cvfolds = 5L,
  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)

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

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

  if (parallel < 1.5) {
    modellist <- vector("list", reptimes)
    for (i in 1L:reptimes) {
      xtmp <- x[samp.idx[[i]], ]
      ytmp <- y[samp.idx[[i]]]
      plsdf <- as.data.frame(cbind(xtmp, "y" = ytmp))
      modellist[[i]] <- suppressWarnings(
        enpls.fit.core(plsdf, maxcomp, cvfolds)
      )
    }
  } else {
    registerDoParallel(parallel)
    modellist <- foreach(i = 1L:reptimes) %dopar% {
      xtmp <- x[samp.idx[[i]], ]
      ytmp <- y[samp.idx[[i]]]
      plsdf <- as.data.frame(cbind(xtmp, "y" = ytmp))
      enpls.fit.core(plsdf, maxcomp, cvfolds)
    }
  }

  names(modellist) <- paste0("pls_model_", 1L:length(modellist))
  class(modellist) <- "enpls.fit"

  modellist
}

#' core function for enpls.fit
#'
#' select the best ncomp with cross-validation and
#' use it to fit the complete training set.
#' scale = TRUE
#'
#' @return the coefficients
#'
#' @keywords internal

enpls.fit.core <- function(plsdf, maxcomp, cvfolds) {
  if (is.null(maxcomp)) {
    plsr.cvfit <- plsr(
      y ~ .,
      data = plsdf,
      scale = TRUE,
      method = "simpls",
      validation = "CV",
      segments = cvfolds
    )
  } else {
    plsr.cvfit <- plsr(
      y ~ .,
      data = plsdf,
      ncomp = maxcomp,
      scale = TRUE,
      method = "simpls",
      validation = "CV",
      segments = cvfolds
    )
  }

  # select best component number using adjusted CV
  cv.bestcomp <- which.min(RMSEP(plsr.cvfit)[["val"]][2L, 1L, -1L])

  # remove plsr.cvfit object
  rm(plsr.cvfit)

  plsr.fit <- plsr(
    y ~ .,
    data = plsdf,
    ncomp = cv.bestcomp,
    scale = TRUE,
    method = "simpls",
    validation = "none"
  )

  # minify plsr.fit object to reduce memory footprint
  plsr.fit[["model"]] <- NULL

  # save cv.bestcomp for predict.enpls
  enpls.core.fit <- list("plsr.fit" = plsr.fit, "cv.bestcomp" = cv.bestcomp)

  # remove plsr.fit object
  rm(plsr.fit)

  enpls.core.fit
}