#' Ensemble Partial Least Squares for Applicability Domain
#'
#' This function performs applicability domain with ensemble partial least squares.
#'
#' This function performs applicability domain with ensemble partial least squares.
#'
#' @param x predictor matrix.
#' @param y response vector.
#' @param x.test predictor matrix for test.
#' @param y.test response vector for test.
#' @param maxcomp Maximum number of components included within the models,
#' if not specified, default is the variable (column) numbers in x.
#' @param MCtimes times of Monte-Carlo.
#' @param method \code{"mc"} or \code{"bootstrap"} or \code{"jagging"}. Default is \code{"mc"}.
#' @param verbose shall we print the MCtimes process.
#' @param ratio sample ratio used when \code{method = "mc"} and \code{method = "jagging"}.
#' @param parallel Integer. Number of parallel processes to use. Default is \code{1}, which means run serially.
#'
#' @return A list containing four components:
#' \itemize{
#' \item \code{STD.cv} - STD value for training set
#' \item \code{STD.te} - STD value for test set
#' \item \code{error.cv} - absolute prediction error of training set
#' \item \code{error.te} - absolute prediction error of test set
#' }
#'
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>,
#' Nan Xiao <\email{road2stat@@gmail.com}>
#'
#' @seealso See \code{\link{enpls.fs}} for feature selection with ensemble PLS.
#' See \code{\link{enpls.en}} for ensemble PLS regression.
#' See \code{\link{enpls.od}} for Outlier Detection with ensemble PLS
#'
#'
#' @export enpls.ad
#'
#' @importFrom doParallel registerDoParallel
#' @importFrom foreach foreach "%dopar%"
#'
#' @references
#' Kaneko H, Funatsu K.
#' "Applicability Domain Based on Ensemble Learning in Classification and Regression Analyses."
#' \emph{Journal of chemical information and modeling} 54, no. 9 (2014): 2469-2482.
#'
#' @examples
#' data(logS)
#' x = logS$x
#' y = logS$y
#' x.test1 = logS$x.test1
#' y.test1 = logS$y.test1
#'
#' set.seed(42)
#' ad_test1 = enpls.ad(x, y, x.test1, y.test1, MCtimes = 10)
#' print(ad_test1)
#' plot(ad_test1)
#'
#' x.test2 = logS$x.test2
#' y.test2 = logS$y.test2
#'
#' set.seed(42)
#' ad_test2 = enpls.ad(x, y, x.test2, y.test2, MCtimes = 10)
#' print(ad_test2)
#' plot(ad_test2)
#'
enpls.ad = function(x, y,
x.test = NULL,
y.test = NULL,
maxcomp = NULL,
MCtimes = 500L,
verbose = FALSE,
method = c('mc', 'bootstrap', 'jagging'), ratio = 0.8,
parallel = 1L) {
if (is.null(x.test)) x.test = x
method = match.arg(method)
x.row = nrow(x)
x.col = ncol(x)
xte.row = nrow(x.test)
samp.idx = vector('list', MCtimes)
if (method == 'mc') {
for (i in 1L:MCtimes) {
samp.idx[[i]] = sample(1L:x.row, floor(x.row * ratio))
}
}
if (method == 'bootstrap') {
for (i in 1L:MCtimes) {
samp.idx[[i]] = sample(1L:x.row, x.row, replace = TRUE)
}
}
if (method == 'jagging') {
for (i in 1L:MCtimes) {
samp.idx[[i]] = sample(1L:x.col, floor(x.col * ratio))
}
}
#plsdf = as.data.frame(cbind(x, y))
if(method == 'mc' | method == 'bootstrap'){
if (is.null(maxcomp)) maxcomp = ncol(x)
if (parallel < 1.5) {
predcvlist = vector('list', MCtimes)
predtelist = vector('list', MCtimes)
for (i in 1L:MCtimes) {
if (verbose) cat('Beginning MCtimes', i, '\n')
plsdf.x = x[samp.idx[[i]], ]
plsdf.y = y[samp.idx[[i]]]
predcvlist[[i]] = suppressWarnings(enpls.ad.cv(plsdf.x, plsdf.y, maxcomp = maxcomp))
predtelist[[i]] = enpls.ad.core(plsdf.x, plsdf.y, x.test, maxcomp = maxcomp)
}
} else {
registerDoParallel(parallel)
predcvlist = foreach(i = 1L:MCtimes) %dopar% {
plsdf.x = x[samp.idx[[i]], ]
plsdf.y = y[samp.idx[[i]]]
enpls.ad.cv(plsdf.x, plsdf.y, maxcomp = maxcomp)
}
predtelist = foreach(i = 1L:MCtimes) %dopar% {
plsdf.x = x[samp.idx[[i]], ]
plsdf.y = y[samp.idx[[i]]]
enpls.ad.core(plsdf.x, plsdf.y, x.test, maxcomp = maxcomp)
}
}
predcvmat = matrix(NA, ncol = x.row, nrow = MCtimes)
for (i in 1L:MCtimes) {
for (j in 1L:length(samp.idx[[i]])) {
predcvmat[i, samp.idx[[i]][j]] = predcvlist[[i]][j]
}
}
predtemat = matrix(NA, ncol = xte.row, nrow = MCtimes)
for (i in 1L:MCtimes) {
predtemat[i, ] = predtelist[[i]]
}
}
if(method == 'jagging'){
if (is.null(maxcomp)) maxcomp = length(samp.idx[[1]])
if (parallel < 1.5) {
predcvlist = vector('list', MCtimes)
predtelist = vector('list', MCtimes)
for (i in 1L:MCtimes) {
if (verbose) cat('Beginning MCtimes', i, '\n')
plsdf.x = x[, samp.idx[[i]]]
plsdf.y = y
predcvlist[[i]] = suppressWarnings(enpls.ad.cv(plsdf.x, plsdf.y, maxcomp = maxcomp))
predtelist[[i]] = enpls.ad.core(plsdf.x, plsdf.y, x.test, maxcomp = maxcomp)
}
}else {
registerDoParallel(parallel)
predcvlist = foreach(i = 1L:MCtimes) %dopar% {
plsdf.x = x[, samp.idx[[i]]]
plsdf.y = y
enpls.ad.cv(plsdf.x, plsdf.y, maxcomp = maxcomp)
}
predtelist = foreach(i = 1L:MCtimes) %dopar% {
plsdf.x = x[, samp.idx[[i]]]
plsdf.y = y
enpls.ad.core(plsdf.x, plsdf.y, x.test, maxcomp = maxcomp)
}
}
predcvmat = matrix(NA, ncol = x.row, nrow = MCtimes)
for (i in 1L:MCtimes) {
predcvmat[i, ] = predcvlist[[i]]
}
predtemat = matrix(NA, ncol = xte.row, nrow = MCtimes)
for (i in 1L:MCtimes) {
predtemat[i, ] = predtelist[[i]]
}
}
ypredcv.mean = apply(predcvmat, 2L, mean, na.rm = TRUE)
error.cv = abs(y - ypredcv.mean)
STD.cv = c()
for(i in 1L:x.row){
subcv.ypred = na.omit(predcvmat[,i])
STD.cv[i] = sqrt(sum((subcv.ypred - ypredcv.mean[i])^2)/(length(subcv.ypred) - 1))
}
ypredte.mean = apply(predtemat, 2L, mean, na.rm = TRUE)
if (is.null(y.test)) error.te = NULL
else error.te = abs(y.test - ypredte.mean)
STD.te = c()
for(i in 1L:xte.row){
STD.te[i] = sqrt(sum((predtemat[,i] - ypredte.mean[i])^2)/(MCtimes - 1))
}
object = list('STD.cv' = STD.cv,
'STD.te' = STD.te,
'error.cv' = error.cv,
'error.te' = error.te)
class(object) = 'enpls.ad'
return(object)
}
#' cv function for enpls.od
#'
#' This function performs k-fold cross validation for
#' ensemble partial least squares regression.
#'
#' @return 5-fold cross validation result predicted y
#'
#' @keywords internal
enpls.ad.cv = function(plsdf.x, plsdf.y,
nfolds = 5L,
maxcomp= NULL) {
plsdf.x.row = nrow(plsdf.x)
index = rep_len(1L:nfolds, plsdf.x.row)
ypred = plsdf.y
for (i in 1L:nfolds) {
xtrain = plsdf.x[index != i, ]
ytrain = plsdf.y[index != i]
xtest = plsdf.x[index == i, ]
ytest = plsdf.y[index == i]
num = apply(xtrain, 2L, function(x) sum(x == 0))
nan.ratio = num/dim(xtrain)[1]
weight = which(nan.ratio > 0.7)
if(length(weight > 0)) {
xtrain = xtrain[, -weight]
xtest = xtest[, -weight]
if(maxcomp > ncol(xtrain)) maxcomp = ncol(xtrain)
}
plsr.cvfit = plsr(ytrain ~ ., data = data.frame(xtrain, ytrain),
ncomp = maxcomp,
scale = TRUE,
method = 'simpls',
validation = 'CV', segments = 5L)
# choose best component number using adjusted CV
cv.bestcomp = which.min(RMSEP(plsr.cvfit)[['val']][2L, 1L, -1L])
plsr.fit = plsr(ytrain ~ ., data = data.frame(xtrain, ytrain),
ncomp = cv.bestcomp,
scale = TRUE,
method = 'simpls',
validation = 'none')
ypredvec = predict(plsr.fit, comps = 1:cv.bestcomp, xtest)
ypred[index == i] = ypredvec
}
return(ypred)
}
#' core function for enpls.od
#'
#' select the best ncomp with cross-validation and
#' use it to fit the complete training set again,
#' then predict on the test set. scale = TRUE
#'
#' @return predicted y for test set
#'
#' @keywords internal
enpls.ad.core = function(plsdf.x, plsdf.y, x.test, maxcomp = NULL) {
num = apply(plsdf.x, 2L, function(x) sum(x == 0))
ratio = num/dim(plsdf.x)[1]
weight = which(ratio > 0.7)
if(length(weight > 0)) {
plsdf.x = plsdf.x[, -weight]
x.test = x.test[, -weight]
if(maxcomp > ncol(plsdf.x)) maxcomp = ncol(plsdf.x)
}
plsr.cvfit = plsr(plsdf.y ~ ., data = data.frame(plsdf.x, plsdf.y),
ncomp = maxcomp,
scale = TRUE,
method = 'simpls',
validation = 'CV', segments = 5L)
# choose best component number using adjusted CV
cv.bestcomp = which.min(RMSEP(plsr.cvfit)[['val']][2L, 1L, -1L])
plsr.fit = plsr(plsdf.y ~ ., data = data.frame(plsdf.x, plsdf.y),
ncomp = cv.bestcomp,
scale = TRUE,
method = 'simpls',
validation = 'none')
ypredvec = predict(plsr.fit, comps = 1:cv.bestcomp, x.test)
return(ypredvec)
}
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