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#' @export
sym_pes_ln <- function(x, ...) UseMethod("sym_pes_ln")
#' Symmetric pessimistic label noise
#'
#' Introduction of \emph{Symmetric pessimistic label noise} into a classification dataset.
#'
#' \emph{Symmetric pessimistic label noise} randomly selects (\code{level}·100)\% of the samples
#' in the dataset with independence of their class.
#' In the pessimistic case, the probability of a class \emph{i} of being mislabeled as class \emph{j} is
#' higher for \emph{j} < \emph{i} in comparison to \emph{j} > \emph{i}.
#' Thus, when noise for a certain class occurs, it is assigned to a random lower class with probability \code{levelL}
#' and to a random higher class with probability 1-\code{levelL}. The order of the classes is determined by
#' \code{order}.
#'
#' @param x a data frame of input attributes.
#' @param y a factor vector with the output class of each sample.
#' @param level a double in [0,1] with the noise level to be introduced.
#' @param levelL a double in (0.5, 1] with the noise level for lower classes (default: 0.9).
#' @param order a character vector indicating the order of the classes (default: \code{levels(y)}).
#' @param sortid a logical indicating if the indices must be sorted at the output (default: \code{TRUE}).
#' @param formula a formula with the output class and, at least, one input attribute.
#' @param data a data frame in which to interpret the variables in the formula.
#' @param ... other options to pass to the function.
#'
#' @return An object of class \code{ndmodel} with elements:
#' \item{xnoise}{a data frame with the noisy input attributes.}
#' \item{ynoise}{a factor vector with the noisy output class.}
#' \item{numnoise}{an integer vector with the amount of noisy samples per class.}
#' \item{idnoise}{an integer vector list with the indices of noisy samples.}
#' \item{numclean}{an integer vector with the amount of clean samples per class.}
#' \item{idclean}{an integer vector list with the indices of clean samples.}
#' \item{distr}{an integer vector with the samples per class in the original data.}
#' \item{model}{the full name of the noise introduction model used.}
#' \item{param}{a list of the argument values.}
#' \item{call}{the function call.}
#'
#' @references
#' R. C. Prati, J. Luengo, and F. Herrera.
#' \strong{Emerging topics and challenges of learning from noisy data in nonstandard classification:
#' a survey beyond binary class noise}. \emph{Knowledge and Information Systems}, 60(1):63–97, 2019.
#' \doi{10.1007/s10115-018-1244-4}.
#'
#' @examples
#' # load the dataset
#' data(iris2D)
#'
#' # usage of the default method
#' set.seed(9)
#' outdef <- sym_pes_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)],
#' level = 0.1, order = c("virginica", "setosa", "versicolor"))
#'
#' # show results
#' summary(outdef, showid = TRUE)
#' plot(outdef)
#'
#' # usage of the method for class formula
#' set.seed(9)
#' outfrm <- sym_pes_ln(formula = Species ~ ., data = iris2D,
#' level = 0.1, order = c("virginica", "setosa", "versicolor"))
#'
#' # check the match of noisy indices
#' identical(outdef$idnoise, outfrm$idnoise)
#'
#' @note Noise model adapted from the papers in References.
#'
#' @seealso \code{\link{sym_opt_ln}}, \code{\link{sym_usim_ln}}, \code{\link{print.ndmodel}}, \code{\link{summary.ndmodel}}, \code{\link{plot.ndmodel}}
#'
#' @name sym_pes_ln
NULL
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#' @export
#' @rdname sym_pes_ln
sym_pes_ln.default <- function(x, y, level, levelL = 0.9, order = levels(y), sortid = TRUE, ...){
######################################################
# check for errors #########
if(!is.data.frame(x)){
stop("argument \"x\" must be a data frame")
}
if(!is.factor(y)){
stop("argument \"y\" must be a factor vector")
}
if(nlevels(y) < 2){
stop("argument \"y\" must have at least 2 levels")
}
if(nrow(x) != length(y)){
stop("number of rows of \"x\" must be equal to length of \"y\"")
}
if(level < 0 || level > 1){
stop("argument \"level\" must be in [0,1]")
}
if(levelL <= 0.5 || levelL > 1){
stop("argument \"levelL\" must be in (0.5,1]")
}
if(!all(order %in% levels(y)) || length(order) != nlevels(y)){
stop("the elements and legnth of \"order\" must match those of levels(y)")
}
######################################################
# introduce noise #########
y <- factor(y, levels = order)
num_noise <- round(nrow(x)*level)
idx_noise <- sample(x = 1:nrow(x), size = num_noise, replace = FALSE)
if(sortid)
idx_noise <- sort(idx_noise)
classes <- order
nnoiseclass <- as.vector(table(factor(y[idx_noise], levels = classes)))
names(nnoiseclass) <- classes
distr <- as.vector(table(factor(y, levels = classes)))
names(distr) <- classes
if(num_noise > 0){
for(s in 1:num_noise){
cl <- as.integer(y[idx_noise[s]])
# lower class
if( (runif(1) < levelL && cl > 1) || cl == length(classes) ){
restc <- seq(from = 1, to = cl-1, by = 1)
newcla <- safe_sample(x = restc, size = 1)
}
# lower class
else{
restc <- seq(from = cl+1, to = length(classes), by = 1)
newcla <- safe_sample(x = restc, size = 1)
}
y[idx_noise[s]] <- order[newcla]
}
}
######################################################
# create object of class 'ndmodel' #########
call <- match.call()
call[[1]] <- as.name("sym_pes_ln")
res <- list(xnoise = x,
ynoise = y,
numnoise = nnoiseclass,
idnoise = list(idx_noise),
numclean = distr-nnoiseclass,
idclean = list(setdiff(1:nrow(x),idx_noise)),
distr = distr,
model = "Symmetric pessimistic label noise",
param = list(level = level, levelL = levelL, order = order, sortid = sortid),
call = call
)
class(res) <- "ndmodel"
return(res)
}
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#' @export
#' @rdname sym_pes_ln
#' @importFrom "stats" "model.frame"
sym_pes_ln.formula <- function(formula, data, ...){
if(!is.data.frame(data)){
stop("argument \"data\" must be a data frame")
}
mf <- model.frame(formula,data)
attr(mf,"terms") <- NULL
x <- mf[,-1]
y <- mf[,1]
res <- sym_pes_ln.default(x = x, y = y, ...)
res$call <- match.call(expand.dots = TRUE)
res$call[[1]] <- as.name("sym_pes_ln")
return(res)
}
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