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#' @export
sym_hienc_ln <- function(x, ...) UseMethod("sym_hienc_ln")
#' Symmetric hierarchical/next-class label noise
#'
#' Introduction of \emph{Symmetric hierarchical/next-class label noise} into a classification dataset.
#'
#' \emph{Symmetric hierarchical/next-class label noise} randomly selects (\code{level}ยท100)\% of the samples
#' in the dataset with independence of their class. Then, the labels of these samples are replaced by
#' the next class within the set of class labels related to them (given by the
#' argument \code{group}). The indices in \code{group} are taken according to the order given by \code{order}.
#' Note that if a class does not belong to any superclass, it may be mislabeled as any other class.
#'
#' @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 group a list of integer vectors with the indices of classes in each superclass.
#' @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
#' T. Kaneko, Y. Ushiku, and T. Harada.
#' \strong{Label-noise robust generative adversarial networks}.
#' In \emph{Proc. 2019 IEEE Conference on Computer Vision and Pattern Recognition},
#' pages 2462-2471, 2019.
#' \doi{10.1109/CVPR.2019.00257}.
#'
#' @examples
#' # load the dataset
#' data(iris2D)
#'
#' # usage of the default method
#' set.seed(9)
#' outdef <- sym_hienc_ln(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1,
#' group = list(c(1,2)), 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_hienc_ln(formula = Species ~ ., data = iris2D, level = 0.1,
#' group = list(c(1,2)), 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_nexc_ln}}, \code{\link{sym_dia_ln}}, \code{\link{print.ndmodel}}, \code{\link{summary.ndmodel}}, \code{\link{plot.ndmodel}}
#'
#' @name sym_hienc_ln
NULL
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#' @export
#' @rdname sym_hienc_ln
sym_hienc_ln.default <- function(x, y, level, group, 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(level < 0 || level > 1){
stop("argument \"level\" must be in [0,1]")
}
if(nrow(x) != length(y)){
stop("number of rows of \"x\" must be equal to length of \"y\"")
}
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(i in 1:num_noise){
cl <- as.integer(y[idx_noise[i]])
ingroup <- sapply(X = group, FUN = function(e){cl %in% e})
if(any(ingroup)){
noisecla <- group[[which(ingroup)]]
pos <- which(noisecla == cl)
pos <- pos + 1
if(pos > length(noisecla))
pos <- 1
newvalue <- group[[ingroup]][pos]
}
else{
newvalue <- sample_replace(x = 1:nlevels(y), size = 1, original = FALSE, ref = cl)
}
y[idx_noise[i]] <- order[newvalue]
}
}
######################################################
# create object of class 'ndmodel' #########
call <- match.call()
call[[1]] <- as.name("sym_hienc_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 hierarchical/next-class label noise",
param = list(level = level, group = group, order = order, sortid = sortid),
call = call
)
class(res) <- "ndmodel"
return(res)
}
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#' @export
#' @rdname sym_hienc_ln
#' @importFrom "stats" "model.frame"
sym_hienc_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_hienc_ln.default(x = x, y = y, ...)
res$call <- match.call(expand.dots = TRUE)
res$call[[1]] <- as.name("sym_hienc_ln")
return(res)
}
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