R/061_sym_gau_an.R In noisemodel: Noise Models for Classification Datasets

Documented in sym_gau_ansym_gau_an.defaultsym_gau_an.formula

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#' @export
sym_gau_an <- function(x, ...) UseMethod("sym_gau_an")

#' Symmetric Gaussian attribute noise
#'
#' Introduction of \emph{Symmetric Gaussian attribute noise} into a classification dataset.
#'
#'\emph{Symmetric Gaussian attribute noise} corrupts (\code{level}·100)\% of the values of
#' each attribute in the dataset. In order to corrupt an attribute \emph{A}, (\code{level}·100)\% of the
#' samples in the dataset are chosen. Then, their values for \emph{A} are corrupted adding a random value
#' that follows a Gaussian distribution of \emph{mean} = 0 and \emph{standard deviation} = (\emph{max}-\emph{min})·\code{k}, being
#' \emph{max} and \emph{min} the limits of the attribute domain. For nominal attributes, a random value is chosen.
#'
#' @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 k a double in [0,1] with the scale used for the standard deviation (default: 0.2).
#' @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 attribute.}
#' \item{idnoise}{an integer vector list with the indices of noisy samples per attribute.}
#' \item{numclean}{an integer vector with the amount of clean samples per attribute.}
#' \item{idclean}{an integer vector list with the indices of clean samples per attribute.}
#' \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
#' J. A. Sáez, M. Galar, J. Luengo, and F. Herrera.
#' \strong{Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition}.
#' \emph{Knowledge and Information Systems}, 38(1):179-206, 2014.
#' \doi{10.1007/s10115-012-0570-1}.
#'
#' @examples
#' data(iris2D)
#'
#' # usage of the default method
#' set.seed(9)
#' outdef <- sym_gau_an(x = iris2D[,-ncol(iris2D)], y = iris2D[,ncol(iris2D)], level = 0.1)
#'
#' # show results
#' summary(outdef, showid = TRUE)
#' plot(outdef)
#'
#' # usage of the method for class formula
#' set.seed(9)
#' outfrm <- sym_gau_an(formula = Species ~ ., data = iris2D, level = 0.1)
#'
#' # check the match of noisy indices
#' identical(outdef$idnoise, outfrm$idnoise)
#'
#' @note Noise model adapted from the papers in References.
#'
#'
#' @name sym_gau_an
NULL

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#' @export
#' @rdname sym_gau_an
#' @importFrom "stats" "rnorm"
sym_gau_an.default <- function(x, y, level, k = 0.2, 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(k < 0 || k > 1){
stop("argument \"k\" must be in [0,1]")
}

######################################################
# introduce noise #########
num_noise <- round(level*nrow(x))
idx_noise <- list()
idx_clean <- list()

if(num_noise > 0){
for(a in 1:ncol(x)){
idx_noise[[a]] <- sample(1:nrow(x), num_noise, replace = FALSE)
if(sortid){
idx_noise[[a]] <- sort(idx_noise[[a]])
}
idx_clean[[a]] <- setdiff(1:nrow(x),idx_noise[[a]])

if(is.numeric(x[,a])){
newvalues <- x[idx_noise[[a]],a] + rnorm(n = num_noise, mean = 0, sd = (max(x[,a])-min(x[,a]))*k)
if(is.integer(x[,a])){
newvalues <- round(newvalues)
}
newvalues[newvalues < min(x[,a])] <- min(x[,a])
newvalues[newvalues > max(x[,a])] <- max(x[,a])
}
else if(is.factor(x[,a])){
newvalues <- sample_replace(x = 1:nlevels(x[,a]), size = num_noise, original = FALSE, ref = as.integer(x[idx_noise[[a]],a]))
newvalues <- levels(x[,a])[newvalues]
}

x[idx_noise[[a]],a] <- newvalues
}
}
else{
for(a in 1:ncol(x)){
idx_noise[[a]] <- integer(0)
idx_clean[[a]] <- setdiff(1:nrow(x),idx_noise[[a]])
}
}

classes <- levels(y)
distr <- as.vector(table(factor(y, levels = classes)))
names(distr) <- classes

######################################################
# create object of class 'ndmodel' #########
call <- match.call()
call[[1]] <- as.name("sym_gau_an")
res <- list(xnoise = x,
ynoise = y,
numnoise = rep(num_noise,ncol(x)),
idnoise = idx_noise,
numclean = rep(nrow(x),ncol(x))-num_noise,
idclean = idx_clean,
distr = distr,
model = "Symmetric Gaussian attribute noise",
param = list(level = level, k = k, sortid = sortid),
call = call
)
class(res) <- "ndmodel"
return(res)
}

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#' @export
#' @rdname sym_gau_an
#' @importFrom "stats" "model.frame"
sym_gau_an.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_gau_an.default(x = x, y = y, ...)
res$call <- match.call(expand.dots = TRUE) res$call[[1]] <- as.name("sym_gau_an")

return(res)
}

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noisemodel documentation built on Oct. 17, 2022, 9:05 a.m.