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#' @title SNNRCE generic method
#' @description SNNRCE is a variant of the self-training classification
#' method (\code{\link{selfTraining}}) with a different
#' addition mechanism and a fixed learning scheme (1-NN). SNNRCE uses an amending scheme
#' to avoid the introduction of noisy examples into the enlarged labeled set.
#' The mislabeled examples are identified using the local information provided
#' by the neighborhood graph. A statistical test using cut edge weight is used to modify
#' the labels of the missclassified examples.
#' @param D A distance matrix between all the training instances. This matrix is used to
#' construct the neighborhood graph.
#' @param y A vector with the labels of training instances. In this vector the
#' unlabeled instances are specified with the value \code{NA}.
#' @param alpha Rejection threshold to test the critical region. Default is 0.1.
#' @return A list object of class "snnrceG" containing:
#' \describe{
#' \item{model}{The final base classifier trained using the enlarged labeled set.}
#' \item{instances.index}{The indexes of the training instances used to
#' train the \code{model}. These indexes include the initial labeled instances
#' and the newly labeled instances.
#' Those indexes are relative to the \code{y} argument.}
#' }
#' @noRd
snnrceG <- function(
D, y,
alpha = 0.1
){
### Check parameters ###
# Check y
if(!is.factor(y) ){
if(!is.vector(y)){
stop("Parameter y is neither a vector nor a factor.")
}else{
y = as.factor(y)
}
}
# Check distance matrix
if(class(D) == "dist"){
D <- proxy::as.matrix(D)
}
if(!is.matrix(D)){
stop("Parameter D is neither a matrix or a dist object.")
}else if(nrow(D) != ncol(D)){
stop("The matrix D is not square.")
}else if(nrow(D) != length(y)){
stop(sprintf(paste("The dimensions of the matrix D is %i x %i",
"and it's expected %i x %i according to the size of y."),
nrow(D), ncol(D), length(y), length(y)))
}
# Check alpha
if(!(alpha >= 0 && alpha <= 1)) {
stop("Parameter alpha must be between 0 and 1")
}
# Init variable to store the labels
ynew <- y
# Obtain the indexes of labeled and unlabeled instances
labeled <- which(!is.na(y))
unlabeled <- which(is.na(y))
# Check the labeled and unlabeled sets
if(length(labeled) == 0){ # labeled is empty
stop("The labeled set is empty. All the values in y parameter are NA.")
}
if(length(unlabeled) == 0){ # unlabeled is empty
stop("The unlabeled set is empty. None value in y parameter is NA.")
}
# Identify the classes
classes <- levels(y)
nclasses <- length(classes)
### SNNRCE algorithm ###
# Initial number of labeled instances
labeledLen <- length(labeled)
# STEPS 1-2
# Count the examples per class
cls.summary <- summary(y[labeled])
# Ratio between count per class and the initial number of labeled instances
proportion <- cls.summary / labeledLen
# STEP 3
# Label the instances with Rj = 0
prueba_ynew <- as.character(ynew)
rem <- snnrce_loop_dos(as.numeric(unlabeled),D,prueba_ynew,as.numeric(labeled),as.character(y))
ynew <- factor(prueba_ynew)
"rem <- NULL
for (i in 1:length(unlabeled)) {
w <- unlabeled[i]
clase <- -1
# w is good when it's neighbors have all the same label
good <- TRUE
# Build RNG
for (j in 1:labeledLen) {
a <- labeled[j]
edge <- TRUE
for (b in labeled)
if (a != b && D[w, a] > max(D[w, b], D[b, a])) {
edge <- FALSE
break
}
if (edge) {
if (clase == -1)
clase <- y[a]
else if (clase != y[a]) {
good <- FALSE
break
}
}
}
if (good) {
# label w and delete it from unlabeled
ynew[w] <- clase
rem <- c(rem, i)
}
}"
## Update labeled and unlabeled sets
labeled <- c(labeled, unlabeled[rem])
unlabeled <- unlabeled[-rem]
# STEP 5 Autolabel
initialLen <- length(labeled)
max.per.class <- proportion * length(unlabeled)
nmax <- min(max.per.class)
count <- 0
while (count < nmax) {
# Predict prob using 1-NN
model <- oneNN(y = ynew[labeled])
prob <- predict(model, D[unlabeled, labeled], type = "prob",
distance.weighting = "reciprocalexp")
# Select one instance per class
selection <- selectInstances(rep(1, nclasses), prob)
# Add selected instances to labeled
labeled.prime <- unlabeled[selection$unlabeled.idx]
sel.classes <- classes[selection$class.idx]
ynew[labeled.prime] <- sel.classes
labeled <- c(labeled, labeled.prime)
# Delete selected instances from unlabeled
unlabeled <- unlabeled[-selection$unlabeled.idx]
count <- count + 1
}
len <- length(labeled)
if (initialLen < len) { # new instances were added
# STEP 6 Build RNG for L
'ady <- vector("list", len) # Adjacency list of G
for (i in 2:len)
for (j in 1:(i-1)) {
con <- TRUE
for (k in 1:len)
if (k != i && k != j && D[labeled[i], labeled[j]] >
max(D[labeled[i], labeled[k]], D[labeled[k], labeled[j]])) {
con <- FALSE
break
}
if (con) {
ady[[i]] <- c(ady[[i]], j)
ady[[j]] <- c(ady[[j]], i)
}
}'
ady <- snnrce_loop(len,D,as.numeric(labeled))
# STEP 7 Relabel
# Build Ii and Ji
I <- rep(0, len) # = 0 len times
J <- rep(0, len)
for (i in 1:len)
for (j in ady[[i]]) {
Wij <- 1 / (1 + D[labeled[i], labeled[j]])
I[i] <- I[i] + Wij
if (ynew[labeled[i]] != ynew[labeled[j]])
J[i] <- J[i] + Wij
}
# Compute mean and standard desviation of R
R <- J / I; rm(J,I)
media <- base::mean(R)
ds <- stats::sd(R)
u <- stats::qnorm(1-alpha/2)
RCritico <- media + u * ds
relabel <- which(R[(labeledLen + 1):len] > RCritico)
for (i in relabel + labeledLen) {
w <- -1
if (nclasses > 2) {
wc <- rep(0, nclasses)
for (j in ady[[i]]) {
Wij <- 1 / (1 + D[labeled[i], labeled[j]])
pos <- unclass(ynew[labeled[j]])
wc[pos] <- wc[pos] + Wij
}
wc[unclass(ynew[labeled[i]])] <- 0
w <- which.max(wc)
} else { # if two classes invert the label
w <- ifelse(unclass(ynew[labeled[i]]) == 1, 2, 1)
}
if (w != -1)
ynew[labeled[i]] <- classes[w]
}
rm(ady)
}
### Result ###
# Save result
result <- list(
model = oneNN(y = ynew[labeled]),
instances.index = labeled
)
class(result) <- "snnrceG"
return(result)
}
#' @title Predictions of the SNNRCE method
#' @description Predicts the label of instances according to the \code{snnrceG} model.
#' @param object model instance
#' @param D distance matrix
#' @param ... This parameter is included for compatibility reasons.
#' @method predict snnrceG
#' @export
#' @importFrom stats predict
predict.snnrceG <- function(object, D, ...) {
if(class(D) == "dist"){
D <- proxy::as.matrix(D)
}
cls <- predict(object$model, D, type = "class")
return(cls)
}
#' @title General Interface for SNNRCE model
#' @description SNNRCE (Self-training Nearest Neighbor Rule using Cut Edges) is a variant
#' of the self-training classification method (\code{\link{selfTraining}}) with a different
#' addition mechanism and a fixed learning scheme (1-NN). SNNRCE uses an amending scheme
#' to avoid the introduction of noisy examples into the enlarged labeled set.
#' The mislabeled examples are identified using the local information provided
#' by the neighborhood graph. A statistical test using cut edge weight is used to modify
#' the labels of the missclassified examples.
#' @param x.inst A boolean value that indicates if \code{x} is or not an instance matrix.
#' Default is \code{TRUE}.
#' @param dist A distance function available in the \code{proxy} package to compute
#' the distance matrix in the case that \code{x.inst} is \code{TRUE}.
#' @param alpha Rejection threshold to test the critical region. Default is 0.1.
#' @details
#' SNNRCE initiates the self-labeling process by training a 1-NN from the original
#' labeled set. This method attempts to reduce the noise in examples by labeling those instances
#' with no cut edges in the initial stages of self-labeling learning.
#' These highly confident examples are added into the training set.
#' The remaining examples follow the standard self-training process until a minimum number
#' of examples will be labeled for each class. A statistical test using cut edge weight is used
#' to modify the labels of the missclassified examples The value of the \code{alpha} argument
#' defines the critical region where the candidates examples are tested. The higher this value
#' is, the more relaxed it is the selection of the examples that are considered mislabeled.
#'
#' @return (When model fit) A list object of class "snnrce" containing:
#' \describe{
#' \item{model}{The final base classifier trained using the enlarged labeled set.}
#' \item{instances.index}{The indexes of the training instances used to
#' train the \code{model}. These indexes include the initial labeled instances
#' and the newly labeled instances.
#' Those indexes are relative to \code{x} argument.}
#' \item{classes}{The levels of \code{y} factor.}
#' \item{x.inst}{The value provided in the \code{x.inst} argument.}
#' \item{dist}{The value provided in the \code{dist} argument when x.inst is \code{TRUE}.}
#' \item{xtrain}{A matrix with the subset of training instances referenced by the indexes
#' \code{instances.index} when x.inst is \code{TRUE}.}
#' }
#' @references
#' Yu Wang, Xiaoyan Xu, Haifeng Zhao, and Zhongsheng Hua.\cr
#' \emph{Semisupervised learning based on nearest neighbor rule and cut edges.}\cr
#' Knowledge-Based Systems, 23(6):547-554, 2010. ISSN 0950-7051. doi: http://dx.doi.org/10.1016/j.knosys.2010.03.012.
#' @example demo/SNNRCE.R
#' @export
snnrce <- function(
x.inst = TRUE,
dist = "Euclidean",
alpha = 0.1
){
train_function <- function(x,y){
### Check parameters ###
x <- as.matrix(x)
if(x.inst){
# Instance matrix case
result <- snnrceG(
D = proxy::dist(x, method = dist, by_rows = TRUE,
diag = TRUE, upper = TRUE),
y,
alpha
)
}else{
# Distance matrix case
result <- snnrceG(D = x, y, alpha)
}
result$classes = levels(y)
result$x.inst = x.inst
if(x.inst){
result$dist <- dist
result$xtrain <- x[result$instances.index, ]
}
result$classes = levels(y)
result$pred.params = c("class","raw")
result$mode = "classification"
class(result) <- "snnrce"
return(result)
}
args <- list(
x.inst = x.inst,
dist = dist,
alpha = alpha
)
new_model_sslr(train_function, "snnrce" ,args)
}
#' @title Predictions of the SNNRCE method
#' @description Predicts the label of instances according to the \code{snnrce} model.
#' @details For additional help see \code{\link{snnrce}} examples.
#' @param object SNNRCE model built with the \code{\link{snnrce}} function.
#' @param x A object that can be coerced as matrix.
#' Depending on how was the model built, \code{x} is interpreted as a matrix
#' with the distances between the unseen instances and the selected training instances,
#' or a matrix of instances.
#' @param ... This parameter is included for compatibility reasons.
#' @return Vector with the labels assigned.
#' @method predict snnrce
#' @importFrom stats predict
predict.snnrce <- function(object, x, ...) {
if(!is.matrix(x))
x <- as.matrix(x)
x <- as.matrix2(x)
if(object$x.inst){
D <- proxy::dist(x, y = object$xtrain, method = object$dist,
diag = TRUE, upper = TRUE, by_rows = TRUE)
cls <- predict(object$model, D, type = "class")
}else{
cls <- predict(object$model, x, type = "class")
}
result <- cls
result
}
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