#' @title SETRED generic method
#' @description SETRED is a variant of the self-training classification method
#' (\code{\link{selfTraining}}) with a different addition mechanism.
#' The SETRED classifier is initially trained with a
#' reduced set of labeled examples. Then it is iteratively retrained with its own most
#' confident predictions over the unlabeled examples. SETRED uses an amending scheme
#' to avoid the introduction of noisy examples into the enlarged labeled set. For each
#' iteration, the mislabeled examples are identified using the local information provided
#' by 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 D A distance matrix between all the training instances. This matrix is used to
#' construct the neighborhood graph.
#' @param gen.learner A function for training a supervised base classifier.
#' This function needs two parameters, indexes and cls, where indexes indicates
#' the instances to use and cls specifies the classes of those instances.
#' @param gen.pred A function for predicting the probabilities per classes.
#' This function must be two parameters, model and indexes, where the model
#' is a classifier trained with \code{gen.learner} function and
#' indexes indicates the instances to predict.
#' @param theta Rejection threshold to test the critical region. Default is 0.1.
#' @param max.iter Maximum number of iterations to execute the self-labeling process.
#' Default is 50.
#' @param perc.full A number between 0 and 1. If the percentage
#' of new labeled examples reaches this value the self-training process is stopped.
#' Default is 0.7.
#' @details
#' SetredG can be helpful in those cases where the method selected as
#' base classifier needs a \code{learner} and \code{pred} functions with other
#' specifications. For more information about the general setred method,
#' please see \code{\link{setred}} function. Essentially, \code{setred}
#' function is a wrapper of \code{setredG} function.
#' @return A list object of class "setredG" 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.}
#' }
#' @example demo/SETREDG.R
#' @export
setredG <- function(
y, D, gen.learner, gen.pred,
theta = 0.1,
max.iter = 50,
perc.full = 0.7
) {
### 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(inherits(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 distance matrix D is not a square matrix.")
} 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 theta
if(!(theta >= 0 && theta <= 1)) {
stop("theta must be between 0 and 1")
}
# Check max.iter
if(max.iter < 1){
stop("Parameter max.iter is less than 1. Expected a value greater than and equal to 1.")
}
# Check perc.full
if(perc.full < 0 || perc.full > 1){
stop("Parameter perc.full is not in the range 0 to 1.")
}
### Init variables ###
# Identify the classes
classes <- levels(y)
nclasses <- length(classes)
# 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.")
}
### SETRED algorithm ###
# 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 / length(labeled)
# Determine the total of instances to include per iteration
cantClass <- round(cls.summary / min(cls.summary))
totalPerIter <- sum(cantClass)
# Compute count full
count.full <- length(labeled) + round(length(unlabeled) * perc.full)
iter <- 1
while ((length(labeled) < count.full) && (length(unlabeled) >= totalPerIter) && (iter <= max.iter)) {
# Train classifier
#model <- trainModel(x[labeled, ], ynew[labeled], learner, learner.pars)
model <- gen.learner(labeled, ynew[labeled])
# Predict probabilities per classes of unlabeled examples
#prob <- predProb(model, x[unlabeled, ], pred, pred.pars, classes)
prob <- checkProb(prob = gen.pred(model, unlabeled), ninstances = length(unlabeled), classes)
# Select the instances with better class probability
selection <- selectInstances(cantClass, prob)
# Save count of labeled set before it's modification
nlabeled.old <- length(labeled)
# Add selected instances to L
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 U
unlabeled <- unlabeled[-selection$unlabeled.idx]
# Save count of labeled set after it's modification
nlabeled.new <- length(labeled)
# Build a neighborhood graph G with L U L'
ady <- vector("list", nlabeled.new) # Adjacency list of G
for (i in (nlabeled.old + 1):nlabeled.new){
for (j in 1:(i - 1)) {
con <- TRUE
for (k in 1:nlabeled.new)
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)
}
}
}
# Compute the bad examples and noise instances
noise.insts <- c() # instances to delete from labeled set
for (i in (nlabeled.old + 1):nlabeled.new) { # only on L'
propi <- proportion[unclass(ynew[labeled[i]])]
# calcular observacion Oi de Ji
Oi <- 0
nv <- W <- k <- 0
for (j in ady[[i]]) {
k <- k + 1
W[k] <- 1 / (1 + D[labeled[i], labeled[j]])
if (ynew[labeled[i]] != ynew[labeled[j]]) {
Oi <- Oi + W[k]
nv <- nv + 1
}
}
if (normalCriterion(theta, Oi, length(ady[[i]]), propi, W)){
noise.insts <- c(noise.insts, i)
}
}
# Delete from labeled the noise instances
if (length(noise.insts) > 0){
ynew[labeled[noise.insts]] <- NA
labeled <- labeled[-noise.insts]
}
iter <- iter + 1
}
### Result ###
# Train final model
#model <- trainModel(x[labeled, ], ynew[labeled], learner, learner.pars)
model <- gen.learner(labeled, ynew[labeled])
# Save result
result <- list(
model = model,
instances.index = labeled
)
class(result) <- "setredG"
result
}
#' @title SETRED method
#' @description SETRED (SElf-TRaining with EDiting) is a variant of the self-training
#' classification method (as implemented in the function \code{\link{selfTraining}}) with a different addition mechanism.
#' The SETRED classifier is initially trained with a
#' reduced set of labeled examples. Then, it is iteratively retrained with its own most
#' confident predictions over the unlabeled examples. SETRED uses an amending scheme
#' to avoid the introduction of noisy examples into the enlarged labeled set. For each
#' iteration, the mislabeled examples are identified using the local information provided
#' by the neighborhood graph.
#' @param x A object that can be coerced as matrix. This object has two possible
#' interpretations according to the value set in the \code{x.inst} argument:
#' a matrix with the training instances where each row represents a single instance
#' or a precomputed (distance or kernel) matrix between the training examples.
#' @param y A vector with the labels of the training instances. In this vector
#' the unlabeled instances are specified with the value \code{NA}.
#' @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 or the name of a distance available
#' in the \code{proxy} package to compute
#' the distance matrix in the case that \code{x.inst} is \code{TRUE}.
#' @param learner either a function or a string naming the function for
#' training a supervised base classifier, using a set of instances
#' (or optionally a distance matrix) and it's corresponding classes.
#' @param learner.pars A list with additional parameters for the
#' \code{learner} function if necessary.
#' Default is \code{NULL}.
#' @param pred either a function or a string naming the function for
#' predicting the probabilities per classes,
#' using the base classifier trained with the \code{learner} function.
#' Default is \code{"predict"}.
#' @param pred.pars A list with additional parameters for the
#' \code{pred} function if necessary.
#' Default is \code{NULL}.
#' @param theta Rejection threshold to test the critical region. Default is 0.1.
#' @param max.iter maximum number of iterations to execute the self-labeling process.
#' Default is 50.
#' @param perc.full A number between 0 and 1. If the percentage
#' of new labeled examples reaches this value the self-training process is stopped.
#' Default is 0.7.
#' @details
#' SETRED initiates the self-labeling process by training a model from the original
#' labeled set. In each iteration, the \code{learner} function detects unlabeled
#' examples for which it makes the most confident prediction and labels those examples
#' according to the \code{pred} function. The identification of mislabeled examples is
#' performed using a neighborhood graph created from the distance matrix.
#' When \code{x.inst} is \code{TRUE} this distance matrix is computed using
#' the \code{dist} function. On the other hand, when \code{x.inst} is \code{FALSE}
#' the matrix provided with \code{x} is used both to train a classifier and to create
#' the neighborhood graph.
#' Most examples possess the same label in a neighborhood. So if an example locates
#' in a neighborhood with too many neighbors from different classes, this example should
#' be considered problematic. The value of the \code{theta} argument controls the confidence
#' of the candidates selected to enlarge the labeled set. The lower this value is, the more
#' restrictive is the selection of the examples that are considered good.
#' For more information about the self-labeled process and the rest of the parameters, please
#' see \code{\link{selfTraining}}.
#'
#' @return A list object of class "setred" 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{pred}{The function provided in the \code{pred} argument.}
#' \item{pred.pars}{The list provided in the \code{pred.pars} argument.}
#' }
#' @references
#' Ming Li and ZhiHua Zhou.\cr
#' \emph{Setred: Self-training with editing.}\cr
#' In Advances in Knowledge Discovery and Data Mining, volume 3518 of Lecture Notes in
#' Computer Science, pages 611-621. Springer Berlin Heidelberg, 2005.
#' ISBN 978-3-540-26076-9. doi: 10.1007/11430919 71.
#' @example demo/SETRED.R
#' @export
setred <- function(
x, y, x.inst = TRUE,
dist = "Euclidean",
learner, learner.pars = NULL,
pred = "predict", pred.pars = NULL,
theta = 0.1,
max.iter = 50,
perc.full = 0.7
) {
### Check parameters ###
checkTrainingData(environment())
learner.pars <- as.list2(learner.pars)
pred.pars <- as.list2(pred.pars)
if(x.inst){
# Instance matrix case
gen.learner2 <- function(training.ints, cls){
m <- trainModel(x[training.ints, ], cls, learner, learner.pars)
return(m)
}
gen.pred2 <- function(m, testing.ints){
prob <- predProb(m, x[testing.ints, ], pred, pred.pars)
return(prob)
}
result <- setredG(
y,
D = proxy::dist(x, method = dist, by_rows = TRUE, diag = TRUE, upper = TRUE),
gen.learner2, gen.pred2,
theta, max.iter, perc.full
)
}else{
# Distance matrix case
if(!is.matrix(x)){
stop("Parameter x is neither a matrix or a dist object.")
} else if(nrow(x) != ncol(x)){
stop("The distance matrix x is not a square matrix.")
} else if(nrow(x) != length(y)){
stop(sprintf(paste("The dimensions of the matrix x is %i x %i",
"and it's expected %i x %i according to the size of y."),
nrow(x), ncol(x), length(y), length(y)))
}
gen.learner1 <- function(training.ints, cls){
m <- trainModel(x[training.ints, training.ints], cls, learner, learner.pars)
r <- list(m = m, training.ints = training.ints)
return(r)
}
gen.pred1 <- function(r, testing.ints){
prob <- predProb(r$m, x[testing.ints, r$training.ints], pred, pred.pars)
return(prob)
}
result <- setredG(y, x, gen.learner1, gen.pred1, theta, max.iter, perc.full)
result$model <- result$model$m
}
### Result ###
result$classes = levels(y)
result$pred = pred
result$pred.pars = pred.pars
class(result) <- "setred"
result
}
#' @title Predictions of the SETRED method
#' @description Predicts the label of instances according to the \code{setred} model.
#' @details For additional help see \code{\link{setred}} examples.
#' @param object SETRED model built with the \code{\link{setred}} function.
#' @param x A object that can be coerced as matrix.
#' Depending on how was the model built, \code{x} is interpreted as a matrix
#' with the distances between the unseen instances and the selected training instances,
#' or a matrix of instances.
#' @param ... This parameter is included for compatibility reasons.
#' @return Vector with the labels assigned.
#' @export
#' @importFrom stats predict
predict.setred <- function(object, x, ...) {
x <- as.matrix2(x)
result <- getClass(
checkProb(
predProb(object$model, x, object$pred, object$pred.pars),
ninstances = nrow(x),
object$classes
)
)
return(result)
}
#' @title Normal criterion
#' @details Computes the critical value using the normal distribution as the authors suggest
#' when the neighborhood is big for the instances in the RNG.
#' @return A boolean value indicating if the instance must be eliminated
#' @noRd
normalCriterion <- function(theta, Oi, vec, propi, W) {
# calcular media y desv est de J
mean <- (1 - propi) * sum(W)
sd <- sqrt(propi * (1 - propi) * sum(W^2))
# calcular el p-value para Oi
vc <- stats::qnorm(theta/2, mean, sd)
if (vc < 0 && Oi == 0) # caso especial en que vc < 0 producto de la aproximacion mediante la dist. Normal
FALSE
else
Oi >= vc
}
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