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#' Concept Meta-features
#'
#' Concept characterization features measure the sparsity of the input space and
#' the irregularity of the input-output distribution measures extract
#' information about validation index.
#'
#' @family meta-features
#' @param x A data.frame contained only the input attributes.
#' @param y A factor response vector with one label for each row/component of x.
#' @param features A list of features names or \code{"all"} to include all them.
#' @param summary A list of summarization functions or empty for all values. See
#' \link{post.processing} method to more information. (Default:
#' \code{c("mean", "sd")})
#' @param transform A logical value indicating if the categorical attributes
#' should be transformed. If \code{FALSE} they will be ignored. (Default:
#' \code{TRUE})
#' @param formula A formula to define the class column.
#' @param data A data.frame dataset contained the input attributes and class.
#' The details section describes the valid values for this group.
#' @param ... Further arguments passed to the summarization functions.
#' @details
#' The following features are allowed for this method:
#' \describe{
#' \item{"cohesiveness"}{Example Cohesiveness is a different version of the
#' wgDist measure.}
#' \item{"conceptvar"}{Concept variation estimates the variability of class
#' labels among examples.}
#' \item{"impconceptvar"}{Improved concept variation is a different version
#' of the conceptvar measure.}
#' \item{"wgDist"}{Weighted distance captures how dense or sparse is the
#' example distribution.}
#' }
#' @return A list named by the requested meta-features.
#'
#' @references
#' Vilalta, R., & Drissi, Y. (2002). A characterization of difficult
#' problems in classification. In M. A. Wani, H. R. Arabnia, K. J.
#' Cios, K. Hafeez, G. Kendall (Eds.), Proceedings ofthe 2002
#' international conference on machine learning and applications -
#' ICMLA 2002, June 24-27, 2002, Las Vegas, Nevada (pp. 133-138).
#'
#' Vilalta, R., 1999. Understanding accuracy performance through
#' concept characterization and algorithm analysis. In: ECML
#' Workshop on Recent Advances in Meta-Learning and Future Work.
#' pp. 3-9.
#'
#' @examples
#' ## Extract all meta-features using formula
#' concept(Species ~ ., iris)
#'
#' ## Extract some meta-features
#' concept(iris[1:4], iris[5], c("conceptvar"))
#'
#' ## Use another summarization function
#' concept(Species ~ ., iris, summary=c("min", "median", "max"))
#' @export
concept <- function(...) {
UseMethod("concept")
}
#' @rdname concept
#' @export
concept.default <- function(x, y, features="all",
summary=c("mean", "sd"),
transform=TRUE, ...) {
if(!is.data.frame(x)) {
stop("data argument must be a data.frame")
}
if(is.data.frame(y)) {
y <- y[, 1]
}
y <- as.factor(y)
if(min(table(y)) < 2) {
stop("number of examples in the minority class should be >= 2")
}
if(nrow(x) != length(y)) {
stop("x and y must have same number of rows")
}
if(features[1] == "all") {
features <- ls.concept()
}
features <- match.arg(features, ls.concept(), TRUE)
colnames(x) <- make.names(colnames(x), unique=TRUE)
if (length(summary) == 0) {
summary <- "non.aggregated"
}
if(transform) {
x <- binarize(x)
} else {
x <- x[sapply(x, is.numeric)]
}
x <- as.matrix(x)
y <- as.integer(y)
alpha <- 1 #Hyperparameter
nfs <- apply(x, 2, function(col) max(col) - min(col))
d <- apply(x, 1, function(row) sqrt(rowSums(t(row - t(x))/nfs) ^2))
sapply(features, function(f) {
fn <- paste("m", f, sep=".")
measure <- eval(call(fn, x=x, y=y, d=d, alpha=alpha))
post.processing(measure, summary, f %in% ls.concept.multiples(), ...)
}, simplify=FALSE)
}
#' @rdname concept
#' @export
concept.formula <- function(formula, data, features="all",
summary=c("mean", "sd"),
transform=TRUE, ...) {
if(!inherits(formula, "formula")) {
stop("method is only for formula datas")
}
if(!is.data.frame(data)) {
stop("data argument must be a data.frame")
}
modFrame <- stats::model.frame(formula, data)
attr(modFrame, "terms") <- NULL
concept.default(modFrame[-1], modFrame[1], features, summary, transform,
...)
}
#' List the best concept meta-features
#'
#' @return A list of concept meta-features names.
#' @export
#'
#' @examples
#' ls.concept()
ls.concept <- function() {
c("cohesiveness", "conceptvar", "impconceptvar", "wgDist")
}
ls.concept.multiples <- function() {
ls.concept()
}
m.cohesiveness <- function(y, d, alpha, ...) {
cls <- sapply(y, function(yr) yr != y)
sapply(seq(ncol(d)), function(i){
row <- cls[i,-i]
radius <- ceiling(d[i,-i])
radius[radius == 0] <- 1
sum((1/2^(alpha*unique(radius))) * table(radius))
})
}
m.conceptvar <- function(x, y, d, alpha, ...) {
sn <- sqrt(ncol(x))
W <- 1 / (2 ^ (alpha * (d/(sn-d))))
diag(W) <- 0
W[is.infinite(W)] <- 0 #TODO Think better what to do with these cases
rowSums(W * sapply(y, function(yr) yr != y)) / rowSums(W)
}
m.impconceptvar <- function(y, d, alpha=1, ...) {
cls <- sapply(y, function(yr) yr != y)
sapply(seq(ncol(d)), function(i){
row <- cls[i,-i]
radius <- ceiling(d[i,-i])
radius[radius == 0] <- 1
sum(sapply(unique(radius), function(r){
mean(row[radius == r]) * (1/2^(alpha*r))
}))
})
}
m.wgDist <- function(x, d, alpha, ...) {
sn <- sqrt(ncol(x))
W <- 1 / (2 ^ (alpha * (d/(sn-d))))
diag(W) <- 0
W[is.infinite(W)] <- 0 #TODO Think better what to do with these cases
rowSums(W * d) / rowSums(W)
}
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