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#' Itemset Meta-features
#'
#' Itemset characterization features measure measure the distribution of values
#' of both single attributes and pairs of attributes.
#'
#' @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 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{"oneitemset"}{Individual frequency of each attributes' value.}
#' \item{"twoitemset"}{Correlation information of the two attributes'
#' value pairs.}
#' \item{"clssitemset"}{It is a two itemset computed using a predictive
#' attribute and the target.}
#' }
#' @return A list named by the requested meta-features.
#'
#' @references
#' Song, Q., Wang, G., & Wang, C. (2012). Automatic recommendation of
#' classification algorithms based on data set characteristics. Pattern
#' Recognition, 45(7), 2672-2689.
#'
#' Wang, G., Song, Q., & Zhu, X. (2015). An improved data characterization
#' method and its application in classification algorithm recommendation.
#' Applied Intelligence, 43(4), 892-912.
#'
#' @examples
#' ## Extract all meta-features using formula
#' itemset(Species ~ ., iris)
#'
#' ## Extract some meta-features
#' itemset(iris[1:4], iris[5], c("oneitemset"))
#'
#' ## Use another summarization function
#' itemset(Species ~ ., iris, summary=c("min", "median", "max"))
#' @export
itemset <- function(...) {
UseMethod("itemset")
}
#' @rdname itemset
#' @export
itemset.default <- function(x, y, features="all",
summary=c("mean", "sd"),
...) {
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.itemset()
}
features <- match.arg(features, ls.itemset(), TRUE)
colnames(x) <- make.names(colnames(x), unique=TRUE)
if (length(summary) == 0) {
summary <- "non.aggregated"
}
x <- categorize(x)
sapply(features, function(f) {
fn <- paste("m", f, sep=".")
measure <- eval(call(fn, x=x, y=y))
post.processing(measure, summary, f %in% ls.itemset.multiples(), ...)
}, simplify=FALSE)
}
#' @rdname itemset
#' @export
itemset.formula <- function(formula, data, features="all",
summary=c("mean", "sd"),
...) {
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
itemset.default(modFrame[-1], modFrame[1], features, summary, ...)
}
#' List the itemset meta-features
#'
#' @return A list of itemset meta-features names.
#' @export
#'
#' @examples
#' ls.itemset()
ls.itemset <- function() {
c("classitemset", "oneitemset", "twoitemset")
}
ls.itemset.multiples <- function() {
ls.itemset()
}
m.oneitemset <- function(x, ...) {
unlist(c(apply(x, 2, table)))/nrow(x)
}
m.twoitemset <- function(x, ...) {
unlist(c(apply(utils::combn(seq(ncol(x)), 2), 2, function(pair){
v1 <- table(x[,as.numeric(pair[1])])/nrow(x)
v2 <- table(x[,as.numeric(pair[2])])/nrow(x)
v12 <- table(apply(x[,as.numeric(pair)], 1, paste, collapse='_'))/nrow(x)
apply(expand.grid(names(v1), names(v2)), 1, function(twop){
val <- v12[paste(twop, collapse='_')]
(v1[twop[1]] + v2[twop[2]]) - ifelse(is.na(val), 0, 2*val)
})
})))
}
m.classitemset <- function(x, y, ...) {
v2 <- table(y)/nrow(x)
unlist(c(apply(x, 2, function(col){
v1 <- table(col)/nrow(x)
v12 <- table(paste(col, y, sep='_'))/nrow(x)
apply(expand.grid(names(v1), names(v2)), 1, function(twop){
val <- v12[paste(twop, collapse='_')]
(v1[twop[1]] + v2[twop[2]]) - ifelse(is.na(val), 0, 2*val)
})
})))
}
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