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#' @title Create a train/test split of a dataset
#'
#' @description Often it is desirable to split a dataset into a training and
#' testing sample. This function is included in GenSVM to make it easy to do
#' so. The function is inspired by a similar function in Scikit-Learn.
#'
#' @param x array to split
#' @param y another array to split (typically this is a vector)
#' @param train.size size of the training dataset. This can be provided as
#' float or as int. If it's a float, it should be between 0.0 and 1.0 and
#' represents the fraction of the dataset that should be placed in the training
#' dataset. If it's an int, it represents the exact number of samples in the
#' training dataset. If it is NULL, the complement of \code{test.size} will be
#' used.
#' @param test.size size of the test dataset. Similarly to train.size both a
#' float or an int can be supplied. If it's NULL, the complement of train.size
#' will be used. If both train.size and test.size are NULL, a default test.size
#' of 0.25 will be used.
#' @param shuffle shuffle the rows or not
#' @param random.state seed for the random number generator (int)
#' @param return.idx whether or not to return the indices in the output
#'
#' @return a list with \code{x.train} and \code{x.test} splits of the \code{x}
#' array provided. If \code{y} is provided, also \code{y.train} and
#' \code{y.test}. If \code{return.idx} is TRUE, also \code{idx.train} and
#' \code{idx.test}.
#'
#' @author
#' Gerrit J.J. van den Burg, Patrick J.F. Groenen \cr
#' Maintainer: Gerrit J.J. van den Burg <gertjanvandenburg@gmail.com>
#'
#' @references
#' Van den Burg, G.J.J. and Groenen, P.J.F. (2016). \emph{GenSVM: A Generalized
#' Multiclass Support Vector Machine}, Journal of Machine Learning Research,
#' 17(225):1--42. URL \url{http://jmlr.org/papers/v17/14-526.html}.
#'
#' @seealso
#' \code{\link{gensvm}}, \code{\link{gensvm-package}}
#'
#' @export
#'
#' @examples
#' x <- iris[, -5]
#' y <- iris[, 5]
#'
#' # using the default values
#' split <- gensvm.train.test.split(x, y)
#'
#' # using the split in a GenSVM model
#' fit <- gensvm(split$x.train, split$y.train)
#' gensvm.accuracy(split$y.test, predict(fit, split$x.test))
#'
#' # using attach makes the results directly available
#' attach(gensvm.train.test.split(x, y))
#' fit <- gensvm(x.train, y.train)
#' gensvm.accuracy(y.test, predict(fit, x.test))
#'
gensvm.train.test.split <- function(x, y=NULL, train.size=NULL, test.size=NULL,
shuffle=TRUE, random.state=NULL,
return.idx=FALSE)
{
if (!is.null(y) && dim(as.matrix(x))[1] != dim(as.matrix(y))[1]) {
cat("Error: First dimension of x and y should be equal.\n")
return
}
n.objects <- dim(as.matrix(x))[1]
if (is.null(train.size) && is.null(test.size)) {
test.size <- round(0.25 * n.objects)
train.size <- n.objects - test.size
}
else if (is.null(train.size)) {
if (test.size > 0.0 && test.size < 1.0)
test.size <- round(n.objects * test.size)
train.size <- n.objects - test.size
}
else if (is.null(test.size)) {
if (train.size > 0.0 && train.size < 1.0)
train.size <- round(n.objects * train.size)
test.size <- n.objects - train.size
}
else {
if (train.size > 0.0 && train.size < 1.0)
train.size <- round(n.objects * train.size)
if (test.size > 0.0 && test.size < 1.0)
test.size <- round(n.objects * test.size)
}
if (!is.null(random.state))
set.seed(random.state)
if (shuffle) {
train.idx <- sample(n.objects, train.size)
diff <- setdiff(1:n.objects, train.idx)
test.idx <- sample(diff, test.size)
} else {
train.idx <- 1:train.size
diff <- setdiff(1:n.objects, train.idx)
test.idx <- diff[1:test.size]
}
x.train <- x[train.idx, ]
x.test <- x[test.idx, ]
if (!is.null(y)) {
if (is.matrix(y)) {
y.train <- y[train.idx, ]
y.test <- y[test.idx, ]
} else {
y.train <- y[train.idx]
y.test <- y[test.idx]
}
}
out <- list(
x.train = x.train,
x.test = x.test
)
if (!is.null(y)) {
out$y.train <- y.train
out$y.test <- y.test
}
if (return.idx) {
out$idx.train <- train.idx
out$idx.test <- test.idx
}
return(out)
}
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