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#' Label Powerset for multi-label Classification
#'
#' Create a Label Powerset model for multilabel classification.
#'
#' Label Powerset is a simple transformation method to predict multi-label data.
#' This is based on the multi-class approach to build a model where the classes
#' are each labelset.
#'
#' @family Transformation methods
#' @family Powerset
#' @param mdata A mldr dataset used to train the binary models.
#' @param base.algorithm A string with the name of the base algorithm. (Default:
#' \code{options("utiml.base.algorithm", "SVM")})
#' @param ... Others arguments passed to the base algorithm for all subproblems
#' @param cores Not used
#' @param seed An optional integer used to set the seed. (Default:
#' \code{options("utiml.seed", NA)})
#' @return An object of class \code{LPmodel} containing the set of fitted
#' models, including:
#' \describe{
#' \item{labels}{A vector with the label names.}
#' \item{model}{A multi-class model.}
#' }
#' @references
#' Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning
#' multi-label scene classification. Pattern Recognition, 37(9), 1757-1771.
#' @export
#'
#' @examples
#' model <- lp(toyml, "RANDOM")
#' pred <- predict(model, toyml)
lp <- function (mdata,
base.algorithm = getOption("utiml.base.algorithm", "SVM"), ...,
cores = getOption("utiml.cores", 1),
seed = getOption("utiml.seed", NA)) {
# Validations
if (!is(mdata, "mldr")) {
stop("First argument must be an mldr object")
}
# LP Model class
lpmodel <- list(labels = rownames(mdata$labels),
call = match.call(),
classes = mdata$labelsets)
lpmodel$model <- utiml_lapply(1, function (x){
#Due the seed
utiml_create_model(
utiml_prepare_data(
utiml_create_lp_data(mdata),
"mldLP", mdata$name, "lp", base.algorithm
), ...
)
}, 1, seed)[[1]]
class(lpmodel) <- "LPmodel"
lpmodel
}
#' Predict Method for Label Powerset
#'
#' This function predicts values based upon a model trained by \code{\link{lp}}.
#'
#' @param object Object of class '\code{LPmodel}'.
#' @param newdata An object containing the new input data. This must be a
#' matrix, data.frame or a mldr object.
#' @param probability Logical indicating whether class probabilities should be
#' returned. (Default: \code{getOption("utiml.use.probs", TRUE)})
#' @param ... Others arguments passed to the base algorithm prediction for all
#' subproblems.
#' @param cores Not used
#' @param seed An optional integer used to set the seed. (Default:
#' \code{options("utiml.seed", NA)})
#' @return An object of type mlresult, based on the parameter probability.
#' @seealso \code{\link[=lp]{Label Powerset (LP)}}
#' @export
#'
#' @examples
#' model <- lp(toyml, "RANDOM")
#' pred <- predict(model, toyml)
predict.LPmodel <- function(object, newdata,
probability = getOption("utiml.use.probs", TRUE),
..., cores = getOption("utiml.cores", 1),
seed = getOption("utiml.seed", NA)) {
# Validations
if (!is(object, "LPmodel")) {
stop("First argument must be a LPmodel object")
}
newdata <- utiml_newdata(newdata)
result <- utiml_lapply(1, function (x){
#Due the seed
utiml_predict_multiclass_model(object$model, newdata, object$labels,
probability, ...)
}, 1, seed)[[1]]
result
}
#' Print LP model
#' @param x The lp model
#' @param ... ignored
#'
#' @return No return value, called for print model's detail
#'
#' @export
print.LPmodel <- function(x, ...) {
cat("Label Powerset Model\n\nCall:\n")
print(x$call)
cat("\n1 Model: ",length(x$classes),"classes\n")
print(cbind.data.frame(classe=names(x$classes), instances=as.numeric(x$classes)))
}
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