#' Radial basis function support vector machines
#'
#' @description
#'
#' `svm_rbf()` defines a support vector machine model. For classification,
#' the model tries to maximize the width of the margin between classes using a
#' nonlinear class boundary. For regression, the model optimizes a robust loss
#' function that is only affected by very large model residuals and uses
#' nonlinear functions of the predictors. The function can fit classification
#' and regression models.
#'
#' \Sexpr[stage=render,results=rd]{parsnip:::make_engine_list("svm_rbf")}
#'
#' More information on how \pkg{parsnip} is used for modeling is at
#' \url{https://www.tidymodels.org/}.
#'
#' @inheritParams nearest_neighbor
#' @param engine A single character string specifying what computational engine
#' to use for fitting. Possible engines are listed below. The default for this
#' model is `"kernlab"`.
#' @param cost A positive number for the cost of predicting a sample within
#' or on the wrong side of the margin
#' @param rbf_sigma A positive number for radial basis function.
#' @param margin A positive number for the epsilon in the SVM insensitive
#' loss function (regression only)
#'
#' @templateVar modeltype svm_rbf
#' @template spec-details
#'
#' @template spec-references
#'
#' @seealso \Sexpr[stage=render,results=rd]{parsnip:::make_seealso_list("svm_rbf")}
#'
#' @examplesIf !parsnip:::is_cran_check()
#' show_engines("svm_rbf")
#'
#' svm_rbf(mode = "classification", rbf_sigma = 0.2)
#' @export
svm_rbf <-
function(mode = "unknown", engine = "kernlab",
cost = NULL, rbf_sigma = NULL, margin = NULL) {
args <- list(
cost = enquo(cost),
rbf_sigma = enquo(rbf_sigma),
margin = enquo(margin)
)
new_model_spec(
"svm_rbf",
args = args,
eng_args = NULL,
mode = mode,
user_specified_mode = !missing(mode),
method = NULL,
engine = engine,
user_specified_engine = !missing(engine)
)
}
# ------------------------------------------------------------------------------
#' @method update svm_rbf
#' @rdname parsnip_update
#' @export
update.svm_rbf <-
function(object,
parameters = NULL,
cost = NULL, rbf_sigma = NULL, margin = NULL,
fresh = FALSE,
...) {
args <- list(
cost = enquo(cost),
rbf_sigma = enquo(rbf_sigma),
margin = enquo(margin)
)
update_spec(
object = object,
parameters = parameters,
args_enquo_list = args,
fresh = fresh,
cls = "svm_rbf",
...
)
}
# ------------------------------------------------------------------------------
#' @export
translate.svm_rbf <- function(x, engine = x$engine, ...) {
x <- translate.default(x, engine = engine, ...)
# slightly cleaner code using
arg_vals <- x$method$fit$args
arg_names <- names(arg_vals)
# add checks to error trap or change things for this method
if (x$engine == "kernlab") {
# unless otherwise specified, classification models predict probabilities
if (x$mode == "classification" && !any(arg_names == "prob.model"))
arg_vals$prob.model <- TRUE
if (x$mode == "classification" && any(arg_names == "epsilon"))
arg_vals$epsilon <- NULL
# convert sigma and scale to a `kpar` argument.
if (any(arg_names == "sigma")) {
kpar <- expr(list())
kpar$sigma <- arg_vals$sigma
arg_vals$sigma <- NULL
arg_vals$kpar <- kpar
}
}
if (x$engine == "liquidSVM") {
# convert parameter arguments
if (any(arg_names == "sigma")) {
arg_vals$gammas <- rlang::quo(1 / !!sqrt(arg_vals$sigma))
arg_vals$sigma <- NULL
}
if (any(arg_names == "C")) {
arg_vals$lambdas <- arg_vals$C
arg_vals$C <- NULL
}
}
x$method$fit$args <- arg_vals
# worried about people using this to modify the specification
x
}
# ------------------------------------------------------------------------------
#' @export
check_args.svm_rbf <- function(object, call = rlang::caller_env()) {
invisible(object)
}
# ------------------------------------------------------------------------------
svm_reg_post <- function(results, object) {
results[,1]
}
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