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#' @title Neural Network Regression Learner
#'
#' @name mlr_learners_regr.nnet
#'
#' @description
#' Single Layer Neural Network.
#' Calls [nnet::nnet.formula()] from package \CRANpkg{nnet}.
#'
#' Note that modern neural networks with multiple layers are connected
#' via package [mlr3keras](https://github.com/mlr-org/mlr3keras).
#'
#' @templateVar id regr.nnet
#' @template learner
#'
#' @section Initial parameter values:
#' - `size`:
#' - Adjusted default: 3L.
#' - Reason for change: no default in `nnet()`.
#'
#' @section Custom mlr3 parameters:
#' - `formula`: if not provided, the formula is set to `task$formula()`.
#'
#' @references
#' `r format_bib("ripley_1996")`
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerRegrNnet = R6Class("LearnerRegrNnet",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
Hess = p_lgl(default = FALSE, tags = "train"),
MaxNWts = p_int(1L, default = 1000L, tags = "train"),
Wts = p_uty(tags = "train"),
abstol = p_dbl(default = 1.0e-4, tags = "train"),
censored = p_lgl(default = FALSE, tags = "train"),
contrasts = p_uty(default = NULL, tags = "train"),
decay = p_dbl(default = 0, tags = "train"),
mask = p_uty(tags = "train"),
maxit = p_int(1L, default = 100L, tags = "train"),
na.action = p_uty(tags = "train"),
rang = p_dbl(default = 0.7, tags = "train"),
reltol = p_dbl(default = 1.0e-8, tags = "train"),
size = p_int(0L, default = 3L, tags = "train"),
skip = p_lgl(default = FALSE, tags = "train"),
subset = p_uty(tags = "train"),
trace = p_lgl(default = TRUE, tags = "train"),
formula = p_uty(tags = "train")
)
ps$values = list(size = 3L)
super$initialize(
id = "regr.nnet",
packages = c("mlr3learners", "nnet"),
feature_types = c("numeric", "factor", "ordered", "integer"),
predict_types = c("response"),
param_set = ps,
properties = c("weights"),
label = "Single Layer Neural Network",
man = "mlr3learners::mlr_learners_regr.nnet"
)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
if ("weights" %in% task$properties) {
pv = insert_named(pv, list(weights = task$weights$weight))
}
if (is.null(pv$formula)) {
pv$formula = task$formula()
}
data = task$data()
# force linout = TRUE for regression
invoke(nnet::nnet.formula, data = data, linout = TRUE, .args = pv)
},
.predict = function(task) {
pv = self$param_set$get_values(tags = "predict")
newdata = ordered_features(task, self)
prediction = invoke(predict, self$model, newdata = newdata, .args = pv)
list(response = as.numeric(prediction))
}
)
)
#' @include aaa.R
learners[["regr.nnet"]] = LearnerRegrNnet
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