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#' @title Multinomial log-linear learner via neural networks
#'
#' @name mlr_learners_classif.multinom
#'
#' @description
#' Multinomial log-linear models via neural networks.
#' Calls [nnet::multinom()] from package \CRANpkg{nnet}.
#'
#' @templateVar id classif.multinom
#' @template learner
#'
#' @export
#' @template seealso_learner
#' @template example
LearnerClassifMultinom = R6Class("LearnerClassifMultinom",
inherit = LearnerClassif,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
Hess = p_lgl(default = FALSE, tags = "train"),
abstol = p_dbl(default = 1.0e-4, tags = "train"),
censored = p_lgl(default = FALSE, tags = "train"),
decay = p_dbl(default = 0, tags = "train"),
entropy = p_lgl(default = FALSE, tags = "train"),
mask = p_uty(tags = "train"),
maxit = p_int(1L, default = 100L, tags = "train"),
MaxNWts = p_int(1L, default = 1000L, tags = "train"),
model = p_lgl(default = FALSE, tags = "train"),
linout = p_lgl(default = FALSE, tags = "train"),
rang = p_dbl(default = 0.7, tags = "train"),
reltol = p_dbl(default = 1.0e-8, tags = "train"),
size = p_int(1L, tags = "train"),
skip = p_lgl(default = FALSE, tags = "train"),
softmax = p_lgl(default = FALSE, tags = "train"),
summ = p_fct(c("0", "1", "2", "3"), default = "0", tags = "train"),
trace = p_lgl(default = TRUE, tags = "train"),
Wts = p_uty(tags = "train")
)
super$initialize(
id = "classif.multinom",
param_set = ps,
predict_types = c("response", "prob"),
feature_types = c("logical", "integer", "numeric", "factor"),
properties = c("weights", "twoclass", "multiclass", "loglik"),
packages = c("mlr3learners", "nnet"),
label = "Multinomial Log-Linear Model",
man = "mlr3learners::mlr_learners_classif.multinom"
)
},
#' @description
#' Extract the log-likelihood (e.g., via [stats::logLik()] from the fitted model.
loglik = function() {
extract_loglik(self)
}
),
private = list(
.train = function(task) {
pv = self$param_set$get_values(tags = "train")
if ("weights" %in% task$properties) {
pv$weights = task$weights$weight
}
if (!is.null(pv$summ)) {
pv$summ = as.integer(pv$summ)
}
# nnet does not handle formulas without env, we need to create it
# here to work with `summary()`.
pv$formula = reformulate(".", response = task$target_names)
invoke(nnet::multinom, data = task$data(), .args = pv)
},
.predict = function(task) {
pv = self$param_set$get_values(tags = "predict")
newdata = ordered_features(task, self)
if (self$predict_type == "response") {
response = invoke(predict, self$model, newdata = newdata, type = "class", .args = pv)
list(response = drop(response))
} else {
lvls = self$model$lev
prob = unname(invoke(predict, self$model, newdata = newdata, type = "probs", .args = pv))
# fix dimensions being dropped for n == 1 (https://github.com/mlr-org/mlr3/issues/883)
if (task$nrow == 1L) {
prob = matrix(prob, nrow = 1L)
}
if (length(lvls) == 2L) {
prob = pvec2mat(prob, lvls)
} else {
colnames(prob) = lvls
}
list(prob = prob)
}
}
)
)
#' @include aaa.R
learners[["classif.multinom"]] = LearnerClassifMultinom
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