#' @title Classification OneR Learner
#'
#' @name mlr_learners_classif.OneR
#'
#' @description
#' A [mlr3::LearnerClassif] implementing classification PART from package \CRANpkg{RWeka}.
#' Calls [RWeka::OneR()].
#'
#' @section Custom mlr3 defaults:
#' - `output_debug_info`:
#' - original id: output-debug-info
#'
#' - `do_not_check_capabilities`:
#' - original id: do-not-check-capabilities
#'
#' - `num_decimal_places`:
#' - original id: num-decimal-places
#'
#' - `batch_size`:
#' - original id: batch-size
#'
#' - Reason for change: This learner contains changed ids of the following control arguments
#' since their ids contain irregular pattern
#'
#' @templateVar id classif.OneR
#' @template section_dictionary_learner
#'
#' @references
#' Holte R (1993).
#' Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
#' \url{https://link.springer.com/content/pdf/10.1023/A:1022631118932.pdf}
#'
#' @export
LearnerClassifOneR = R6Class("LearnerClassifOneR",
inherit = LearnerClassif,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ParamSet$new(
params = list(
ParamUty$new(id = "subset", tags = c("train", "pars")),
ParamUty$new(id = "na.action", tags = c("train", "pars")),
ParamInt$new(id = "B", default = 6L, lower = 1L, tags = c("train", "control")),
ParamLgl$new(id = "output_debug_info", default = FALSE, tags = c("train", "control")),
ParamLgl$new(
id = "do_not_check_capabilities", default = FALSE,
tags = c("train", "control")),
ParamInt$new(
id = "num_decimal_places", default = 2L, lower = 1L,
tags = c("train", "control")),
ParamInt$new(id = "batch_size", default = 100L, lower = 1L, tags = c("train", "control")),
ParamUty$new(id = "options", default = NULL, tags = c("train", "pars"))
)
)
super$initialize(
id = "classif.OneR",
packages = "RWeka",
feature_types = c("numeric", "factor", "ordered"),
predict_types = c("response", "prob"),
param_set = ps,
properties = c("twoclass", "multiclass"),
man = "mlr3learners.rweka::mlr_learners_classif.OneR"
)
}
),
private = list(
.train = function(task) {
ctrl = self$param_set$get_values(tags = "control")
if (length(ctrl) > 0L) {
names(ctrl) = gsub("_", replacement = "-", x = names(ctrl))
ctrl = mlr3misc::invoke(RWeka::Weka_control, ctrl)
}
pars = self$param_set$get_values(tags = "pars")
f = task$formula()
data = task$data()
mlr3misc::invoke(RWeka::OneR, formula = f, data = data, control = ctrl, .args = pars)
},
.predict = function(task) {
response = NULL
prob = NULL
newdata = task$data(cols = task$feature_names)
if (self$predict_type == "response") {
response = mlr3misc::invoke(predict, self$model, newdata = newdata, type = "class")
} else {
prob = mlr3misc::invoke(predict, self$model, newdata = newdata, type = "prob")
}
PredictionClassif$new(task = task, response = response, prob = prob)
}
)
)
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