#' @title Classification JRip Learner
#' @author henrifnk
#' @name mlr_learners_classif.JRip
#'
#' @description
#' Repeated Incremental Pruning to Produce Error Reduction.
#' Calls [RWeka::JRip()] from \CRANpkg{RWeka}.
#'
#' @template learner
#' @templateVar id classif.JRip
#'
#' @section Parameter changes:
#' - `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
#'
#' @references
#' `r format_bib("cohen1995fast")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerClassifJRip = R6Class("LearnerClassifJRip",
inherit = LearnerClassif,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
subset = p_uty(tags = "train"),
na.action = p_uty(tags = "train"),
F = p_int(default = 3L, lower = 2L, tags = "train"),
N = p_dbl(default = 2, lower = 0, tags = "train"),
O = p_int(default = 2L, lower = 1L, tags = "train"),
D = p_lgl(default = FALSE, tags = "train"),
S = p_int(default = 1L, lower = 1L, tags = "train"),
E = p_lgl(default = FALSE, tags = "train"),
P = p_lgl(default = FALSE, tags = "train"),
output_debug_info = p_lgl(default = FALSE, tags = "train"),
do_not_check_capabilities = p_lgl(default = FALSE,
tags = "train"),
num_decimal_places = p_int(default = 2L, lower = 1L,
tags = "train"),
batch_size = p_int(default = 100L, lower = 1L, tags = "train"),
options = p_uty(default = NULL, tags = "train")
)
super$initialize(
id = "classif.JRip",
packages = c("mlr3extralearners", "RWeka"),
feature_types = c("numeric", "factor", "ordered", "integer"),
predict_types = c("response", "prob"),
param_set = ps,
properties = c("twoclass", "multiclass"),
man = "mlr3extralearners::mlr_learners_classif.JRip",
label = "Propositional Rule Learner."
)
}
),
private = list(
.train = function(task) {
weka_learner = RWeka::JRip
pars = self$param_set$get_values(tags = "train")
rweka_train(task$data(), task$formula(), pars, weka_learner)
},
.predict = function(task) {
pars = self$param_set$get_values(tags = "predict")
newdata = ordered_features(task, self)
rweka_predict(newdata, pars, self$predict_type, self$model)
}
)
)
.extralrns_dict$add("classif.JRip", LearnerClassifJRip)
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