#' @title Regression Decision Table Learner
#' @author damirpolat
#' @name mlr_learners_regr.decision_table
#'
#' @description
#' Simple Decision Table majority regressor.
#' Calls [RWeka::make_Weka_classifier()] from \CRANpkg{RWeka}.
#'
#' @section Initial parameter values:
#' - `E`:
#' - Has only 2 out of 4 original evaluation measures : rmse and mae with rmse being the default
#' - Reason for change: this learner should only contain evaluation measures appropriate for regression tasks
#'
#' @section Custom mlr3 parameters:
#' - `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
#'
#' - `P_best`:
#' - original id: P
#'
#' - `D_best`:
#' - original id: D
#'
#' - `N_best`:
#' - original id: N
#'
#' - `S_best`:
#' - original id: S
#'
#' - Reason for change: This learner contains changed ids of the following control arguments
#' since their ids contain irregular pattern
#'
#'
#' @templateVar id regr.decision_table
#' @template learner
#'
#' @references
#' `r format_bib("Kohavi1995")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerRegrDecisionTable = R6Class("LearnerRegrDecisionTable",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
param_set = ps(
subset = p_uty(tags = "train"),
na.action = p_uty(tags = "train"),
S = p_fct(default = "BestFirst", levels = c("BestFirst", "GreedyStepwise"), tags = "train"),
X = p_int(default = 1L, tags = "train"),
E = p_fct(default = "rmse", levels = c("rmse", "mae"), tags = "train"),
I = p_lgl(tags = "train"),
R = p_lgl(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"),
P_best = p_uty(depends = (S == "BestFirst"), tags = "train"),
D_best = p_fct(default = "1", levels = c("0", "1", "2"), depends = (S == "BestFirst"),
tags = "train"),
N_best = p_int(depends = (S == "BestFirst"), tags = "train"),
S_best = p_int(default = 1L, depends = (S == "BestFirst"), tags = "train"),
options = p_uty(default = NULL, tags = "train")
)
param_set$values = list(E = "rmse")
super$initialize(
id = "regr.decision_table",
packages = "RWeka",
feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
predict_types = "response",
param_set = param_set,
properties = character(0L),
man = "mlr3extralearners::mlr_learners_regr.decision_table",
label = "Decision Table"
)
}
),
private = list(
.train = function(task) {
weka_learner = RWeka::make_Weka_classifier("weka/classifiers/rules/DecisionTable")
pars = self$param_set$get_values(tags = "train")
nested_pars = list(S = "_best")
rweka_train(task$data(), task$formula(), pars, weka_learner, nested_pars)
},
.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("regr.decision_table", LearnerRegrDecisionTable)
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