#' @title Classification BART (Bayesian Additive Regression Trees) Learner
#' @author ck37
#' @name mlr_learners_classif.bart
#'
#' @description
#' Bayesian Additive Regression Trees are similar to gradient boosting algorithms.
#' The classification problem is solved by 0-1 encoding of the two-class targets and setting the
#' decision threshold to p = 0.5 during the prediction phase.
#' Calls [dbarts::bart()] from \CRANpkg{dbarts}.
#'
#' @template learner
#' @templateVar id classif.bart
#'
#' @section Parameter Changes:
#' * Parameter: keeptrees
#'
#' * Original: FALSE
#' * New: TRUE
#' * Reason: Required for prediction
#'
#' * Parameter: offset
#' * The parameter is removed, because only `dbarts::bart2` allows an offset during training,
#' and therefore the offset parameter in `dbarts:::predict.bart` is irrelevant for
#' `dbarts::dbart`.
#'
#' * Parameter: nchain, combineChains, combinechains
#' * The parameters are removed as parallelization of multiple models is handled by future.
#'
#' * Parameter: sigest, sigdf, sigquant, keeptres
#' * Regression only.
#'
#' @references
#' `r format_bib("sparapani2021nonparametric", "chipman2010bart")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerClassifBart = R6Class("LearnerClassifBart",
inherit = LearnerClassif,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(
ntree = p_int(default = 200L, lower = 1L, tags = "train"),
k = p_dbl(default = 2.0, lower = 0, tags = "train"),
power = p_dbl(default = 2.0, lower = 0, tags = "train"),
base = p_dbl(default = 0.95, lower = 0, upper = 1, tags = "train"),
binaryOffset = p_dbl(default = 0.0, tags = "train"),
ndpost = p_int(default = 1000L, lower = 1L, tags = "train"),
nskip = p_int(default = 100L, lower = 0L, tags = "train"),
printevery = p_int(default = 100L, lower = 0L, tags = "train"),
keepevery = p_int(default = 1L, lower = 1L, tags = "train"),
keeptrainfits = p_lgl(default = TRUE, tags = "train"),
usequants = p_lgl(default = FALSE, tags = "train"),
numcut = p_int(default = 100L, lower = 1L, tags = "train"),
printcutoffs = p_int(default = 0, tags = "train"),
verbose = p_lgl(default = FALSE, tags = "train"),
nthread = p_int(default = 1L, tags = c("train", "threads")),
keepcall = p_lgl(default = TRUE, tags = "train"),
sampleronly = p_lgl(default = FALSE, tags = "train"),
seed = p_int(default = NA_integer_, tags = "train", special_vals = list(NA_integer_)),
proposalprobs = p_uty(default = NULL, tags = "train"),
splitprobs = p_uty(default = NULL, tags = "train"),
keepsampler = p_lgl(default = NO_DEF, tags = "train")
)
super$initialize(
id = "classif.bart",
packages = c("mlr3extralearners", "dbarts"),
feature_types = c("integer", "numeric", "factor", "ordered"),
predict_types = c("response", "prob"),
param_set = ps,
properties = c("weights", "twoclass"),
man = "mlr3extralearners::mlr_learners_classif.bart",
label = "Bayesian Additive Regression Trees"
)
}
),
private = list(
.train = function(task) {
pars = self$param_set$get_values(tags = "train")
# Extact just the features from the task data.
x_train = task$data(cols = task$feature_names)
y_train = task$data(cols = task$target_names)
y_train = as.integer(y_train == task$positive)
if ("weights" %in% task$properties) {
pars$weights = task$weights$weight
}
invoke(
dbarts::bart,
x.train = x_train,
y.train = y_train,
keeptrees = TRUE,
.args = pars
)
},
.predict = function(task) {
pars = self$param_set$get_values(tags = "predict") # get parameters with tag "predict"
newdata = ordered_features(task, self)
# This will return a matrix of predictions, where each column is an observation
# and each row is a sample from the posterior.
p = colMeans(invoke(
predict,
self$model,
newdata = newdata,
.args = pars
))
if (self$predict_type == "response") {
list(response = ifelse(p >= 0.5, task$positive, task$negative))
} else {
list(prob = pprob_to_matrix(p, task))
}
}
)
)
.extralrns_dict$add("classif.bart", LearnerClassifBart)
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