#FIXME: I have no idea which routine internally prints to which fucking stream
# but neither verbose=FALSE can sicth off the iteration output in all case, nor
# can I suppress it with capture.output or suppressMessages
#' @export
makeRLearner.classif.bartMachine = function() {
makeRLearnerClassif(
cl = "classif.bartMachine",
package = "bartMachine",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "num_trees", default = 50L, lower = 1L),
makeIntegerLearnerParam(id = "num_burn_in", default = 250L, lower = 0L),
makeIntegerLearnerParam(id = "num_iterations_after_burn_in", default = 1000L, lower = 0L),
makeNumericLearnerParam(id = "alpha", default = 0.95, lower = 0),
makeNumericLearnerParam(id = "beta", default = 2, lower = 0),
makeNumericLearnerParam(id = "k", default = 2, lower = 0),
makeNumericLearnerParam(id = "q", default = 0.9, lower = 0, upper = 1),
makeNumericLearnerParam(id = "prob_rule_class", default = 0.5, lower = 0, upper = 1),
makeNumericVectorLearnerParam(id = "mh_prob_steps", default = c(2.5, 2.5, 4) / 9, len = 3L),
makeLogicalLearnerParam(id = "debug_log", default = FALSE, tunable = FALSE),
makeLogicalLearnerParam(id = "run_in_sample", default = TRUE),
makeNumericVectorLearnerParam(id = "cov_prior_vec"),
makeLogicalLearnerParam(id = "use_missing_data", default = FALSE),
makeIntegerLearnerParam(id = "num_rand_samps_in_library", default = 10000, lower = 1),
makeLogicalLearnerParam(id = "use_missing_data_dummies_as_covars", default = FALSE),
makeLogicalLearnerParam(id = "replace_missing_data_with_x_j_bar", default = FALSE),
makeLogicalLearnerParam(id = "impute_missingness_with_rf_impute", default = FALSE),
makeLogicalLearnerParam(id = "impute_missingness_with_x_j_bar_for_lm", default = TRUE),
makeLogicalLearnerParam(id = "mem_cache_for_speed", default = TRUE),
makeLogicalLearnerParam(id = "serialize", default = FALSE),
makeIntegerLearnerParam(id = "seed", tunable = FALSE),
makeLogicalLearnerParam(id = "verbose", default = TRUE, tunable = FALSE)
),
par.vals = list("use_missing_data" = TRUE),
properties = c("numerics", "prob", "twoclass", "factors", "missings"),
name = "Bayesian Additive Regression Trees",
short.name = "bartmachine",
note = "`use_missing_data` has been set to `TRUE` by default to allow missing data support.",
callees = c("bartMachine", "predict.bartMachine")
)
}
#' @export
trainLearner.classif.bartMachine = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
y = d$target
td = getTaskDesc(.task)
levs = c(td$positive, td$negative)
y = factor(y, levels = levs)
bartMachine::bartMachine(X = d$data, y = y, ...)
}
#' @export
predictLearner.classif.bartMachine = function(.learner, .model, .newdata, ...) {
td = .model$task.desc
levs = c(td$positive, td$negative)
if (.learner$predict.type == "prob"){
p = predict(.model$learner.model, new_data = .newdata, type = "prob", ...)
y = propVectorToMatrix(1 - p, levs)
} else {
y = predict(.model$learner.model, new_data = .newdata, type = "class", ...)
y = factor(y, levs)
}
return(y)
}
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