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#' @title RandomBot V2 Tuning Spaces
#'
#' @name mlr_tuning_spaces_rbv2
#'
#' @description
#' Tuning spaces from the `r cite_bib("binder_2020")` article.
#'
#' @source
#' `r format_bib("binder_2020")`
#'
#' @aliases
#' mlr_tuning_spaces_classif.glmnet.rbv2
#' mlr_tuning_spaces_classif.kknn.rbv2
#' mlr_tuning_spaces_classif.ranger.rbv2
#' mlr_tuning_spaces_classif.rpart.rbv2
#' mlr_tuning_spaces_classif.svm.rbv2
#' mlr_tuning_spaces_classif.xgboost.rbv2
#' mlr_tuning_spaces_regr.glmnet.rbv2
#' mlr_tuning_spaces_regr.kknn.rbv2
#' mlr_tuning_spaces_regr.ranger.rbv2
#' mlr_tuning_spaces_regr.rpart.rbv2
#' mlr_tuning_spaces_regr.svm.rbv2
#' mlr_tuning_spaces_regr.xgboost.rbv2
#'
#' @section glmnet tuning space:
#' `r rd_info(lts("classif.glmnet.rbv2"))`
#'
#' @section kknn tuning space:
#' `r rd_info(lts("classif.kknn.rbv2"))`
#'
#' @section ranger tuning space:
#' `r rd_info(lts("classif.ranger.rbv2"))`
#'
#' `mtry.power` is replaced by `mtry.ratio`.
#'
#' @section rpart tuning space:
#' `r rd_info(lts("classif.rpart.rbv2"))`
#'
#' @section svm tuning space:
#' `r rd_info(lts("classif.svm.rbv2"))`
#'
#' @section xgboost tuning space:
#' `r rd_info(lts("classif.xgboost.rbv2"))`
#'
#' @include mlr_tuning_spaces.R
NULL
# glmnet
vals = list(
alpha = to_tune(0, 1),
s = to_tune(1e-4, 1e3, logscale = TRUE)
)
add_tuning_space(
id = "classif.glmnet.rbv2",
values = vals,
tags = c("rbv2", "classification"),
learner = "classif.glmnet",
package = "mlr3learners",
label = "Classification GLM with RandomBot"
)
add_tuning_space(
id = "regr.glmnet.rbv2",
values = vals,
tags = c("rbv2", "regression"),
learner = "regr.glmnet",
package = "mlr3learners",
label = "Regression GLM with RandomBot"
)
# kknn
vals = list(
k = to_tune(1, 30)
)
add_tuning_space(
id = "classif.kknn.rbv2",
values = vals,
tags = c("rbv2", "classification"),
learner = "classif.kknn",
package = "mlr3learners",
label = "Classification KKNN with RandomBot"
)
add_tuning_space(
id = "regr.kknn.rbv2",
values = vals,
tags = c("rbv2", "regression"),
learner = "regr.kknn",
package = "mlr3learners",
label = "Regression KKNN with RandomBot"
)
# ranger
vals = list(
num.trees = to_tune(1, 2000),
replace = to_tune(p_lgl()),
sample.fraction = to_tune(0.1, 1),
mtry.ratio = to_tune(0, 1),
respect.unordered.factors = to_tune(c("ignore", "order", "partition")),
min.node.size = to_tune(1, 100),
splitrule = to_tune(c("gini", "extratrees")),
num.random.splits = to_tune(1, 100)
)
add_tuning_space(
id = "classif.ranger.rbv2",
values = vals,
tags = c("rbv2", "classification"),
learner = "classif.ranger",
package = "mlr3learners",
label = "Classification Ranger with RandomBot"
)
vals = list(
num.trees = to_tune(1, 2000),
replace = to_tune(p_lgl()),
sample.fraction = to_tune(0.1, 1),
mtry.ratio = to_tune(0, 1),
respect.unordered.factors = to_tune(c("ignore", "order", "partition")),
min.node.size = to_tune(1, 100),
num.random.splits = to_tune(1, 100)
)
add_tuning_space(
id = "regr.ranger.rbv2",
values = vals,
tags = c("rbv2", "regression"),
learner = "regr.ranger",
package = "mlr3learners",
label = "Regression Ranger with RandomBot"
)
# rpart
vals = list(
cp = to_tune(1e-4, 1, logscale = TRUE),
maxdepth = to_tune(1, 30),
minbucket = to_tune(1, 100),
minsplit = to_tune(1, 100)
)
add_tuning_space(
id = "classif.rpart.rbv2",
values = vals,
tags = c("rbv2", "classification"),
learner = "classif.rpart",
package = "mlr3learners",
label = "Classification Rpart with RandomBot"
)
add_tuning_space(
id = "regr.rpart.rbv2",
values = vals,
tags = c("rbv2", "regression"),
learner = "regr.rpart",
package = "mlr3learners",
label = "Regression Rpart with RandomBot"
)
# svm
vals = list(
kernel = to_tune(c("linear", "polynomial", "radial")),
cost = to_tune(1e-4, 1e3, logscale = TRUE),
gamma = to_tune(1e-4, 1e3, logscale = TRUE),
tolerance = to_tune(1e-4, 2, logscale = TRUE),
degree = to_tune(2, 5)
)
add_tuning_space(
id = "classif.svm.rbv2",
values = vals,
tags = c("rbv2", "classification"),
learner = "classif.svm",
package = "mlr3learners",
label = "Classification SVM with RandomBot"
)
add_tuning_space(
id = "regr.svm.rbv2",
values = vals,
tags = c("rbv2", "regression"),
learner = "regr.svm",
package = "mlr3learners",
label = "Regression SVM with RandomBot"
)
# xgboost
vals = list(
booster = to_tune(c("gblinear", "gbtree", "dart")),
nrounds = to_tune(7, 2981, logscale = TRUE),
eta = to_tune(1e-4, 1, logscale = TRUE),
gamma = to_tune(1e-5, 7, logscale = TRUE),
lambda = to_tune(1e-4, 1e3, logscale = TRUE),
alpha = to_tune(1e-4, 1e3, logscale = TRUE),
subsample = to_tune(0.1, 1),
max_depth = to_tune(1, 15),
min_child_weight = to_tune(1, 1e2, logscale = TRUE),
colsample_bytree = to_tune(0.01, 1),
colsample_bylevel = to_tune(0.01, 1),
rate_drop = to_tune(0, 1),
skip_drop = to_tune(0, 1)
)
add_tuning_space(
id = "classif.xgboost.rbv2",
values = vals,
tags = c("rbv2", "classification"),
learner = "classif.xgboost",
package = "mlr3learners",
label = "Classification XGBoost with RandomBot"
)
add_tuning_space(
id = "regr.xgboost.rbv2",
values = vals,
tags = c("rbv2", "regression"),
learner = "regr.xgboost",
package = "mlr3learners",
label = "Regression XGBoost with RandomBot"
)
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