# These functions define the Decision tree models.
# They are executed when this package is loaded via `.onLoad()` and modify the
# parsnip package's model environment.
# These functions are tested indirectly when the models are used. Since this
# function is executed on package startup, you can't execute them to test since
# they are already in the parsnip model database. We'll exclude them from
# coverage stats for this reason.
# nocov start
make_decision_tree_rpart <- function() {
parsnip::set_model_engine("decision_tree", mode = "censored regression", eng = "rpart")
parsnip::set_dependency(
"decision_tree",
eng = "rpart",
pkg = "pec",
mode = "censored regression"
)
parsnip::set_dependency(
"decision_tree",
eng = "rpart",
pkg = "censored",
mode = "censored regression"
)
parsnip::set_fit(
model = "decision_tree",
eng = "rpart",
mode = "censored regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "pec", fun = "pecRpart"),
defaults = list()
)
)
parsnip::set_encoding(
model = "decision_tree",
eng = "rpart",
mode = "censored regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "decision_tree",
eng = "rpart",
mode = "censored regression",
type = "time",
value = list(
pre = NULL,
post = unname,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit$rpart),
newdata = quote(new_data)
)
)
)
parsnip::set_pred(
model = "decision_tree",
eng = "rpart",
mode = "censored regression",
type = "survival",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "censored", fun = "survival_prob_pecRpart"),
args =
list(
object = rlang::expr(object),
new_data = rlang::expr(new_data),
eval_time = rlang::expr(eval_time)
)
)
)
}
make_decision_tree_partykit <- function() {
parsnip::set_model_engine("decision_tree", mode = "censored regression", eng = "partykit")
parsnip::set_dependency(
"decision_tree",
eng = "partykit",
pkg = "partykit",
mode = "censored regression"
)
parsnip::set_dependency(
"decision_tree",
eng = "partykit",
pkg = "censored",
mode = "censored regression"
)
parsnip::set_model_arg(
model = "decision_tree",
eng = "partykit",
parsnip = "tree_depth",
original = "maxdepth",
func = list(pkg = "dials", fun = "tree_depth"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "decision_tree",
eng = "partykit",
parsnip = "min_n",
original = "minsplit",
func = list(pkg = "dials", fun = "min_n"),
has_submodel = FALSE
)
parsnip::set_fit(
model = "decision_tree",
eng = "partykit",
mode = "censored regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "parsnip", fun = "ctree_train"),
defaults = list()
)
)
parsnip::set_encoding(
model = "decision_tree",
mode = "censored regression",
eng = "partykit",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "decision_tree",
eng = "partykit",
mode = "censored regression",
type = "time",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
parsnip::set_pred(
model = "decision_tree",
eng = "partykit",
mode = "censored regression",
type = "survival",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "censored", fun = "survival_prob_partykit"),
args = list(
object = rlang::expr(object),
new_data = rlang::expr(new_data),
eval_time = rlang::expr(eval_time)
)
)
)
}
# nocov end
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.