# These functions define the Random Forest 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_rand_forest_partykit <- function() {
parsnip::set_model_engine(
"rand_forest",
mode = "censored regression",
eng = "partykit"
)
parsnip::set_dependency(
"rand_forest",
eng = "partykit",
pkg = "partykit",
mode = "censored regression"
)
parsnip::set_dependency(
"rand_forest",
eng = "partykit",
pkg = "modeltools",
mode = "censored regression"
)
parsnip::set_dependency(
"rand_forest",
eng = "partykit",
pkg = "censored",
mode = "censored regression"
)
parsnip::set_model_arg(
model = "rand_forest",
eng = "partykit",
parsnip = "trees",
original = "ntree",
func = list(pkg = "dials", fun = "trees"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rand_forest",
eng = "partykit",
parsnip = "min_n",
original = "minsplit",
func = list(pkg = "dials", fun = "min_n"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rand_forest",
eng = "partykit",
parsnip = "mtry",
original = "mtry",
func = list(pkg = "dials", fun = "mtry"),
has_submodel = FALSE
)
parsnip::set_fit(
model = "rand_forest",
eng = "partykit",
mode = "censored regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "parsnip", fun = "cforest_train"),
defaults = list()
)
)
parsnip::set_encoding(
model = "rand_forest",
mode = "censored regression",
eng = "partykit",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "rand_forest",
eng = "partykit",
mode = "censored regression",
type = "time",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data),
type = "response"
)
)
)
parsnip::set_pred(
model = "rand_forest",
eng = "partykit",
mode = "censored regression",
type = "survival",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "censored", fun = "survival_prob_partykit"),
args = list(
object = rlang::expr(object),
new_data = rlang::expr(new_data),
eval_time = rlang::expr(eval_time)
)
)
)
}
make_rand_forest_aorsf <- function() {
parsnip::set_model_engine(
"rand_forest",
mode = "censored regression",
eng = "aorsf"
)
parsnip::set_dependency(
"rand_forest",
eng = "aorsf",
pkg = "aorsf",
mode = "censored regression"
)
parsnip::set_dependency(
"rand_forest",
eng = "aorsf",
pkg = "censored",
mode = "censored regression"
)
parsnip::set_model_arg(
model = "rand_forest",
eng = "aorsf",
parsnip = "trees",
original = "n_tree",
func = list(pkg = "dials", fun = "trees"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rand_forest",
eng = "aorsf",
parsnip = "min_n",
original = "leaf_min_obs",
func = list(pkg = "dials", fun = "min_n"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rand_forest",
eng = "aorsf",
parsnip = "mtry",
original = "mtry",
func = list(pkg = "dials", fun = "mtry"),
has_submodel = FALSE
)
parsnip::set_fit(
model = "rand_forest",
eng = "aorsf",
mode = "censored regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "aorsf", fun = "orsf"),
defaults = list()
)
)
parsnip::set_encoding(
model = "rand_forest",
mode = "censored regression",
eng = "aorsf",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "rand_forest",
eng = "aorsf",
mode = "censored regression",
type = "time",
value = list(
pre = NULL,
post = function(x, object) {
as.vector(x)
},
func = c(fun = "predict"),
args = list(
object = rlang::expr(object$fit),
new_data = rlang::expr(new_data),
pred_type = "time",
na_action = "pass"
)
)
)
parsnip::set_pred(
model = "rand_forest",
eng = "aorsf",
mode = "censored regression",
type = "survival",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "censored", fun = "survival_prob_orsf"),
args = list(
object = rlang::expr(object),
new_data = rlang::expr(new_data),
eval_time = rlang::expr(eval_time)
)
)
)
}
# nocov end
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