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make_rule_fit <- function() {
parsnip::set_model_engine("rule_fit", "classification", "xrf")
parsnip::set_model_engine("rule_fit", "regression", "xrf")
parsnip::set_dependency("rule_fit", "xrf", "xrf", "classification")
parsnip::set_dependency("rule_fit", "xrf", "xrf", "regression")
parsnip::set_dependency("rule_fit", "xrf", "rules", "classification")
parsnip::set_dependency("rule_fit", "xrf", "rules", "regression")
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "tree_depth",
original = "max_depth",
func = list(pkg = "dials", fun = "tree_depth"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "trees",
original = "nrounds",
func = list(pkg = "dials", fun = "trees"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "learn_rate",
original = "eta",
func = list(pkg = "dials", fun = "learn_rate"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "mtry",
original = "colsample_bynode",
func = list(pkg = "dials", fun = "mtry"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "min_n",
original = "min_child_weight",
func = list(pkg = "dials", fun = "min_n"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "loss_reduction",
original = "gamma",
func = list(pkg = "dials", fun = "loss_reduction"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "sample_size",
original = "subsample",
func = list(pkg = "dials", fun = "sample_prop"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "penalty",
original = "lambda",
func = list(pkg = "dials", fun = "penalty"),
has_submodel = TRUE
)
parsnip::set_model_arg(
model = "rule_fit",
eng = "xrf",
parsnip = "stop_iter",
original = "early_stop",
func = list(pkg = "dials", fun = "stop_iter"),
has_submodel = FALSE
)
parsnip::set_fit(
model = "rule_fit",
eng = "xrf",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data", "xgb_control"),
func = c(pkg = "rules", fun = "xrf_fit"),
defaults = list()
)
)
parsnip::set_encoding(
model = "rule_fit",
eng = "xrf",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "rule_fit",
eng = "xrf",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = organize_xrf_pred,
func = c(fun = "xrf_pred"),
args = list(
object = quote(object),
new_data = quote(new_data),
lambda = quote(object$fit$lambda),
type = "response"
)
)
)
parsnip::set_fit(
model = "rule_fit",
eng = "xrf",
mode = "classification",
value = list(
interface = "formula",
protect = c("formula", "data", "xgb_control"),
func = c(pkg = "rules", fun = "xrf_fit"),
defaults = list()
)
)
parsnip::set_encoding(
model = "rule_fit",
eng = "xrf",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_pred(
model = "rule_fit",
eng = "xrf",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = NULL, fun = "xrf_pred"),
args = list(
object = quote(object),
new_data = quote(new_data),
lambda = quote(object$fit$lambda),
type = "response" # post-processed into classes
)
)
)
parsnip::set_pred(
model = "rule_fit",
eng = "xrf",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = NULL, fun = "xrf_pred"),
args = list(
object = quote(object),
new_data = quote(new_data),
lambda = quote(object$fit$lambda),
type = "prob"
)
)
)
}
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