Nothing
set_new_model("svm_rbf")
set_model_mode("svm_rbf", "classification")
set_model_mode("svm_rbf", "regression")
# ------------------------------------------------------------------------------
set_model_engine("svm_rbf", "classification", "kernlab")
set_model_engine("svm_rbf", "regression", "kernlab")
set_dependency("svm_rbf", "kernlab", "kernlab")
set_model_arg(
model = "svm_rbf",
eng = "kernlab",
parsnip = "cost",
original = "C",
func = list(pkg = "dials", fun = "cost", range = c(-10, 5)),
has_submodel = FALSE
)
set_model_arg(
model = "svm_rbf",
eng = "kernlab",
parsnip = "rbf_sigma",
original = "sigma",
func = list(pkg = "dials", fun = "rbf_sigma"),
has_submodel = FALSE
)
set_model_arg(
model = "svm_rbf",
eng = "kernlab",
parsnip = "margin",
original = "epsilon",
func = list(pkg = "dials", fun = "svm_margin"),
has_submodel = FALSE
)
set_fit(
model = "svm_rbf",
eng = "kernlab",
mode = "regression",
value = list(
interface = "formula",
data = c(formula = "x", data = "data"),
protect = c("x", "data"),
func = c(pkg = "kernlab", fun = "ksvm"),
defaults = list(kernel = "rbfdot")
)
)
set_fit(
model = "svm_rbf",
eng = "kernlab",
mode = "classification",
value = list(
interface = "formula",
data = c(formula = "x", data = "data"),
protect = c("x", "data"),
func = c(pkg = "kernlab", fun = "ksvm"),
defaults = list(kernel = "rbfdot")
)
)
set_encoding(
model = "svm_rbf",
eng = "kernlab",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "svm_rbf",
eng = "kernlab",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = svm_reg_post,
func = c(pkg = "kernlab", fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "response"
)
)
)
set_pred(
model = "svm_rbf",
eng = "kernlab",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "kernlab", fun = "predict"),
args = list(object = quote(object$fit), newdata = quote(new_data))
)
)
set_encoding(
model = "svm_rbf",
eng = "kernlab",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "svm_rbf",
eng = "kernlab",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "kernlab", fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "response"
)
)
)
set_pred(
model = "svm_rbf",
eng = "kernlab",
mode = "classification",
type = "prob",
value = list(
pre = NULL,
post = function(result, object) as_tibble(result),
func = c(pkg = "kernlab", fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
type = "probabilities"
)
)
)
set_pred(
model = "svm_rbf",
eng = "kernlab",
mode = "classification",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(pkg = "kernlab", fun = "predict"),
args = list(object = quote(object$fit), newdata = quote(new_data))
)
)
# ------------------------------------------------------------------------------
set_model_engine("svm_rbf", "classification", "liquidSVM")
set_model_engine("svm_rbf", "regression", "liquidSVM")
set_dependency("svm_rbf", "liquidSVM", "liquidSVM")
set_model_arg(
model = "svm_rbf",
eng = "liquidSVM",
parsnip = "cost",
original = "lambdas",
func = list(pkg = "dials", fun = "cost"),
has_submodel = FALSE
)
set_model_arg(
model = "svm_rbf",
eng = "liquidSVM",
parsnip = "rbf_sigma",
original = "gammas",
func = list(pkg = "dials", fun = "rbf_sigma"),
has_submodel = FALSE
)
set_fit(
model = "svm_rbf",
eng = "liquidSVM",
mode = "regression",
value = list(
interface = "data.frame",
protect = c("x", "y"),
func = c(pkg = "liquidSVM", fun = "svm"),
defaults = list(
folds = 1,
threads = 0
)
)
)
set_encoding(
model = "svm_rbf",
eng = "liquidSVM",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_fit(
model = "svm_rbf",
eng = "liquidSVM",
mode = "classification",
value = list(
interface = "data.frame",
protect = c("x", "y"),
func = c(pkg = "liquidSVM", fun = "svm"),
defaults = list(
folds = 1,
threads = 0
)
)
)
set_encoding(
model = "svm_rbf",
eng = "liquidSVM",
mode = "classification",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
set_pred(
model = "svm_rbf",
eng = "liquidSVM",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
set_pred(
model = "svm_rbf",
eng = "liquidSVM",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = quote(object$fit),
newdata = quote(new_data))
)
)
set_pred(
model = "svm_rbf",
eng = "liquidSVM",
mode = "classification",
type = "class",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data)
)
)
)
set_pred(
model = "svm_rbf",
eng = "liquidSVM",
mode = "classification",
type = "prob",
value = list(
pre = function(x, object) {
if (!object$fit$predict.prob) {
rlang::abort(
paste0("`svm` model does not appear to use class probabilities. Was ",
"the model fit with `predict.prob = TRUE`?")
)
}
x
},
post = function(result, object) {
res <- tibble::as_tibble(result)
names(res) <- object$lvl
res
},
func = c(fun = "predict"),
args =
list(
object = quote(object$fit),
newdata = quote(new_data),
predict.prob = TRUE
)
)
)
set_pred(
model = "svm_rbf",
eng = "liquidSVM",
mode = "classification",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = quote(object$fit),
newdata = quote(new_data))
)
)
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