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Merge pull request #606 from tidymodels/tunable
move methods for `tunable()` from tune to parsnip
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‎DESCRIPTION

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Package: parsnip
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Title: A Common API to Modeling and Analysis Functions
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Version: 0.1.7.9002
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Version: 0.1.7.9003
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Authors@R: c(
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person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
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person("Davis", "Vaughan", , "[email protected]", role = "aut"),

‎R/tunable.R

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# Lazily registered in .onLoad()
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tunable_model_spec <- function(x, ...) {
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mod_env <- rlang::ns_env("parsnip")$parsnip
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if (is.null(x$engine)) {
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stop("Please declare an engine first using `set_engine()`.", call. = FALSE)
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}
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arg_name <- paste0(mod_type(x), "_args")
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if (!(any(arg_name == names(mod_env)))) {
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stop("The `parsnip` model database doesn't know about the arguments for ",
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"model `", mod_type(x), "`. Was it registered?",
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sep = "", call. = FALSE)
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}
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arg_vals <-
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mod_env[[arg_name]] %>%
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dplyr::filter(engine == x$engine) %>%
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dplyr::select(name = parsnip, call_info = func) %>%
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dplyr::full_join(
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tibble::tibble(name = c(names(x$args), names(x$eng_args))),
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by = "name"
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) %>%
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dplyr::mutate(
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source = "model_spec",
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component = mod_type(x),
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component_id = dplyr::if_else(name %in% names(x$args), "main", "engine")
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)
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if (nrow(arg_vals) > 0) {
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has_info <- purrr::map_lgl(arg_vals$call_info, is.null)
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rm_list <- !(has_info & (arg_vals$component_id == "main"))
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arg_vals <- arg_vals[rm_list,]
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}
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arg_vals %>% dplyr::select(name, call_info, source, component, component_id)
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}
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mod_type <- function(.mod) class(.mod)[class(.mod) != "model_spec"][1]
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# ------------------------------------------------------------------------------
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add_engine_parameters <- function(pset, engines) {
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is_engine_param <- pset$name %in% engines$name
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if (any(is_engine_param)) {
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engine_names <- pset$name[is_engine_param]
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pset <- pset[!is_engine_param,]
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pset <-
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dplyr::bind_rows(pset, engines %>% dplyr::filter(name %in% engines$name))
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}
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pset
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}
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c5_tree_engine_args <-
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tibble::tibble(
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name = c(
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"CF",
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"noGlobalPruning",
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"winnow",
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"fuzzyThreshold",
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"bands"
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),
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call_info = list(
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list(pkg = "dials", fun = "confidence_factor"),
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list(pkg = "dials", fun = "no_global_pruning"),
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list(pkg = "dials", fun = "predictor_winnowing"),
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list(pkg = "dials", fun = "fuzzy_thresholding"),
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list(pkg = "dials", fun = "rule_bands")
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),
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source = "model_spec",
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component = "decision_tree",
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component_id = "engine"
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)
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c5_boost_engine_args <- c5_tree_engine_args
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c5_boost_engine_args$component <- "boost_tree"
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xgboost_engine_args <-
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tibble::tibble(
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name = c(
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"alpha",
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"lambda",
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"scale_pos_weight"
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),
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call_info = list(
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list(pkg = "dials", fun = "penalty_L1"),
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list(pkg = "dials", fun = "penalty_L2"),
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list(pkg = "dials", fun = "scale_pos_weight")
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),
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source = "model_spec",
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component = "boost_tree",
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component_id = "engine"
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)
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ranger_engine_args <-
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tibble::tibble(
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name = c(
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"regularization.factor",
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"regularization.usedepth",
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"alpha",
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"minprop",
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"splitrule",
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"num.random.splits"
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),
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call_info = list(
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list(pkg = "dials", fun = "regularization_factor"),
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list(pkg = "dials", fun = "regularize_depth"),
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list(pkg = "dials", fun = "significance_threshold"),
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list(pkg = "dials", fun = "lower_quantile"),
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list(pkg = "dials", fun = "splitting_rule"),
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list(pkg = "dials", fun = "num_random_splits")
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),
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source = "model_spec",
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component = "rand_forest",
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component_id = "engine"
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)
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randomForest_engine_args <-
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tibble::tibble(
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name = c("maxnodes"),
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call_info = list(
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list(pkg = "dials", fun = "max_nodes")
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),
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source = "model_spec",
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component = "rand_forest",
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component_id = "engine"
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)
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earth_engine_args <-
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tibble::tibble(
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name = c("nk"),
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call_info = list(
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list(pkg = "dials", fun = "max_num_terms")
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),
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source = "model_spec",
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component = "mars",
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component_id = "engine"
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)
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# ------------------------------------------------------------------------------
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# Lazily registered in .onLoad()
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tunable_linear_reg <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "glmnet") {
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res$call_info[res$name == "mixture"] <-
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list(list(pkg = "dials", fun = "mixture", range = c(0.05, 1.00)))
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}
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res
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}
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# Lazily registered in .onLoad()
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tunable_logistic_reg <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "glmnet") {
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res$call_info[res$name == "mixture"] <-
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list(list(pkg = "dials", fun = "mixture", range = c(0.05, 1.00)))
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}
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res
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}
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# Lazily registered in .onLoad()
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tunable_multinomial_reg <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "glmnet") {
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res$call_info[res$name == "mixture"] <-
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list(list(pkg = "dials", fun = "mixture", range = c(0.05, 1.00)))
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}
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res
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}
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# Lazily registered in .onLoad()
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tunable_boost_tree <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "xgboost") {
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res <- add_engine_parameters(res, xgboost_engine_args)
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res$call_info[res$name == "sample_size"] <-
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list(list(pkg = "dials", fun = "sample_prop"))
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} else {
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if (x$engine == "C5.0") {
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res <- add_engine_parameters(res, c5_boost_engine_args)
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res$call_info[res$name == "trees"] <-
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list(list(pkg = "dials", fun = "trees", range = c(1, 100)))
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res$call_info[res$name == "sample_size"] <-
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list(list(pkg = "dials", fun = "sample_prop"))
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}
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}
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res
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}
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# Lazily registered in .onLoad()
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tunable_rand_forest <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "ranger") {
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res <- add_engine_parameters(res, ranger_engine_args)
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}
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if (x$engine == "randomForest") {
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res <- add_engine_parameters(res, randomForest_engine_args)
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}
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res
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}
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# Lazily registered in .onLoad()
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tunable_mars <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "earth") {
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res <- add_engine_parameters(res, earth_engine_args)
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}
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res
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}
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# Lazily registered in .onLoad()
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tunable_decision_tree <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "C5.0") {
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res <- add_engine_parameters(res, c5_tree_engine_args)
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}
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res
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}
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# Lazily registered in .onLoad()
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tunable_svm_poly <- function(x, ...) {
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res <- NextMethod()
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if (x$engine == "kernlab") {
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res$call_info[res$name == "degree"] <-
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list(list(pkg = "dials", fun = "prod_degree", range = c(1L, 3L)))
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}
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res
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}

‎R/zzz.R

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# `tune_args.model_spec()` moved from tune to parsnip
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vctrs::s3_register("generics::tune_args", "model_spec", tune_args_model_spec)
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}
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# - If tune isn't installed, register the method (`packageVersion()` will error here)
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# - If tune >= 0.1.6.9002 is installed, register the method
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should_register_tunable_method <- tryCatch(
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expr = utils::packageVersion("tune") >= "0.1.6.9002",
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error = function(cnd) TRUE
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)
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if (should_register_tunable_method) {
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# `tunable.model_spec()` and friends moved from tune to parsnip
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vctrs::s3_register("generics::tunable", "model_spec", tunable_model_spec)
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vctrs::s3_register("generics::tunable", "linear_reg", tunable_linear_reg)
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vctrs::s3_register("generics::tunable", "logistic_reg", tunable_logistic_reg)
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vctrs::s3_register("generics::tunable", "multinomial_reg", tunable_multinomial_reg)
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vctrs::s3_register("generics::tunable", "boost_tree", tunable_boost_tree)
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vctrs::s3_register("generics::tunable", "rand_forest", tunable_rand_forest)
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vctrs::s3_register("generics::tunable", "mars", tunable_mars)
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vctrs::s3_register("generics::tunable", "decision_tree", tunable_decision_tree)
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vctrs::s3_register("generics::tunable", "svm_poly", tunable_svm_poly)
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}
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}
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