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Merged
topepo merged 12 commits into from
Oct 23, 2024
Merged

add rlang type checkers #950

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b73dc6e
add type checking files
topepo Oct 23, 2024
93b286f
remove newly unneeded checking functions
topepo Oct 23, 2024
b5a9fe9
snapshot updates from tidymodels/recipes#1381
topepo Oct 23, 2024
5bd2329
updates files
topepo Oct 23, 2024
022644e
basic replacements
topepo Oct 23, 2024
163236b
type checker replacements
topepo Oct 23, 2024
f66e2af
tidymodels/tailor#53
topepo Oct 23, 2024
d2d9810
Update R/checks.R
topepo Oct 23, 2024
9474ee8
add remote to get proper error messages
topepo Oct 23, 2024
ff1494f
typo
topepo Oct 23, 2024
1533820
update remotes?
topepo Oct 23, 2024
723f65a
only test snapshots with more recent version of R *with* rankdeficient
topepo Oct 23, 2024
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15 changes: 9 additions & 6 deletions DESCRIPTION
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Expand Up @@ -4,7 +4,7 @@ Version: 1.2.1.9000
Authors@R: c(
person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-2402-136X")),
person(given = "Posit Software, PBC", role = c("cph", "fnd"))
person("Posit Software, PBC", role = c("cph", "fnd"))
)
Description: The ability to tune models is important. 'tune' contains
functions and classes to be used in conjunction with other
Expand All @@ -27,12 +27,12 @@ Imports:
ggplot2,
glue (>= 1.6.2),
GPfit,
hardhat (>= 1.2.0),
hardhat (>= 1.4.0.9002),
lifecycle (>= 1.0.0),
parsnip (>= 1.2.0),
parsnip (>= 1.2.1.9003),
purrr (>= 1.0.0),
recipes (>= 1.0.4),
rlang (>= 1.1.0),
recipes (>= 1.1.0.9001),
rlang (>= 1.1.4),
rsample (>= 1.2.1.9000),
tailor,
tibble (>= 3.1.0),
Expand All @@ -57,8 +57,11 @@ Suggests:
xgboost,
xml2
Remotes:
tidymodels/hardhat,
tidymodels/parsnip,
tidymodels/recipes,
tidymodels/rsample,
tidymodels/tailor,
tidymodels/tailor,
tidymodels/workflows
Config/Needs/website: pkgdown, tidymodels, kknn, doParallel, doFuture,
tidyverse/tidytemplate
Expand Down
1 change: 1 addition & 0 deletions NAMESPACE
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Expand Up @@ -244,6 +244,7 @@ export(tune_bayes)
export(tune_grid)
export(val_class_and_single)
export(val_class_or_null)
import(rlang)
import(vctrs)
import(workflows)
importFrom(GPfit,GP_fit)
Expand Down
2 changes: 1 addition & 1 deletion R/0_imports.R
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Expand Up @@ -22,7 +22,7 @@
#' @importFrom cli cli_inform cli_warn cli_abort qty
#' @importFrom foreach foreach getDoParName %dopar%
#' @importFrom tibble obj_sum size_sum

#' @import rlang

# ------------------------------------------------------------------------------
# Only a small number of functions in workflows.
Expand Down
10 changes: 5 additions & 5 deletions R/acquisition.R
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Expand Up @@ -74,8 +74,8 @@ print.prob_improve <- function(x, ...) {
#' @export
predict.prob_improve <-
function(object, new_data, maximize, iter, best, ...) {
check_direction(maximize)
check_best(best)
check_bool(maximize)
check_number_decimal(best, allow_infinite = FALSE)

if (is.function(object$trade_off)) {
trade_off <- object$trade_off(iter)
Expand Down Expand Up @@ -126,8 +126,8 @@ exp_improve <- function(trade_off = 0, eps = .Machine$double.eps) {

#' @export
predict.exp_improve <- function(object, new_data, maximize, iter, best, ...) {
check_direction(maximize)
check_best(best)
check_bool(maximize)
check_number_decimal(best, allow_infinite = FALSE)

if (is.function(object$trade_off)) {
trade_off <- object$trade_off(iter)
Expand Down Expand Up @@ -177,7 +177,7 @@ conf_bound <- function(kappa = 0.1) {

#' @export
predict.conf_bound <- function(object, new_data, maximize, iter, ...) {
check_direction(maximize)
check_bool(maximize)

if (is.function(object$kappa)) {
kappa <- object$kappa(iter)
Expand Down
31 changes: 10 additions & 21 deletions R/checks.R
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Expand Up @@ -493,26 +493,6 @@ get_objective_name <- function(x, metrics) {
x
}


# ------------------------------------------------------------------------------
# acq functions

check_direction <- function(x) {
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@topepo topepo Oct 23, 2024

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DELETE

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@simonpcouch simonpcouch Oct 23, 2024

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SO SATISFYING

if (!is.logical(x) || length(x) != 1) {
rlang::abort("`maximize` should be a single logical.")
}
invisible(NULL)
}


check_best <- function(x) {
if (!is.numeric(x) || length(x) != 1 || is.na(x)) {
rlang::abort("`best` should be a single, non-missing numeric.")
}
invisible(NULL)
}


# ------------------------------------------------------------------------------

check_class_or_null <- function(x, cls = "numeric") {
Expand All @@ -537,6 +517,7 @@ val_class_or_null <- function(x, cls = "numeric", where = NULL) {
}
invisible(NULL)
}
# TODO remove this once finetune is updated
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@topepo topepo Oct 23, 2024

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Note to myself and @simonpcouch to make similar changes to finetune so that we can remove these three checking functions


check_class_and_single <- function(x, cls = "numeric") {
isTRUE(inherits(x, cls) & length(x) == 1)
Expand All @@ -558,7 +539,7 @@ val_class_and_single <- function(x, cls = "numeric", where = NULL) {
}
invisible(NULL)
}

# TODO remove this once finetune is updated

# Check the data going into the GP. If there are all missing values, fail. If some
# are missing, remove them and send a warning. If all metrics are the same, fail.
Expand Down Expand Up @@ -644,3 +625,11 @@ check_eval_time <- function(eval_time, metrics) {
invisible(NULL)

}

check_time_limit_arg <- function(x, call = rlang::caller_env()) {
if (!inherits(x, c("logical", "numeric")) || length(x) != 1L) {
cli::cli_abort("{.arg time_limit} should be either a single numeric or
logical value.", call = call)
}
invisible(NULL)
}
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@simonpcouch simonpcouch Oct 23, 2024

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TIL that this control option existed😆

5 changes: 1 addition & 4 deletions R/compute_metrics.R
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Expand Up @@ -87,6 +87,7 @@ compute_metrics.tune_results <- function(x,
summarize = TRUE,
event_level = "first") {
rlang::check_dots_empty()
check_bool(summarize)
if (!".predictions" %in% names(x)) {
rlang::abort(paste0(
"`x` must have been generated with the ",
Expand Down Expand Up @@ -114,10 +115,6 @@ compute_metrics.tune_results <- function(x,
))
}

if (!inherits(summarize, "logical") || length(summarize) != 1L) {
rlang::abort("The `summarize` argument must be a single logical value.")
}

param_names <- .get_tune_parameter_names(x)
outcome_name <- .get_tune_outcome_names(x)

Expand Down
55 changes: 27 additions & 28 deletions R/control.R
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Expand Up @@ -38,15 +38,15 @@ control_grid <- function(verbose = FALSE, allow_par = TRUE,
# Any added arguments should also be added in superset control functions
# in other packages

# add options for seeds per resample
# add options for seeds per resample
check_bool(verbose)
check_bool(allow_par)
check_bool(save_pred)
check_bool(save_workflow)
check_string(event_level)
check_character(pkgs, allow_null = TRUE)
check_function(extract, allow_null = TRUE)

val_class_and_single(verbose, "logical", "control_grid()")
val_class_and_single(allow_par, "logical", "control_grid()")
val_class_and_single(save_pred, "logical", "control_grid()")
val_class_and_single(save_workflow, "logical", "control_grid()")
val_class_and_single(event_level, "character", "control_grid()")
val_class_or_null(pkgs, "character", "control_grid()")
val_class_or_null(extract, "function", "control_grid()")
val_parallel_over(parallel_over, "control_grid()")


Expand Down Expand Up @@ -241,26 +241,27 @@ control_bayes <-
# in other packages

# add options for seeds per resample
check_bool(verbose)
check_bool(verbose_iter)
check_bool(allow_par)
check_bool(save_pred)
check_bool(save_workflow)
check_bool(save_gp_scoring)
check_character(pkgs, allow_null = TRUE)
check_function(extract, allow_null = TRUE)
check_number_whole(no_improve, min = 0, allow_infinite = TRUE)
check_number_whole(uncertain, min = 0, allow_infinite = TRUE)
check_number_whole(seed)

check_time_limit_arg(time_limit)

val_class_and_single(verbose, "logical", "control_bayes()")
val_class_and_single(verbose_iter, "logical", "control_bayes()")
val_class_and_single(save_pred, "logical", "control_bayes()")
val_class_and_single(save_gp_scoring, "logical", "control_bayes()")
val_class_and_single(save_workflow, "logical", "control_bayes()")
val_class_and_single(no_improve, c("numeric", "integer"), "control_bayes()")
val_class_and_single(uncertain, c("numeric", "integer"), "control_bayes()")
val_class_and_single(seed, c("numeric", "integer"), "control_bayes()")
val_class_or_null(extract, "function", "control_bayes()")
val_class_and_single(time_limit, c("logical", "numeric"), "control_bayes()")
val_class_or_null(pkgs, "character", "control_bayes()")
val_class_and_single(event_level, "character", "control_bayes()")
val_parallel_over(parallel_over, "control_bayes()")
val_class_and_single(allow_par, "logical", "control_bayes()")


if (!is.infinite(uncertain) && uncertain > no_improve) {
cli::cli_alert_warning(
"Uncertainty sample scheduled after {uncertain} poor iterations but the search will stop after {no_improve}."
cli::cli_warn(
"Uncertainty sample scheduled after {uncertain} poor iterations but the
search will stop after {no_improve}."
)
}

Expand Down Expand Up @@ -296,13 +297,11 @@ print.control_bayes <- function(x, ...) {
# ------------------------------------------------------------------------------

val_parallel_over <- function(parallel_over, where) {
if (is.null(parallel_over)) {
return(invisible(NULL))
check_string(parallel_over, allow_null = TRUE)
if (!is.null(parallel_over)) {
rlang::arg_match0(parallel_over, c("resamples", "everything"), "parallel_over")
}

val_class_and_single(parallel_over, "character", where)
rlang::arg_match0(parallel_over, c("resamples", "everything"), "parallel_over")

invisible(NULL)
}

Expand Down
4 changes: 1 addition & 3 deletions R/extract.R
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Expand Up @@ -109,9 +109,7 @@ extract_spec_parsnip.tune_results <- function(x, ...) {
#' @rdname extract-tune
extract_recipe.tune_results <- function(x, ..., estimated = TRUE) {
check_empty_dots(...)
if (!rlang::is_bool(estimated)) {
rlang::abort("`estimated` must be a single `TRUE` or `FALSE`.")
}
check_bool(estimated)
extract_recipe(extract_workflow(x), estimated = estimated)
}
check_empty_dots <- function(...) {
Expand Down
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