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#' Fit multiple models via resampling
#'
#' [fit_resamples()] computes a set of performance metrics across one or more
#' resamples. It does not perform any tuning (see [tune_grid()] and
#' [tune_bayes()] for that), and is instead used for fitting a single
#' model+recipe or model+formula combination across many resamples.
#'
#' @inheritParams last_fit
#' @inheritParams tune_grid
#'
#' @param resamples An `rset` resampling object created from an `rsample`
#' function, such as [rsample::vfold_cv()].
#'
#' @param control A [control_resamples()] object used to fine tune the resampling
#' process.
#'
#' @inheritSection tune_grid Performance Metrics
#' @inheritSection tune_grid Obtaining Predictions
#' @inheritSection tune_grid Extracting Information
#' @template case-weights
#' @template censored-regression
#' @seealso [control_resamples()], [collect_predictions()], [collect_metrics()]
#' @examplesIf tune:::should_run_examples()
#' library(recipes)
#' library(rsample)
#' library(parsnip)
#' library(workflows)
#'
#' set.seed(6735)
#' folds <- vfold_cv(mtcars, v = 5)
#'
#' spline_rec <- recipe(mpg ~ ., data = mtcars) %>%
#' step_ns(disp) %>%
#' step_ns(wt)
#'
#' lin_mod <- linear_reg() %>%
#' set_engine("lm")
#'
#' control <- control_resamples(save_pred = TRUE)
#'
#' spline_res <- fit_resamples(lin_mod, spline_rec, folds, control = control)
#'
#' spline_res
#'
#' show_best(spline_res, metric = "rmse")
#'
#' # You can also wrap up a preprocessor and a model into a workflow, and
#' # supply that to `fit_resamples()` instead. Here, a workflows "variables"
#' # preprocessor is used, which lets you supply terms using dplyr selectors.
#' # The variables are used as-is, no preprocessing is done to them.
#' wf <- workflow() %>%
#' add_variables(outcomes = mpg, predictors = everything()) %>%
#' add_model(lin_mod)
#'
#' wf_res <- fit_resamples(wf, folds)
#' @export
fit_resamples <- function(object, ...) {
UseMethod("fit_resamples")
}
#' @export
fit_resamples.default <- function(object, ...) {
msg <- paste0(
"The first argument to [fit_resamples()] should be either ",
"a model or workflow."
)
rlang::abort(msg)
}
#' @export
#' @rdname fit_resamples
fit_resamples.model_spec <- function(object,
preprocessor,
resamples,
...,
metrics = NULL,
eval_time = NULL,
control = control_resamples()) {
if (rlang::is_missing(preprocessor) || !is_preprocessor(preprocessor)) {
rlang::abort(paste(
"To tune a model spec, you must preprocess",
"with a formula or recipe"
))
}
control <- parsnip::condense_control(control, control_resamples())
empty_ellipses(...)
wflow <- add_model(workflow(), object)
if (is_recipe(preprocessor)) {
wflow <- add_recipe(wflow, preprocessor)
} else if (rlang::is_formula(preprocessor)) {
wflow <- add_formula(wflow, preprocessor)
}
fit_resamples(
wflow,
resamples = resamples,
metrics = metrics,
eval_time = eval_time,
control = control
)
}
#' @rdname fit_resamples
#' @export
fit_resamples.workflow <- function(object,
resamples,
...,
metrics = NULL,
eval_time = NULL,
control = control_resamples()) {
empty_ellipses(...)
control <- parsnip::condense_control(control, control_resamples())
res <-
resample_workflow(
workflow = object,
resamples = resamples,
metrics = metrics,
eval_time = eval_time,
control = control,
rng = TRUE
)
.stash_last_result(res)
res
}
# ------------------------------------------------------------------------------
resample_workflow <- function(workflow, resamples, metrics, eval_time = NULL,
control, rng, call = caller_env()) {
check_no_tuning(workflow)
# `NULL` is the signal that we have no grid to tune with
grid <- NULL
pset <- NULL
out <- tune_grid_workflow(
workflow = workflow,
resamples = resamples,
grid = grid,
metrics = metrics,
eval_time = eval_time,
pset = pset,
control = control,
rng = rng,
call = call
)
attributes <- attributes(out)
new_resample_results(
x = out,
parameters = attributes$parameters,
metrics = attributes$metrics,
eval_time = attributes$eval_time,
eval_time_target = NULL,
outcomes = attributes$outcomes,
rset_info = attributes$rset_info,
workflow = attributes$workflow
)
}
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