#' Auto Linear Regression Workflowset Function
#'
#' @family Auto Workflowsets
#'
#' @author Steven P. Sanderson II, MPH
#'
#' @description
#' This function is used to quickly create a workflowsets object.
#'
#' @seealso \url{https://workflowsets.tidymodels.org/}(workflowsets)
#'
#' @details This function expects to take in the recipes that you want to use in
#' the modeling process. This is an automated workflow process. There are sensible
#' defaults set for the `glmnet` model specification, but if you choose you can
#' set them yourself if you have a good understanding of what they should be.
#'
#' @param .model_type This is where you will set your engine. It uses
#' [parsnip::linear_reg()] under the hood and can take one of the following:
#' * "lm"
#' * "glmnet"
#' * "all_engines" - This will make a model spec for all available engines.
#'
#' Not yet implemented are:
#' * "stan"
#' * "spark"
#' * "keras"
#' @param .recipe_list You must supply a list of recipes. list(rec_1, rec_2, ...)
#' @param .penalty The penalty parameter of the glmnet. The default is 1
#' @param .mixture The mixture parameter of the glmnet. The default is 0.5
#'
#' @examples
#' suppressPackageStartupMessages(library(modeltime))
#' suppressPackageStartupMessages(library(timetk))
#' suppressPackageStartupMessages(library(dplyr))
#' suppressPackageStartupMessages(library(rsample))
#'
#' data <- AirPassengers %>%
#' ts_to_tbl() %>%
#' select(-index)
#'
#' splits <- time_series_split(
#' data
#' , date_col
#' , assess = 12
#' , skip = 3
#' , cumulative = TRUE
#' )
#'
#' rec_objs <- ts_auto_recipe(
#' .data = training(splits)
#' , .date_col = date_col
#' , .pred_col = value
#' )
#'
#' wf_sets <- ts_wfs_lin_reg("all_engines", rec_objs)
#' wf_sets
#'
#' @return
#' Returns a workflowsets object.
#'
#' @name ts_wfs_lin_reg
NULL
#' @export
#' @rdname ts_wfs_lin_reg
ts_wfs_lin_reg <- function(.model_type, .recipe_list, .penalty = 1, .mixture = 0.5){
# * Tidyeval ---
model_type = .model_type
recipe_list = .recipe_list
# * Checks ----
if (!is.character(model_type)) {
stop(call. = FALSE, "(.model_type) must be a character like 'lm', 'glmnet'")
}
if (!model_type %in% c("lm","glmnet","all_engines")){
stop(call. = FALSE, "(.model_type) must be one of the following, 'lm','glmnet', or 'all_engines'")
}
if (!is.list(recipe_list)){
stop(call. = FALSE, "(.recipe_list) must be a list of recipe objects")
}
if (!is.numeric(.penalty) | !is.numeric(.mixture)){
stop(call. = FALSE, "Both the .penalty and .mixture parameters must be numeric.")
}
# * Models ----
model_spec_lm <- parsnip::linear_reg(
mode = "regression"
) %>%
parsnip::set_engine("lm")
model_spec_glmnet <- parsnip::linear_reg(
mode = "regression",
penalty = .penalty,
mixture = .mixture
) %>%
parsnip::set_engine("glmnet")
final_model_list <- if (model_type == "lm"){
fml <- list(model_spec_lm)
} else if (model_type == "glmnet"){
fml <- list(model_spec_glmnet)
} else {
fml <- list(
model_spec_lm,
model_spec_glmnet
)
}
# * Workflow Sets ----
wf_sets <- workflowsets::workflow_set(
preproc = recipe_list,
models = final_model_list,
cross = TRUE
)
# * Return ---
return(wf_sets)
}
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