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#' Modeltime workflow sets refit
#'
#' @description applies the `modeltime_refit()` function from 'modeltime' package to the object generated from
#' the `modeltime_wfs_fit()` function (or the filtered version after the `modeltime_wfs_bestmodel()` is applied).
#'
#' @details each workflow is now re-trained using all the available data.
#'
#' @param .wfs_results tibble of combination of recipes and models fitted, generated with the `modeltime_wfs_fit()` function.
#' @param .serie a time series dataframe.
#'
#' @return a tibble containing the re-trained models.
#' @export
#'
#' @examples
#' library(modeltime)
#' library(dplyr)
#' library(earth)
#'
#' data <- sknifedatar::data_avellaneda %>%
#' mutate(date=as.Date(date)) %>%
#' filter(date<'2012-06-01')
#'
#' recipe_date <- recipes::recipe(value ~ ., data = data) %>%
#' recipes::step_date(date, features = c('dow','doy','week','month','year'))
#'
#' mars <- parsnip::mars(mode = 'regression') %>%
#' parsnip::set_engine('earth')
#'
#' wfsets <- workflowsets::workflow_set(
#' preproc = list(
#' R_date = recipe_date),
#' models = list(M_mars = mars),
#' cross = TRUE)
#'
#' wffits <- sknifedatar::modeltime_wfs_fit(.wfsets = wfsets,
#' .split_prop = 0.8,
#' .serie = data)
#'
#' sknifedatar::modeltime_wfs_refit(.wfs_results = wffits,
#' .serie = data)
#'
modeltime_wfs_refit <- function(.wfs_results, .serie){
list_models <- .wfs_results %>% split(.$.model_id)
# Refit on whole dataset
table_refit <- purrr::map(list_models, function(.model = list_models, .df=.serie){
.models_table <- .model %>%
purrr::pluck('.fit_model',1) %>%
modeltime::modeltime_refit(data = .serie)
})
# Convert lists to table
table_wfs <- dplyr::bind_rows(table_refit, .id = ".model_id")
return(table_wfs)
}
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