Refit one or more trained workflows to new data — modeltime_wfs

It allows retraining a set of workflows trained on new data.

modeltime_wfs_multirefit(models_table)

Arguments

models_table	a tibble that comes from the output of the `modeltime_wfs_multifit()`, `modeltime_wfs_multiforecast()`, `modeltime_wfs_multibestmodel()` functions. For the `modeltime_wfs_multifit` function, the 'table_time' object must be selected from the output.

Value

a tibble, corresponds to the same tibble supplied in the 'models_table' parameter but with the refit of the workflows saved in the 'nested_model' column.

Examples

library(dplyr)
library(earth)

df <- sknifedatar::emae_series

datex <- '2020-02-01'
df_emae <- df %>% 
  dplyr::filter(date <= datex) %>% 
  tidyr::nest(nested_column=-sector) %>% 
  head(2)

receta_base <- recipes::recipe(value ~ ., data = df %>% select(-sector))

mars <- parsnip::mars(mode = 'regression') %>% parsnip::set_engine('earth')

wfsets <- workflowsets::workflow_set(
  preproc = list(
    R_date = receta_base),
  models  = list(M_mars = mars),
  cross   = TRUE)

wfsets_fit <- modeltime_wfs_multifit(.wfs = wfsets,
                                     .prop = 0.8, 
                                     serie = df_emae)
#> ✓ Workflow training finished OK.
#> ✓ Workflow training finished OK.
#> 
#> ── 1 models fitted ♥ ───────────────────────────────────────────────────────────
#> 
#> ── 0 models deleted x ──
#> 

sknifedatar::modeltime_wfs_multirefit(wfsets_fit$table_time)
#> # A tibble: 2 x 5
#>   sector    nested_column     R_date_M_mars nested_model       calibration      
#>   <chr>     <list>            <list>        <list>             <list>           
#> 1 Comercio  <tibble [194 × 2… <workflow>    <model_time [1 × … <model_time [1 ×…
#> 2 Ensenanza <tibble [194 × 2… <workflow>    <model_time [1 × … <model_time [1 ×…