My parsnip models doesn't work in modeltime_calibrate function after updating packages

Question

I developed some models for a time series, which was running normally yesterday, but after updating several packages today, including modeltime and timetk packages, the code doesn't work. It seems that the error is in the ranger and xgboost models when performing the calibration step. How can I solve that?

Before updating packages,with this session the all code ran fine:

library(forecast)
library(tidyverse)
library(lubridate)
library(quantdates)
library(tidymodels)
library(timetk)
library(modeltime)
library(modeltime.ensemble)

> sessionInfo()
R version 4.1.3 (2022-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] kernlab_0.9-30           prophet_1.0              rlang_1.0.2              Rcpp_1.0.8.3             xgboost_1.6.0.1          ranger_0.13.1           
 [7] modeltime.ensemble_1.0.0 modeltime.resample_0.2.0 modeltime_1.2.0          timetk_2.8.0             yardstick_0.0.9          workflowsets_0.2.1      
[13] workflows_0.2.6          tune_0.2.0               rsample_0.1.1            recipes_0.2.0            parsnip_0.2.1            modeldata_0.1.1         
[19] infer_1.0.0              dials_0.1.1              scales_1.2.0             broom_0.8.0              tidymodels_0.2.0         quantdates_1.0          
[25] lubridate_1.8.0          forcats_0.5.1            stringr_1.4.0            dplyr_1.0.9              purrr_0.3.4              readr_2.1.2             
[31] tidyr_1.2.0              tibble_3.1.7             ggplot2_3.3.6            tidyverse_1.3.1          forecast_8.16           

loaded via a namespace (and not attached):
  [1] readxl_1.4.0         backports_1.4.1      plyr_1.8.7           splines_4.1.3        listenv_0.8.0        inline_0.3.19        digest_0.6.29       
  [8] foreach_1.5.2        fansi_1.0.3          magrittr_2.0.3       tzdb_0.3.0           Metrics_0.1.4        globals_0.15.0       modelr_0.1.8        
 [15] gower_1.0.0          RcppParallel_5.1.5   matrixStats_0.62.0   xts_0.12.1           hardhat_0.2.0        tseries_0.10-51      prettyunits_1.1.1   
 [22] colorspace_2.0-3     rvest_1.0.2          warp_0.2.0           haven_2.5.0          callr_3.7.0          crayon_1.5.1         jsonlite_1.8.0      
 [29] progressr_0.10.0     survival_3.3-1       zoo_1.8-10           iterators_1.0.14     glue_1.6.2           gtable_0.3.0         ipred_0.9-12        
 [36] pkgbuild_1.3.1       rstan_2.21.5         future.apply_1.9.0   quantmod_0.4.20      DBI_1.1.2            GPfit_1.0-8          stats4_4.1.3        
 [43] lava_1.6.10          StanHeaders_2.21.0-7 prodlim_2019.11.13   httr_1.4.3           ellipsis_0.3.2       pkgconfig_2.0.3      loo_2.5.1           
 [50] nnet_7.3-17          dbplyr_2.1.1         utf8_1.2.2           janitor_2.1.0        tidyselect_1.1.2     DiceDesign_1.9       munsell_0.5.0       
 [57] cellranger_1.1.0     tools_4.1.3          cli_3.2.0            generics_0.1.2       processx_3.5.3       fs_1.5.2             future_1.25.0       
 [64] nlme_3.1-155         tictoc_1.0.1         xml2_1.3.3           compiler_4.1.3       rstudioapi_0.13      curl_4.3.2           slider_0.2.2        
 [71] reprex_2.0.1         lhs_1.1.5            stringi_1.7.6        ps_1.7.0             lattice_0.20-45      Matrix_1.4-1         urca_1.3-0          
 [78] vctrs_0.4.1          pillar_1.7.0         lifecycle_1.0.1      furrr_0.3.0          lmtest_0.9-40        data.table_1.14.2    R6_2.5.1            
 [85] gridExtra_2.3        parallelly_1.31.1    codetools_0.2-18     MASS_7.3-55          assertthat_0.2.1     withr_2.5.0          fracdiff_1.5-1      
 [92] parallel_4.1.3       hms_1.1.1            quadprog_1.5-8       grid_4.1.3           rpart_4.1.16         timeDate_3043.102    class_7.3-20        
 [99] snakecase_0.11.0     TTR_0.24.3           pROC_1.18.0  

And after updating packages the error I get now is the following:

wflw_mod_rf <- workflow() %>%
    add_model(
      spec = rand_forest(
        mode = "regression"
      ) %>%
        set_engine("ranger")
    ) %>%
    add_recipe(recipe_spec %>%
                 update_role(dtemonth, new_role = "indicator")) %>%
    fit(training(splits))
 
  wflw_mod_xgboost <- workflow() %>%
    add_model(
      spec = boost_tree(
        mode = "regression"
      ) %>%
        set_engine("xgboost")
    ) %>%
    add_recipe(recipe_spec %>%
                 update_role(dtemonth, new_role = "indicator")) %>%
    fit(training(splits))
 
  wflw_mod_prophet <- workflow() %>%
    add_model(
      spec = prophet_reg(
        seasonality_daily  = FALSE,
        seasonality_weekly = FALSE,
        seasonality_yearly = TRUE
      ) %>%
        set_engine("prophet")
    ) %>%
    add_recipe(recipe_spec) %>%
    fit(training(splits))

submodels_all_tbl <- modeltime_table(
    wflw_mod_rf,
    wflw_mod_xgboost,
    wflw_mod_prophet
  )
  
> submodels_all_tbl %>% modeltime_calibrate(testing(splits), quiet = F)
Error: Problem occurred getting predictors from new data. Error in tk_get_timeseries_signature.default(.): No method for class NULL.

Error: Problem occurred getting predictors from new data. Error in tk_get_timeseries_signature.default(.): No method for class NULL.



── Model Calibration Failure Report ────────────────────────
# A tibble: 2 × 6
  .model_id .model     .model_desc .type .calibration_data fail_check
      <int> <list>     <chr>       <chr> <list>            <lgl>    
1         1 <workflow> RANGER      NA    <lgl [1]>         TRUE      
2         2 <workflow> XGBOOST     NA    <lgl [1]>         TRUE      
The following models had errors:
- Model 1: Failed Calibration.
- Model 2: Failed Calibration.

Potential Solution: Check the Error/Warning Messages for clues as to why your model(s) failed calibration.
── End Model Calibration Failure Report ────────────────────

Answer 1

In case it happens to someone else, the solution is in this link. There was an update from hardhat 1.0.0 that changed the functionality in update_role, so step_rm had to be used in the recipe.

wflw_mod_rf <- workflow() %>%
    add_model(
      spec = rand_forest(
        mode = "regression"
      ) %>%
        set_engine("ranger")
    ) %>%
    add_recipe(recipe_spec %>%
                 step_rm(dtemonth)) %>%
    fit(training(splits))