Tuning results give "All models failed" message

[JauntyJJS]

Hi,

I tried to run the code that does the tuning in https://emilhvitfeldt.github.io/tidyclust/articles/tuning_and_metrics.html

I have installed the development of parsnip and workflows.

remotes::install_github("tidymodels/parsnip")
#> Skipping install of 'parsnip' from a github remote, the SHA1 (de886645) has not changed since last install.
#>   Use `force = TRUE` to force installation
remotes::install_github("tidymodels/workflows@celery")
#> Skipping install of 'workflows' from a github remote, the SHA1 (099a735e) has not changed since last install.
#>   Use `force = TRUE` to force installation

^{Created on 2022-08-17 by the reprex package (v2.0.1)}

Unfortunately, the tuning results give this message saying all the models have failed.

library(parsnip)
library(workflows)
library(rsample)
library(recipes)
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> 
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stats':
#> 
#>     step
library(tune)
library(dials)
#> Loading required package: scales
library(tidyr)
library(modeldata)
library(tidyclust)
#> 
#> Attaching package: 'tidyclust'
#> The following object is masked from 'package:parsnip':
#> 
#>     prepare_data

data("penguins", package = "modeldata")

penguins <- penguins %>%
  drop_na()

penguins_cv <- vfold_cv(penguins, v = 5)

kmeans_spec <- k_means(num_clusters = tune()) 

penguins_rec <- recipe(~ bill_length_mm + bill_depth_mm, 
                       data = penguins)

kmeans_wflow <- workflow(penguins_rec, kmeans_spec)

clust_num_grid <- grid_regular(num_clusters(), 
                               levels = 10)

res <- tune_cluster(
  kmeans_wflow,
  resamples = penguins_cv,
  grid = clust_num_grid,
  control = control_grid(save_pred = TRUE, extract = identity),
  metrics = cluster_metric_set(tot_wss, tot_sse, sse_ratio)
) 
#> Warning: All models failed. See the `.notes` column.

res
#> # Tuning results
#> # 5-fold cross-validation 
#> # A tibble: 5 × 6
#>   splits           id    .metrics .notes .extracts .predictions
#>   <list>           <chr> <list>   <list> <list>    <list>      
#> 1 <split [266/67]> Fold1 <NULL>   <NULL> <NULL>    <NULL>      
#> 2 <split [266/67]> Fold2 <NULL>   <NULL> <NULL>    <NULL>      
#> 3 <split [266/67]> Fold3 <NULL>   <NULL> <NULL>    <NULL>      
#> 4 <split [267/66]> Fold4 <NULL>   <NULL> <NULL>    <NULL>      
#> 5 <split [267/66]> Fold5 <NULL>   <NULL> <NULL>    <NULL>

sessionInfo()
#> R version 4.2.1 (2022-06-23 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19044)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=English_Singapore.utf8  LC_CTYPE=English_Singapore.utf8   
#> [3] LC_MONETARY=English_Singapore.utf8 LC_NUMERIC=C                      
#> [5] LC_TIME=English_Singapore.utf8    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] tidyclust_0.0.0.9000 modeldata_1.0.0      tidyr_1.2.0         
#>  [4] dials_1.0.0          scales_1.2.0         tune_1.0.0          
#>  [7] recipes_1.0.1        dplyr_1.0.9          rsample_1.1.0       
#> [10] workflows_1.0.0.9000 parsnip_1.0.0.9000  
#> 
#> loaded via a namespace (and not attached):
#>  [1] splines_4.2.1      foreach_1.5.2      R.utils_2.12.0     prodlim_2019.11.13
#>  [5] highr_0.9          GPfit_1.0-8        yaml_2.3.5         globals_0.16.0    
#>  [9] ipred_0.9-13       pillar_1.8.0       lattice_0.20-45    glue_1.6.2        
#> [13] digest_0.6.29      hardhat_1.2.0      colorspace_2.0-3   htmltools_0.5.2   
#> [17] Matrix_1.4-1       R.oo_1.25.0        timeDate_4021.104  pkgconfig_2.0.3   
#> [21] lhs_1.1.5          DiceDesign_1.9     listenv_0.8.0      purrr_0.3.4       
#> [25] gower_1.0.0        lava_1.6.10        tibble_3.1.8       styler_1.7.0      
#> [29] generics_0.1.3     ggplot2_3.3.6      ellipsis_0.3.2     withr_2.5.0       
#> [33] furrr_0.3.1        nnet_7.3-17        cli_3.3.0          crayon_1.5.1      
#> [37] survival_3.3-1     magrittr_2.0.3     evaluate_0.15      R.methodsS3_1.8.2 
#> [41] fs_1.5.2           future_1.27.0      fansi_1.0.3        parallelly_1.32.1 
#> [45] R.cache_0.15.0     MASS_7.3-57        class_7.3-20       tools_4.2.1       
#> [49] lifecycle_1.0.1    stringr_1.4.0      munsell_0.5.0      reprex_2.0.1      
#> [53] compiler_4.2.1     rlang_1.0.4        grid_4.2.1         iterators_1.0.14  
#> [57] yardstick_1.0.0    rstudioapi_0.13    rmarkdown_2.14     gtable_0.3.0      
#> [61] codetools_0.2-18   R6_2.5.1           lubridate_1.8.0    knitr_1.39        
#> [65] fastmap_1.1.0      future.apply_1.9.0 utf8_1.2.2         stringi_1.7.8     
#> [69] parallel_4.2.1     Rcpp_1.0.9         vctrs_0.4.1        rpart_4.1.16      
#> [73] tidyselect_1.1.2   xfun_0.31

^{Created on 2022-08-17 by the reprex package (v2.0.1)}

Is the tuning procedures still under development ?

[JauntyJJS]

Somehow I managed to get it to work again.

[github-actions[bot]]

This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue.