`CppMethod` error when applying prepped UMAP recipe after saving/reading as `.rds`

[juliasilge]

Seems like there is a bug 🐛 for step_umap() when trying to save a prepped recipe as .rds and reading it back to apply it new data.

library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
library(tidyverse)
library(embed)

split <- seq.int(1, 150, by = 9)
tr <- iris[-split, ]
te <- iris[ split, ]

set.seed(11)
supervised <- 
   recipe(Species ~ ., data = tr) %>%
   step_center(all_predictors()) %>% 
   step_scale(all_predictors()) %>% 
   step_umap(all_predictors(), outcome = vars(Species), num_comp = 2) %>% 
   prep(training = tr)

write_rds(supervised, here::here(tempdir(), "umap.rds"))
saved_rec <- read_rds(here::here(tempdir(), "umap.rds"))
saved_rec %>% bake(new_data = te)
#> Error in .External(structure(list(name = "CppMethod__invoke_notvoid", : NULL value passed as symbol address

^{Created on 2021-08-02 by the reprex package (v2.0.0)}

I'm sure this is not us (i.e. not the embed package) but I wonder if there is anything we can do about this.

The recipe is fine if you don't save as .rds and then read it back.

[jlmelville]

I am very late to discovering this, but yes this is almost certainly because of the underlying UMAP package (uwot), which uses RcppAnnoy, which itself wraps the C++ library Annoy to find approximate nearest neighbors. The RcppAnnoy objects have save and load methods that must be called and just using saveRDS with them won't work (at least I couldn't get it to work). In turn uwot needs to provide special functions to save and load its state but it's all very unsatisfactory. Sorry about that. I was unable to think of a workaround.

I do intend to fix this but my current solution involves writing an entirely new approximate nearest neighbors package. As that and maintaining uwot exists entirely as a spare time endeavor, it's taking quite a long time (3 years and counting for the nearest neighbor package). I'll get there in the end. Probably.

[juliasilge]

Thanks for the message @jlmelville and for your work on uwot! 🙌 We also are thinking about serialization for trained model objects like xgboost, torch, etc, that have native methods for saving/loading. Definitely an area that needs some attention from all of us!

[juliasilge]

This has now been solved with the new bundle package:

library(tidymodels)
library(tidyverse)
library(embed)

split <- seq.int(1, 150, by = 9)
tr <- iris[-split, ]
te <- iris[ split, ]

set.seed(11)
supervised <- 
  recipe(Species ~ ., data = tr) %>%
  step_center(all_predictors()) %>% 
  step_scale(all_predictors()) %>% 
  step_umap(all_predictors(), outcome = vars(Species), num_comp = 2) %>% 
  prep(training = tr)

library(bundle)
temp_file <- fs::file_temp(pattern = "umap", ext = "rds")
bundle(supervised) %>% write_rds(temp_file)

saved_rec <- read_rds(temp_file)
unbundle(saved_rec) %>% bake(new_data = te)
#> # A tibble: 17 × 3
#>    Species     UMAP1  UMAP2
#>    <fct>       <dbl>  <dbl>
#>  1 setosa      13.3    2.93
#>  2 setosa      12.0    4.69
#>  3 setosa      14.5    3.12
#>  4 setosa      13.5    3.07
#>  5 setosa      13.4    2.99
#>  6 setosa      12.0    4.86
#>  7 versicolor -10.1    8.80
#>  8 versicolor  -9.79   8.28
#>  9 versicolor  -4.91 -11.6 
#> 10 versicolor  -9.66   6.12
#> 11 versicolor -10.1    6.61
#> 12 versicolor -10.3    6.98
#> 13 virginica   -4.14 -11.6 
#> 14 virginica   -2.69 -12.1 
#> 15 virginica   -4.06 -10.3 
#> 16 virginica   -1.73 -11.5 
#> 17 virginica   -2.33 -10.9

^{Created on 2022-09-16 with reprex v2.0.2}

We should document somewhere that this step needs to be bundled for use in a new session. How do you all want to do that?

[jlmelville]

Looks like I need to get in on this bundle thing...

[EmilHvitfeldt]

I think we should document it as a section. Like we do with Tidying and Case weights, this way it will be easier to link to the documentation when the question pops up

[topepo]

Agreed. We just did this for the parsnip engine docs.

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