make_splits() does not work on empty assessment indices

[tjmahr]

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• Start a new R session
• Install the latest version of of the package: update.packages(oldPkgs="rsample", ask=FALSE)
• Write a minimal reproducible example
• run sessionInfo() and add the results to the issue. Even better would be to use the sessioninfo package's session_info().

Minimal, reproducible example:

I am trying to make my own rset function for bootstrap sampling groups in a grouped dataframes. But I run into an issue when the assessment rows empty. When making the reprex, I also discovered that I had to use 5L instead of 5 for the index.

rsample::make_splits(
  list(
    analysis = 1:5,
    assessment = NA
  ),
  data.frame(x = 1:5)
)
#> Error in UseMethod("complement"): no applicable method for 'complement' applied to an object of class "rsplit"

rsample::make_splits(
  list(
    analysis = 1:5,
    assessment = integer(0)
  ),
  data.frame(x = 1:5)
)
#> Error in UseMethod("complement"): no applicable method for 'complement' applied to an object of class "rsplit"

rsample::make_splits(
  list(
    analysis = 1:4,
    assessment = 5
  ),
  data.frame(x = 1:5)
)
#> Error: `out_id` must be a positive integer vector.

rsample::make_splits(
  list(
    analysis = 1:4,
    assessment = 5L
  ),
  data.frame(x = 1:5)
)
#> <Analysis/Assess/Total>
#> <4/1/5>

^{Created on 2020-09-25 by the reprex package (v0.3.0)}

Session info

devtools::session_info()
#> - Session info ---------------------------------------------------------------
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#>  system   x86_64, mingw32             
#>  ui       RTerm                       
#>  language (EN)                        
#>  collate  English_United States.1252  
#>  ctype    English_United States.1252  
#>  tz       America/Chicago             
#>  date     2020-09-25                  
#> 
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[juliasilge]

For some context setting here, what do you plan or expect to do when the assessment set is empty?

[tjmahr]

To match what bootstraps() does. See <split [N/0]> when the assessment set is empty.

set.seed(1)
df <- data.frame(x = c(1, 2))

splits <- rsample::bootstraps(df, 5)
splits
#> # Bootstrap sampling 
#> # A tibble: 5 x 2
#>   splits        id        
#>   <list>        <chr>     
#> 1 <split [2/0]> Bootstrap1
#> 2 <split [2/1]> Bootstrap2
#> 3 <split [2/0]> Bootstrap3
#> 4 <split [2/1]> Bootstrap4
#> 5 <split [2/1]> Bootstrap5

rsample::assessment(splits[["splits"]][[1]])
#> [1] x
#> <0 rows> (or 0-length row.names)

I get that bootstrapping is weird, compared to N fold or leave-one-out sampling, because it does not guarantee a holdout sample for assessment.

^{Created on 2020-09-25 by the reprex package (v0.3.0)}

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