Error: `x` must be a vector, not a `rsplit/vfold_split` object

[gcameron89777]

Error: x must be a vector, not a rsplit/vfold_split object

I am experiencing the above error when using tidyr::crossing() just after creativng a rsplit object using vfold_cv(). The error is intermittent, it happens sometimes. Others have been able to reproduce, sometimes.

Example csv file to reproduce.

library(tidyverse)
library(rsample)

example_data <- read_csv("example_data.csv")

example_split <- initial_split(example_data, 0.9)
training_data <- training(example_split)
testing_data <- testing(example_split)

# 5 fold split stratified on j
set.seed(123)
train_cv <- vfold_cv(training_data, 5, strata = j) %>% 

  # create training and validation sets within each fold
  mutate(train = map(splits, ~training(.x)), 
         validate = map(splits, ~testing(.x)))

blah <- train_cv %>% 
  crossing(mtry = c(1,2))
> Error: `x` must be a vector, not a `rsplit/vfold_split` object

train_cv looks like this:

train_cv
#  5-fold cross-validation using stratification 
# A tibble: 5 x 4
  splits            id    train                  validate              
* <named list>      <chr> <named list>           <named list>          
1 <split [72K/18K]> Fold1 <tibble [72,000 × 10]> <tibble [18,001 × 10]>
2 <split [72K/18K]> Fold2 <tibble [72,001 × 10]> <tibble [18,000 × 10]>
3 <split [72K/18K]> Fold3 <tibble [72,001 × 10]> <tibble [18,000 × 10]>
4 <split [72K/18K]> Fold4 <tibble [72,001 × 10]> <tibble [18,000 × 10]>
5 <split [72K/18K]> Fold5 <tibble [72,001 × 10]> <tibble [18,000 × 10]>

I would like to use the same train_cv object in my script for trying different models with their own tuning parameters. In the example above, if crossing(mtry = c(1, 2)) works, the desired output would take train_cv and make it look like this:

# A tibble: 10 x 5
   splits            id    train                  validate                mtry
   <named list>      <chr> <named list>           <named list>           <dbl>
 1 <split [72K/18K]> Fold1 <tibble [72,000 × 10]> <tibble [18,001 × 10]>     1
 2 <split [72K/18K]> Fold1 <tibble [72,000 × 10]> <tibble [18,001 × 10]>     2
 3 <split [72K/18K]> Fold2 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
 4 <split [72K/18K]> Fold2 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2
 5 <split [72K/18K]> Fold3 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
 6 <split [72K/18K]> Fold3 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2
 7 <split [72K/18K]> Fold4 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
 8 <split [72K/18K]> Fold4 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2
 9 <split [72K/18K]> Fold5 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
10 <split [72K/18K]> Fold5 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2

Session Info:

sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Amazon Linux 2

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] Metrics_0.1.4     rsample_0.0.5     rlang_0.4.2       odbc_1.2.2        DBI_1.1.0         dbplyr_1.4.2      rmarkdown_2.0     kableExtra_1.1.0 
 [9] scales_1.1.0      doParallel_1.0.15 iterators_1.0.12  foreach_1.4.7     lubridate_1.7.4   forcats_0.4.0     stringr_1.4.0     dplyr_0.8.3      
[17] purrr_0.3.3       readr_1.3.1       tidyr_1.0.0       tibble_2.1.3      ggplot2_3.2.1     tidyverse_1.3.0   tufte_0.5        

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3        lattice_0.20-38   listenv_0.8.0     utf8_1.1.4        assertthat_0.2.1  zeallot_0.1.0     digest_0.6.23     packrat_0.5.0    
 [9] R6_2.4.1          cellranger_1.1.0  backports_1.1.5   reprex_0.3.0      evaluate_0.14     httr_1.4.1        pillar_1.4.3      lazyeval_0.2.2   
[17] readxl_1.3.1      data.table_1.12.8 rstudioapi_0.10   furrr_0.1.0       blob_1.2.0        webshot_0.5.2     bit_1.1-15.1      munsell_0.5.0    
[25] broom_0.5.3       compiler_3.6.0    modelr_0.1.5      xfun_0.12         pkgconfig_2.0.3   globals_0.12.5    htmltools_0.4.0   tidyselect_0.2.5 
[33] codetools_0.2-16  future_1.16.0     fansi_0.4.1       viridisLite_0.3.0 crayon_1.3.4      withr_2.1.2       grid_3.6.0        nlme_3.1-143     
[41] jsonlite_1.6      gtable_0.3.0      lifecycle_0.1.0   magrittr_1.5      cli_2.0.1         stringi_1.4.5     fs_1.3.1          xml2_1.2.2       
[49] generics_0.0.2    vctrs_0.2.1       tools_3.6.0       bit64_0.9-7       glue_1.3.1        hms_0.5.3         colorspace_1.4-1  rvest_0.3.5      
[57] knitr_1.27        haven_2.2.0      

Not sure if this is an actual issue or a problem with my code. I tried the rstudio community forum first.

[gacolitti]

Here is perhaps a simpler reprex:

library(rsample)
#> Loading required package: tidyr
library(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

mtcars %>% 
  vfold_cv(10) %>% 
  crossing(x = c(1, 2, 3))
#> `x` must be a vector, not a `rsplit/vfold_split` object

mtcars %>% 
  vfold_cv(10) %>% 
  nest(data = c(id)) %>% 
  unnest(cols = c(data)) %>% 
  crossing(x = c(1, 2, 3))
#> `x` must be a vector, not a `rsplit/vfold_split` object

mtcars %>% 
  vfold_cv(10) %>% 
  group_by(id) %>% 
  nest() %>% 
  unnest(cols = c(data)) %>% 
  crossing(x = c(1, 2, 3))
#> # A tibble: 30 x 3
#>    id     splits             x
#>    <chr>  <list>         <dbl>
#>  1 Fold01 <split [28/4]>     1
#>  2 Fold01 <split [28/4]>     2
#>  3 Fold01 <split [28/4]>     3
#>  4 Fold02 <split [28/4]>     1
#>  5 Fold02 <split [28/4]>     2
#>  6 Fold02 <split [28/4]>     3
#>  7 Fold03 <split [29/3]>     1
#>  8 Fold03 <split [29/3]>     2
#>  9 Fold03 <split [29/3]>     3
#> 10 Fold04 <split [29/3]>     1
#> # ... with 20 more rows

^{Created on 2020-02-13 by the reprex package (v0.3.0)}

For some reason grouping, nesting, and unnesting works.

[topepo]

I'm guessing that it was an issue with the version of tidyr that you were using. It works for me (see below).

Two things though:

• From the output below, note that you are blowing up the memory requirements by saving the train and validate columns. The memory requirements for the splits column are not affected since they store exactly the same data but your new columns do not.

• If you are going to tune, think about using the tune package (which was new when you posted this).

library(tidyverse)
library(rsample)

example_data <- read_csv("~/Downloads/example_data.csv")
#> Parsed with column specification:
#> cols(
#>   a = col_double(),
#>   b = col_double(),
#>   c = col_double(),
#>   d = col_double(),
#>   e = col_double(),
#>   f = col_double(),
#>   g = col_double(),
#>   h = col_double(),
#>   i = col_double(),
#>   j = col_logical()
#> )

example_split <- initial_split(example_data, 0.9)
training_data <- training(example_split)
testing_data <- testing(example_split)

# 5 fold split stratified on j
set.seed(123)
train_cv <- vfold_cv(training_data, 5, strata = j) 

# Before unpacking: 
lobstr::obj_size(train_cv)
#> 8,289,640 B

train_cv <- 
  train_cv %>% 
  # create training and validation sets within each fold
  mutate(train = map(splits, ~training(.x)), 
         validate = map(splits, ~testing(.x)))

# After unpacking: 
lobstr::obj_size(train_cv)
#> 42,499,296 B

blah <- train_cv %>% 
  crossing(mtry = c(1,2))
blah
#> # A tibble: 10 x 5
#>    splits            id    train                  validate                mtry
#>    <named list>      <chr> <named list>           <named list>           <dbl>
#>  1 <split [72K/18K]> Fold1 <tibble [72,000 × 10]> <tibble [18,001 × 10]>     1
#>  2 <split [72K/18K]> Fold1 <tibble [72,000 × 10]> <tibble [18,001 × 10]>     2
#>  3 <split [72K/18K]> Fold2 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
#>  4 <split [72K/18K]> Fold2 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2
#>  5 <split [72K/18K]> Fold3 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
#>  6 <split [72K/18K]> Fold3 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2
#>  7 <split [72K/18K]> Fold4 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
#>  8 <split [72K/18K]> Fold4 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2
#>  9 <split [72K/18K]> Fold5 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     1
#> 10 <split [72K/18K]> Fold5 <tibble [72,001 × 10]> <tibble [18,000 × 10]>     2

lobstr::obj_size(blah)
#> 42,498,352 B

^{Created on 2020-03-29 by the reprex package (v0.3.0)}

[gcameron89777]

Thank you Max!

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