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Pipeable steps for feature engineering and data preprocessing to prepare for modeling
https://recipes.tidymodels.org
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[Bug] I think changes to purrr broke a tutorial on the recipe package. #1081

Closed lbui30 closed 1 year ago

lbui30 commented 1 year ago

Tips for a helpful bug report:

The reprex is the code that can be found on this webpage: https://www.tidymodels.org/learn/models/pls/

The problem

I'm having trouble with running the demo code on that page. It looks like the purr::map function is erroring out with the message:

folds <- 
  folds %>%
  mutate(var = map(recipes, get_var_explained),
         var = unname(var))
Error in `mutate()`:  In argument: `var = map(recipes, get_var_explained)`.
Caused by error in `map()`:  In index: 1.
Caused by error in `mutate()`:  In argument: `predictors = cumsum(xve) %>% as.vector()`.
Caused by error:
! `predictors` must be size 1, not 16.
Run `rlang::last_error()` to see where the error occurred.

Reproducible example

Use: the latest version of purr Follow the instructions here: https://www.tidymodels.org/learn/models/pls/

Also this error super confusing... Why would an environment like a ggplot or prep script be a list()? Why would we assume that its not actually 16 long. Is there a fundamental assumption in purrr that lists cannot be nested?

If both the first index and second index of any structure (including reciepes) in purr are equal then what is the point of evening using a maps?

Isn't the point of nested structures in tidymodels, so you can apply tidy principles to models and not just datasets?

EmilHvitfeldt commented 1 year ago

Hello @lbui30 👋

I'm not able to reproduce the error on my side, can you run the following code as a reprex and report back what you get? That would be awesome

library(pls)
#> 
#> Attaching package: 'pls'
#> The following object is masked from 'package:stats':
#> 
#>     loadings
library(tidymodels)
library(sessioninfo)
data(meats)

norm_rec <- 
  recipe(water + fat + protein ~ ., data = meats) %>%
  step_normalize(everything()) 

set.seed(57343)
folds <- vfold_cv(meats, repeats = 10)

folds <- 
  folds %>%
  mutate(recipes = map(splits, prepper, recipe = norm_rec))

get_var_explained <- function(recipe, ...) {

  # Extract the predictors and outcomes into their own matrices
  y_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_outcomes())
  x_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_predictors())

  # The pls package prefers the data in a data frame where the outcome
  # and predictors are in _matrices_. To make sure this is formatted
  # properly, use the `I()` function to inhibit `data.frame()` from making
  # all the individual columns. `pls_format` should have two columns.
  pls_format <- data.frame(
    endpoints = I(y_mat),
    measurements = I(x_mat)
  )
  # Fit the model
  mod <- plsr(endpoints ~ measurements, data = pls_format)

  # Get the proportion of the predictor variance that is explained
  # by the model for different number of components. 
  xve <- explvar(mod)/100 

  # To do the same for the outcome, it is more complex. This code 
  # was extracted from pls:::summary.mvr. 
  explained <- 
    drop(pls::R2(mod, estimate = "train", intercept = FALSE)$val) %>% 
    # transpose so that components are in rows
    t() %>% 
    as_tibble() %>%
    # Add the predictor proportions
    mutate(predictors = cumsum(xve) %>% as.vector(),
           components = seq_along(xve)) %>%
    # Put into a tidy format that is tall
    pivot_longer(
      cols = c(-components),
      names_to = "source",
      values_to = "proportion"
    )
}

folds <- 
  folds %>%
  mutate(var = map(recipes, get_var_explained),
         var = unname(var))

folds
#> #  10-fold cross-validation repeated 10 times 
#> # A tibble: 100 × 5
#>    splits           id       id2    recipes  var               
#>    <list>           <chr>    <chr>  <list>   <list>            
#>  1 <split [193/22]> Repeat01 Fold01 <recipe> <tibble [400 × 3]>
#>  2 <split [193/22]> Repeat01 Fold02 <recipe> <tibble [400 × 3]>
#>  3 <split [193/22]> Repeat01 Fold03 <recipe> <tibble [400 × 3]>
#>  4 <split [193/22]> Repeat01 Fold04 <recipe> <tibble [400 × 3]>
#>  5 <split [193/22]> Repeat01 Fold05 <recipe> <tibble [400 × 3]>
#>  6 <split [194/21]> Repeat01 Fold06 <recipe> <tibble [400 × 3]>
#>  7 <split [194/21]> Repeat01 Fold07 <recipe> <tibble [400 × 3]>
#>  8 <split [194/21]> Repeat01 Fold08 <recipe> <tibble [400 × 3]>
#>  9 <split [194/21]> Repeat01 Fold09 <recipe> <tibble [400 × 3]>
#> 10 <split [194/21]> Repeat01 Fold10 <recipe> <tibble [400 × 3]>
#> # … with 90 more rows

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.1 (2022-06-23)
#>  os       macOS Monterey 12.6
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#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
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Created on 2023-02-05 with reprex v2.0.2

lbui30 commented 1 year ago

So the code had in your reprex ran at first until... I made it match what I had.

It's been very difficult to run reproducible examples or roll back because if I try to debug the my code our your code (using recover/browser), my R randomly occasionally crashes (hence why this was listed as a bug). When I have tried to roll back the original purr 0.3.5 this tutorial used then tidy-verse won't work install problem because it talk about missing purr version 1.0.1. So, I am only able to create a reprex on the latest version of R and not run it back in R 4.2.0 and purr version 0.3.5.
After re-installing R and R-studio 2 times, this is what I have so far.....

Results of your reprex

library(pls)
#> Attaching package: 'pls'
#> The following object is masked from 'package:stats':
#> 
#>     loadings
library(tidymodels)
library(sessioninfo)
data(meats)

norm_rec <- 
  recipe(water + fat + protein ~ ., data = meats) %>%
  step_normalize(everything()) 

set.seed(57343)
folds <- vfold_cv(meats, repeats = 10)

folds <- 
  folds %>%
  mutate(recipes = map(splits, prepper, recipe = norm_rec))

get_var_explained <- function(recipe, ...) {

  # Extract the predictors and outcomes into their own matrices
  y_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_outcomes())
  x_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_predictors())

  # The pls package prefers the data in a data frame where the outcome
  # and predictors are in _matrices_. To make sure this is formatted
  # properly, use the `I()` function to inhibit `data.frame()` from making
  # all the individual columns. `pls_format` should have two columns.
  pls_format <- data.frame(
    endpoints = I(y_mat),
    measurements = I(x_mat)
  )
  # Fit the model
  mod <- plsr(endpoints ~ measurements, data = pls_format)

  # Get the proportion of the predictor variance that is explained
  # by the model for different number of components. 
  xve <- explvar(mod)/100 

  # To do the same for the outcome, it is more complex. This code 
  # was extracted from pls:::summary.mvr. 
  explained <- 
    drop(pls::R2(mod, estimate = "train", intercept = FALSE)$val) %>% 
    # transpose so that components are in rows
    t() %>% 
    as_tibble() %>%
    # Add the predictor proportions
    mutate(predictors = cumsum(xve) %>% as.vector(),
           components = seq_along(xve)) %>%
    # Put into a tidy format that is tall
    pivot_longer(
      cols = c(-components),
      names_to = "source",
      values_to = "proportion"
    )
}

folds <- 
  folds %>%
  mutate(var = map(recipes, get_var_explained),
         var = unname(var))

folds
#> #  10-fold cross-validation repeated 10 times 
#> # A tibble: 100 × 5
#>    splits           id       id2    recipes  var               
#>    <list>           <chr>    <chr>  <list>   <list>            
#>  1 <split [193/22]> Repeat01 Fold01 <recipe> <tibble [400 × 3]>
#>  2 <split [193/22]> Repeat01 Fold02 <recipe> <tibble [400 × 3]>
#>  3 <split [193/22]> Repeat01 Fold03 <recipe> <tibble [400 × 3]>
#>  4 <split [193/22]> Repeat01 Fold04 <recipe> <tibble [400 × 3]>
#>  5 <split [193/22]> Repeat01 Fold05 <recipe> <tibble [400 × 3]>
#>  6 <split [194/21]> Repeat01 Fold06 <recipe> <tibble [400 × 3]>
#>  7 <split [194/21]> Repeat01 Fold07 <recipe> <tibble [400 × 3]>
#>  8 <split [194/21]> Repeat01 Fold08 <recipe> <tibble [400 × 3]>
#>  9 <split [194/21]> Repeat01 Fold09 <recipe> <tibble [400 × 3]>
#> 10 <split [194/21]> Repeat01 Fold10 <recipe> <tibble [400 × 3]>
#> # … with 90 more rows

sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.2 (2022-10-31 ucrt)
#>  os       Windows 11 x64 (build 22000)
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#>  language (EN)
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#>  ctype    English_United States.utf8
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#> 
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#>  survival       3.4-0      2022-08-09 [2] CRAN (R 4.2.2)
#>  tibble       * 3.1.8      2022-07-22 [1] CRAN (R 4.2.2)
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#>  timechange     0.2.0      2023-01-11 [1] CRAN (R 4.2.2)
#>  tune         * 1.0.1      2022-10-09 [1] CRAN (R 4.2.2)
#>  vctrs          0.5.2      2023-01-23 [1] CRAN (R 4.2.2)
#>  workflows    * 1.1.2      2022-11-16 [1] CRAN (R 4.2.2)
#>  workflowsets * 1.0.0      2022-07-12 [1] CRAN (R 4.2.2)
#> 
#>  [1] C:/Users/lechi/AppData/Local/R/win-library/4.2
#>  [2] C:/Program Files/R/R-4.2.2/library
#> 
#>
lbui30 commented 1 year ago

I was able to reproduce the errors after changing the conditions to recipe(protein ~ ., data = meats) as you can see below. I was able to isolate the issue to something to do something before predictors = cumsum(xve) %>% as.vector().

The challenge is I cannot seem to figure out more information because the log output from purr is extremely hard to read which line of code errored out. Which is why I brought up this issue in the purr package. If recover/browser were reliable when using purr (less crashes), then I could figure out what is going on hence why I suspect its a bug. 

library(modeldata)
library(tidyverse)
library(pls)
#> 
#> Attaching package: 'pls'
#> The following object is masked from 'package:stats':
#> 
#>     loadings
library(tidymodels)
library(sessioninfo)
data(meats)
#  ====>  This is the line that is different between your reprex and mine <====
norm_rec <- 
  recipe(protein ~ ., data = meats) %>%
  step_normalize(everything()) 
set.seed(57343)
folds <- vfold_cv(meats, repeats = 10)
folds <- folds %>%
  mutate(recipes = map(splits, prepper, recipe = norm_rec))
get_var_explained <- function(recipe, ...) {
  # Extract the predictors and outcomes into their own matrices
  y_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_outcomes())
  x_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_predictors())

  # The pls package prefers the data in a data frame where the outcome
  # and predictors are in _matrices_. To make sure this is formatted
  # properly, use the `I()` function to inhibit `data.frame()` from making
  # all the individual columns. `pls_format` should have two columns.
  pls_format <- data.frame(
    endpoints = I(y_mat),
    measurements = I(x_mat)
  )
  # Fit the model
  mod <- plsr(endpoints ~ measurements, data = pls_format)

  # Get the proportion of the predictor variance that is explained
  # by the model for different number of components. 
  xve <- explvar(mod)/100 

  # To do the same for the outcome, it is more complex. This code 
  # was extracted from pls:::summary.mvr. 
  explained <- 
    drop(pls::R2(mod, estimate = "train", intercept = FALSE)$val) %>% 
    # transpose so that components are in rows
    t() %>% 
    as_tibble() %>%
    # Add the predictor proportions
    mutate(predictors = cumsum(xve) %>% as.vector(),
           components = seq_along(xve)) %>%
    # Put into a tidy format that is tall
    pivot_longer(
      cols = c(-components),
      names_to = "source",
      values_to = "proportion"
    )
}

#This line done so it comes before the erro
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.2 (2022-10-31 ucrt)
#>  os       Windows 11 x64 (build 22000)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  English_United States.utf8
#>  ctype    English_United States.utf8
#>  tz       America/New_York
#>  date     2023-02-11
#>  pandoc   2.19.2 @ C:/Program Files/RStudio/bin/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package       * version    date (UTC) lib source
#>  assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.2.2)
#>  backports       1.4.1      2021-12-13 [1] CRAN (R 4.2.0)
#>  broom         * 1.0.3      2023-01-25 [1] CRAN (R 4.2.2)
#>  cellranger      1.1.0      2016-07-27 [1] CRAN (R 4.2.2)
#>  class           7.3-20     2022-01-16 [2] CRAN (R 4.2.2)
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#> 
#>  [1] C:/Users/lechi/AppData/Local/R/win-library/4.2
#>  [2] C:/Program Files/R/R-4.2.2/library

It errors out here: 

folds <- 
  folds %>%
  mutate(var = map(recipes, get_var_explained),
         var = unname(var))
#> Error in `mutate()`:
#> ℹ In argument: `var = map(recipes, get_var_explained)`.
#> Caused by error in `map()`:
#> ℹ In index: 1.
#> Caused by error in `mutate()`:
#> ℹ In argument: `predictors = cumsum(xve) %>% as.vector()`.
#> Caused by error:
#> ! `predictors` must be size 1, not 102.

#> Backtrace:
#>      ▆
#>   1. ├─folds %>% ...
#>   2. ├─dplyr::mutate(., var = map(recipes, get_var_explained), var = unname(var))
#>   3. ├─dplyr:::mutate.data.frame(...)
#>   4. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#>   5. │   ├─base::withCallingHandlers(...)
#>   6. │   └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#>   7. │     └─mask$eval_all_mutate(quo)
#>   8. │       └─dplyr (local) eval()
#>   9. ├─purrr::map(recipes, get_var_explained)
#>  10. │ └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
#>  11. │   ├─purrr:::with_indexed_errors(...)
#>  12. │   │ └─base::withCallingHandlers(...)
#>  13. │   ├─purrr:::call_with_cleanup(...)
#>  14. │   └─global .f(.x[[i]], ...)
#>  15. │     └─... %>% ...
#>  16. ├─tidyr::pivot_longer(...)
#>  17. ├─dplyr::mutate(., predictors = cumsum(xve) %>% as.vector(), components = seq_along(xve))
#>  18. ├─dplyr:::mutate.data.frame(., predictors = cumsum(xve) %>% as.vector(), components = seq_along(xve))
#>  19. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#>  20. │   ├─base::withCallingHandlers(...)
#>  21. │   └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#>  22. │     └─mask$eval_all_mutate(quo)
#>  23. │       └─dplyr (local) eval()
#>  24. ├─dplyr:::dplyr_internal_error(...)
#>  25. │ └─rlang::abort(class = c(class, "dplyr:::internal_error"), dplyr_error_data = data)
#>  26. │   └─rlang:::signal_abort(cnd, .file)
#>  27. │     └─base::signalCondition(cnd)
#>  28. └─dplyr (local) `<fn>`(`<dpl:::__>`)
#>  29.   └─rlang::abort(message, class = error_class, parent = parent, call = error_call)

Created on 2023-02-11 with reprex v2.0.2

lbui30 commented 1 year ago

Its looks like though the reprex is not reading my OS correctly, I will edit it fix it above: os Windows 11 x64 (build 22000)

EmilHvitfeldt commented 1 year ago

Thank you! I have been able to reproduce it!

library(modeldata)
library(tidyverse)
library(pls)
#> 
#> Attaching package: 'pls'
#> The following object is masked from 'package:stats':
#> 
#>     loadings
library(tidymodels)
library(sessioninfo)
data(meats)

norm_rec <- 
  recipe(protein ~ ., data = meats) %>%
  step_normalize(everything()) 

set.seed(57343)

folds <- vfold_cv(meats, repeats = 10) %>%
  mutate(recipes = map(splits, prepper, recipe = norm_rec))

get_var_explained <- function(recipe, ...) {
  # Extract the predictors and outcomes into their own matrices
  y_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_outcomes())
  x_mat <- bake(recipe, new_data = NULL, composition = "matrix", all_predictors())

  # The pls package prefers the data in a data frame where the outcome
  # and predictors are in _matrices_. To make sure this is formatted
  # properly, use the `I()` function to inhibit `data.frame()` from making
  # all the individual columns. `pls_format` should have two columns.
  pls_format <- data.frame(
    endpoints = I(y_mat),
    measurements = I(x_mat)
  )
  # Fit the model
  mod <- plsr(endpoints ~ measurements, data = pls_format)

  # Get the proportion of the predictor variance that is explained
  # by the model for different number of components. 
  xve <- explvar(mod)/100 

  # To do the same for the outcome, it is more complex. This code 
  # was extracted from pls:::summary.mvr. 
  explained <- 
    drop(pls::R2(mod, estimate = "train", intercept = FALSE)$val) %>% 
    # transpose so that components are in rows
    t() %>% 
    as_tibble() %>%
    # Add the predictor proportions
    mutate(predictors = cumsum(xve) %>% as.vector(),
           components = seq_along(xve)) %>%
    # Put into a tidy format that is tall
    pivot_longer(
      cols = c(-components),
      names_to = "source",
      values_to = "proportion"
    )
}

folds <- 
  folds %>%
  mutate(var = map(recipes, get_var_explained),
         var = unname(var))
#> Error in `mutate()`:
#> ℹ In argument: `var = map(recipes, get_var_explained)`.
#> Caused by error in `map()`:
#> ℹ In index: 1.
#> Caused by error in `mutate()`:
#> ℹ In argument: `predictors = cumsum(xve) %>% as.vector()`.
#> Caused by error:
#> ! `predictors` must be size 1, not 102.

#> Backtrace:
#>      ▆
#>   1. ├─folds %>% ...
#>   2. ├─dplyr::mutate(., var = map(recipes, get_var_explained), var = unname(var))
#>   3. ├─dplyr:::mutate.data.frame(...)
#>   4. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#>   5. │   ├─base::withCallingHandlers(...)
#>   6. │   └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#>   7. │     └─mask$eval_all_mutate(quo)
#>   8. │       └─dplyr (local) eval()
#>   9. ├─purrr::map(recipes, get_var_explained)
#>  10. │ └─purrr:::map_("list", .x, .f, ..., .progress = .progress)
#>  11. │   ├─purrr:::with_indexed_errors(...)
#>  12. │   │ └─base::withCallingHandlers(...)
#>  13. │   ├─purrr:::call_with_cleanup(...)
#>  14. │   └─global .f(.x[[i]], ...)
#>  15. │     └─... %>% ...
#>  16. ├─tidyr::pivot_longer(...)
#>  17. ├─dplyr::mutate(., predictors = cumsum(xve) %>% as.vector(), components = seq_along(xve))
#>  18. ├─dplyr:::mutate.data.frame(., predictors = cumsum(xve) %>% as.vector(), components = seq_along(xve))
#>  19. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#>  20. │   ├─base::withCallingHandlers(...)
#>  21. │   └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#>  22. │     └─mask$eval_all_mutate(quo)
#>  23. │       └─dplyr (local) eval()
#>  24. ├─dplyr:::dplyr_internal_error(...)
#>  25. │ └─rlang::abort(class = c(class, "dplyr:::internal_error"), dplyr_error_data = data)
#>  26. │   └─rlang:::signal_abort(cnd, .file)
#>  27. │     └─base::signalCondition(cnd)
#>  28. └─dplyr (local) `<fn>`(`<dpl:::__>`)
#>  29.   └─rlang::abort(message, class = error_class, parent = parent, call = error_call)

Created on 2023-02-16 with reprex v2.0.2

EmilHvitfeldt commented 1 year ago

I figured out the issue. The changes to purrr didn't break the tutorial outright, but it did break when the number of outcomes was reduced to 1 instead of 3 (as in the document), then drop(pls::R2(mod, estimate = "train", intercept = FALSE)$val) would produce a numeric vector instead of a numeric matrix because the dimensions were [1, 1, 102].

This PR fixes this edge-case by using abind::adrop() instead of drop() https://github.com/tidymodels/tidymodels.org/pull/277.

I will thus close this issue.

github-actions[bot] commented 1 year ago

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.