using fit_best with wflow_sets

[tryhard124]

I'm currently trying to extract and fit my tuning results from a wflow set and running into some issues with fit_best. I'm running log reg with the glmnet engine which supports case weights. I added my importance weights into my dataset

int <- int |> 
  group_by(SubjectID) |> 
  mutate(
    visits = as.numeric(n()),
    visits_group = case_when(
      visits <= 3 ~ 'low',
      visits > 3 & visits <= 6 ~ 'med',
      visits > 6 ~ 'high'
    )
  ) |> 
  dplyr::ungroup() |> 
  relocate(visits, visits_group, .before = Ethnicity) |> 
  mutate(is_female = as.factor(is_female),
         imp_weight = importance_weights(1/visits))

I checked that they are the correct object

> hardhat::is_case_weights(int$imp_weight)
[1] TRUE

I checked that it was in my recipe

> sex_recipes[1]
$transcript

── Recipe ─────────────────────────────────────────────────────────────────────────────────────────

── Inputs 
Number of variables by role
outcome:          1
predictor:    10346
case_weights:     1
ID:            1972

I made my worflow set 

sex_models_set <- 
  workflow_set(
    preproc = sex_recipes,
    models = list(glmnet = tune_lg_glmnet_el),
    case_weights = imp_weight,
    cross = TRUE
  )

I extracted my workflows to check that my case weights were there (showing one for simplicity)

transcript_wflow <- extract_workflow(sex_models_set, "transcript_glmnet")
> transcript_wflow
══ Workflow ═══════════════════════════════════════════════════════════════════════════════════════
Preprocessor: Recipe
Model: logistic_reg()

── Preprocessor ───────────────────────────────────────────────────────────────────────────────────
2 Recipe Steps

• step_zv()
• step_normalize()

── Case Weights ───────────────────────────────────────────────────────────────────────────────────
imp_weight

── Model ──────────────────────────────────────────────────────────────────────────────────────────
Logistic Regression Model Specification (classification)

Main Arguments:
  penalty = tune()
  mixture = tune()

Computational engine: glmnet 

I tune my workdlow and it runs fine but, when I try to extract and fit my results I get this

> fit_best(protein_result, metric = "accuracy")
Error in `vec_equal()`:
! Can't combine `..1` <importance_weights> and `..2` <double>.
Run `rlang::last_trace()` to see where the error occurred.
> rlang::last_trace()
<error/vctrs_error_ptype2>
Error in `vec_equal()`:
! Can't combine `..1` <importance_weights> and `..2` <double>.
---
Backtrace:
     ▆
  1. ├─tune::fit_best(protein_result, metric = "accuracy")
  2. └─tune:::fit_best.tune_results(protein_result, metric = "accuracy")
  3.   ├─parsnip::fit(wflow, dat)
  4.   └─workflows:::fit.workflow(wflow, dat)
  5.     └─workflows:::toggle_sparsity(object, data)
  6.       ├─recipes::.recipes_estimate_sparsity(extract_preprocessor(object))
  7.       └─recipes:::.recipes_estimate_sparsity.recipe(extract_preprocessor(object))
  8.         └─sparsevctrs::sparsity(template, sample = 1000)
  9.           └─sparsevctrs:::sparsity_df(x)
 10.             └─base::vapply(x, count_zeroes, double(1))
 11.               └─sparsevctrs (local) FUN(X[[i]], ...)
 12.                 └─vctrs:::`==.vctrs_vctr`(x, 0)
 13.                   └─vctrs::vec_equal(e1, e2)
Run rlang::last_trace(drop = FALSE) to see 11 hidden frames.
> fit_best(sex_models_set)
Error in `vec_equal()`:
! Can't combine `..1` <importance_weights> and `..2` <double>.
Run `rlang::last_trace()` to see where the error occurred.

My hypothesis is that it's trying to check for sparsity in the imp_weight column even though it isn't a predictor, but I don't know how to correct this behavior. Any help is greatly appreciated?

[Max]

I believe that this is a bug that we just fixed. Try loading the latest sparsevctrs package from CRAN (version 0.3.2)