finetune
finetune copied to clipboard
tidymodels
Groupwise metrics not working with finetune
The problem
I'm having trouble with using groupwise metrics with finetune
Reproducible example
pacman::p_load(tidymodels, finetune)
data(ames)
diff_range <- function(x) {
diff(range(x$.estimate))
}
demographic_parity_ <-
new_groupwise_metric(
fn = detection_prevalence,
name = "demographic_parity",
aggregate = diff_range
)
m_set <- metric_set(demographic_parity_(Neighborhood))
set.seed(502)
ames_split <- initial_split(ames)
ames_train <- training(ames_split)
ames_folds <- vfold_cv(ames_train, v = 10)
rf_model <-
rand_forest(trees = tune()) %>%
set_engine("ranger") %>%
set_mode("classification")
rf_wflow <-
workflow() %>%
add_formula(
Lot_Shape ~ Year_Built + Bldg_Type + Latitude + Longitude
) %>%
add_model(rf_model)
grid <- parameters(trees(c(10, 100))) %>%
grid_max_entropy(size = 10)
tune_res_anova <- tune_race_anova(
rf_wflow,
ames_folds,
grid = grid,
metrics = m_set
)
#> Warning in max(best_config$B, na.rm = TRUE): no non-missing arguments to max;
#> returning -Inf
#> Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels
#> Warning: package 'ranger' was built under R version 4.2.3
#> Warning: package 'rlang' was built under R version 4.2.3
#> Warning in max(best_config$B, na.rm = TRUE): no non-missing arguments to max;
#> returning -Inf
#> Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]): contrasts can be applied only to factors with 2 or more levels
Created on 2024-08-30 with reprex v2.0.2
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.1 (2022-06-23 ucrt)
#> os Windows 10 x64 (build 19045)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate Finnish_Finland.utf8
#> ctype Finnish_Finland.utf8
#> tz Europe/Helsinki
#> date 2024-08-30
#> pandoc 2.18 @ C:/Program Files/RStudio/bin/quarto/bin/tools/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
#> boot 1.3-28 2021-05-03 [1] CRAN (R 4.2.1)
#> broom * 1.0.5 2023-06-09 [1] CRAN (R 4.2.1)
#> class 7.3-20 2022-01-16 [1] CRAN (R 4.2.1)
#> cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.3)
#> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.1)
#> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.3)
#> data.table 1.14.2 2021-09-27 [1] CRAN (R 4.2.1)
#> dials * 1.2.0 2023-04-03 [1] CRAN (R 4.2.3)
#> DiceDesign 1.9 2021-02-13 [1] CRAN (R 4.2.1)
#> digest 0.6.30 2022-10-18 [1] CRAN (R 4.2.2)
#> dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.2.3)
#> evaluate 0.23 2023-11-01 [1] CRAN (R 4.2.3)
#> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.2.3)
#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.1)
#> finetune * 1.1.0 2023-04-19 [1] CRAN (R 4.2.3)
#> foreach 1.5.2 2022-02-02 [1] CRAN (R 4.2.1)
#> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.1)
#> furrr 0.3.1 2022-08-15 [1] CRAN (R 4.2.1)
#> future 1.33.0 2023-07-01 [1] CRAN (R 4.2.3)
#> future.apply 1.11.0 2023-05-21 [1] CRAN (R 4.2.3)
#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.1)
#> ggplot2 * 3.5.0 2024-02-23 [1] CRAN (R 4.2.3)
#> globals 0.16.2 2022-11-21 [1] CRAN (R 4.2.1)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.1)
#> gower 1.0.1 2022-12-22 [1] CRAN (R 4.2.2)
#> GPfit 1.0-8 2019-02-08 [1] CRAN (R 4.2.1)
#> gtable 0.3.5 2024-04-22 [1] CRAN (R 4.2.1)
#> hardhat 1.3.1.9000 2024-04-11 [1] Github (tidymodels/hardhat@ed8b032)
#> htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.2.3)
#> infer * 1.0.4 2022-12-02 [1] CRAN (R 4.2.3)
#> ipred 0.9-14 2023-03-09 [1] CRAN (R 4.2.3)
#> iterators 1.0.14 2022-02-05 [1] CRAN (R 4.2.1)
#> knitr 1.45 2023-10-30 [1] CRAN (R 4.2.3)
#> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.1)
#> lava 1.7.2.1 2023-02-27 [1] CRAN (R 4.2.3)
#> lhs 1.1.5 2022-03-22 [1] CRAN (R 4.2.1)
#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.2.3)
#> listenv 0.9.0 2022-12-16 [1] CRAN (R 4.2.3)
#> lme4 1.1-30 2022-07-08 [1] CRAN (R 4.2.1)
#> lubridate 1.9.0 2022-11-06 [1] CRAN (R 4.2.2)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.1)
#> MASS 7.3-58.1 2022-08-03 [1] CRAN (R 4.2.1)
#> Matrix 1.5-3 2022-11-11 [1] CRAN (R 4.2.2)
#> minqa 1.2.4 2014-10-09 [1] CRAN (R 4.2.1)
#> modeldata * 1.3.0 2024-01-21 [1] CRAN (R 4.2.3)
#> modelenv 0.1.0 2022-10-17 [1] CRAN (R 4.2.2)
#> munsell 0.5.1 2024-04-01 [1] CRAN (R 4.2.3)
#> nlme 3.1-159 2022-08-09 [1] CRAN (R 4.2.1)
#> nloptr 2.0.3 2022-05-26 [1] CRAN (R 4.2.1)
#> nnet 7.3-17 2022-01-16 [1] CRAN (R 4.2.1)
#> pacman 0.5.1 2019-03-11 [1] CRAN (R 4.2.1)
#> parallelly 1.36.0 2023-05-26 [1] CRAN (R 4.2.3)
#> parsnip * 1.2.1.9001 2024-04-11 [1] Github (tidymodels/parsnip@853d814)
#> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.3)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.1)
#> prodlim 2023.03.31 2023-04-02 [1] CRAN (R 4.2.3)
#> purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.2.3)
#> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.2)
#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0)
#> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0)
#> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.2.2)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.1)
#> ranger * 0.15.1 2023-04-03 [1] CRAN (R 4.2.3)
#> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.1)
#> recipes * 1.0.10.9000 2024-04-11 [1] Github (tidymodels/recipes@63ced27)
#> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.2.1)
#> rlang * 1.1.3 2024-01-10 [1] CRAN (R 4.2.3)
#> rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.2.3)
#> rpart 4.1.16 2022-01-24 [1] CRAN (R 4.2.1)
#> rsample * 1.2.0 2023-08-23 [1] CRAN (R 4.2.3)
#> rstudioapi 0.15.0.9000 2024-02-29 [1] Github (rstudio/rstudioapi@5b639f9)
#> scales * 1.3.0 2023-11-28 [1] CRAN (R 4.2.3)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.1)
#> styler 1.10.2 2023-08-29 [1] CRAN (R 4.2.3)
#> survival 3.4-0 2022-08-09 [1] CRAN (R 4.2.1)
#> tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.3)
#> tidymodels * 1.1.1 2023-08-24 [1] CRAN (R 4.2.3)
#> tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.2.3)
#> tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.2.3)
#> timechange 0.1.1 2022-11-04 [1] CRAN (R 4.2.2)
#> timeDate 4022.108 2023-01-07 [1] CRAN (R 4.2.3)
#> tune * 1.2.1 2024-04-18 [1] CRAN (R 4.2.3)
#> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.2.3)
#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.2.3)
#> withr 3.0.1 2024-07-31 [1] CRAN (R 4.2.1)
#> workflows * 1.1.4.9000 2024-04-22 [1] Github (tidymodels/workflows@89005f5)
#> workflowsets * 1.0.1 2023-04-06 [1] CRAN (R 4.2.3)
#> xfun 0.42 2024-02-08 [1] CRAN (R 4.2.3)
#> yaml 2.3.8 2023-12-11 [1] CRAN (R 4.2.3)
#> yardstick * 1.3.0 2024-01-19 [1] CRAN (R 4.2.3)
#>
#> [1] C:/Users/saariea/R-4.2.1/library
#>
#> ──────────────────────────────────────────────────────────────────────────────
Just noting that I can reproduce this and 1) don't see the failure with tune_grid() nor 2) with detection_prevalence on its own! Will revisit this when we're focused in on the package again—thanks for the reprex.
