@Max ,
Thanks for your patience with my basic questions here! I have spent loads of time at the wonderful pkgdown site but I don't always end up finding all of the relevant bits (that's obviously on me! it's beautifully organized -- apologies for asking questions that are answered there. I'm always happy to read tutorials and documentation that you link to).
Information on parameters: E.g., for parameters that have an unknown() bit, how can you see what value was filled in (in one of the resulting objects created), or where in the documentation does it list the equation for calculating, eg, the max value for mtry? (I assume the max value for mtry is the number of columns, but I see that there are other parameters that I haven't touched that also have unknown() listed, like sample_size, and would love to know where to see how default values are chosen, if unspecified by the user.)
My tidymodels seems to be up to date -- I am sure this is a user error on where I'm specifying tune = scale_pos_weight() or my lack of specification of a range.
Scale pos weight custom range: The default scale_pos_weight range doesn't work for me, but I haven't yet made sense of what needs to happen to provide custom range (eg finalize? most of the examples I've seen have hyperparameters that are either tuned over default ranges or set, not tuned over custom ranges).
library(mlbench)
library(forcats)
library(tidymodels)
#> Warning: package 'tidymodels' was built under R version 4.0.5
#> Warning in system("timedatectl", intern = TRUE): running command 'timedatectl'
#> had status 1
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
#> Warning: package 'broom' was built under R version 4.0.5
#> Warning: package 'dials' was built under R version 4.0.5
#> Warning: package 'infer' was built under R version 4.0.5
#> Warning: package 'modeldata' was built under R version 4.0.5
#> Warning: package 'parsnip' was built under R version 4.0.5
#> Warning: package 'recipes' was built under R version 4.0.5
#> Warning: package 'rsample' was built under R version 4.0.5
#> Warning: package 'tibble' was built under R version 4.0.5
#> Warning: package 'tune' was built under R version 4.0.5
#> Warning: package 'workflows' was built under R version 4.0.5
#> Warning: package 'workflowsets' was built under R version 4.0.5
library(finetune)
#> Warning: package 'finetune' was built under R version 4.0.5
data("PimaIndiansDiabetes")
set.seed(24)
df <- PimaIndiansDiabetes %>%
mutate(diabetes = fct_relevel(diabetes, 'pos'))
xgb_rec <- recipe(diabetes ~ ., data = df)
xgb_spec <- boost_tree(
trees = tune()) %>%
set_engine("xgboost", scale_pos_weight = tune()) %>%
set_mode("classification")
resamples_cv <- vfold_cv(df, v = 5)
my_metrics <- metric_set(mn_log_loss, roc_auc, pr_auc)
xgb_wf <- workflow() %>%
add_recipe(xgb_rec) %>%
add_model(xgb_spec)
xgb_rs <- tune_race_anova(
xgb_wf,
resamples = resamples_cv,
grid = 10,
metrics = my_metrics,
control = control_race(verbose_elim = TRUE)
)
#> Error: The workflow has arguments to be tuned that are missing some parameter objects: 'scale_pos_weight'
sessionInfo()
#> R version 4.0.4 (2021-02-15)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CloudForms
#>
#> Matrix products: default
#> BLAS: /usr/local/lib64/R/lib/libRblas.so
#> LAPACK: /usr/local/lib64/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] finetune_0.1.0 yardstick_0.0.8 workflowsets_0.1.0 workflows_0.2.4
#> [5] tune_0.1.6 tidyr_1.1.4 tibble_3.1.5 rsample_0.1.1
#> [9] recipes_0.1.17 purrr_0.3.4 parsnip_0.1.7 modeldata_0.1.1
#> [13] infer_1.0.0 ggplot2_3.3.5 dplyr_1.0.7 dials_0.0.10
#> [17] scales_1.1.1 broom_0.7.10 tidymodels_0.1.4 forcats_0.5.1
#> [21] mlbench_2.1-3
#>
#> loaded via a namespace (and not attached):
#> [1] nlme_3.1-152 fs_1.5.0 lubridate_1.7.10 DiceDesign_1.9
#> [5] tools_4.0.4 backports_1.2.1 utf8_1.2.2 R6_2.5.1
#> [9] rpart_4.1-15 DBI_1.1.1 colorspace_2.0-2 nnet_7.3-15
#> [13] withr_2.4.2 tidyselect_1.1.1 compiler_4.0.4 cli_3.1.0
#> [17] stringr_1.4.0 digest_0.6.28 minqa_1.2.4 rmarkdown_2.11
#> [21] pkgconfig_2.0.3 htmltools_0.5.2 parallelly_1.24.0 lme4_1.1-27.1
#> [25] styler_1.4.1 lhs_1.1.1 fastmap_1.1.0 highr_0.9
#> [29] rlang_0.4.12 rstudioapi_0.13 generics_0.1.1 jsonlite_1.7.2
#> [33] magrittr_2.0.1 Matrix_1.3-2 Rcpp_1.0.7 munsell_0.5.0
#> [37] fansi_0.5.0 GPfit_1.0-8 lifecycle_1.0.1 furrr_0.2.2
#> [41] stringi_1.7.5 pROC_1.17.0.1 yaml_2.2.1 MASS_7.3-53
#> [45] plyr_1.8.6 grid_4.0.4 parallel_4.0.4 listenv_0.8.0
#> [49] crayon_1.4.2 lattice_0.20-41 splines_4.0.4 knitr_1.36
#> [53] pillar_1.6.3 boot_1.3-26 xgboost_1.4.1.1 codetools_0.2-18
#> [57] reprex_2.0.0 glue_1.5.1 evaluate_0.14 data.table_1.14.0
#> [61] nloptr_1.2.2.2 vctrs_0.3.8 foreach_1.5.1 gtable_0.3.0
#> [65] future_1.21.0 assertthat_0.2.1 xfun_0.28 gower_0.2.2
#> [69] prodlim_2019.11.13 class_7.3-18 survival_3.2-7 timeDate_3043.102
#> [73] iterators_1.0.13 hardhat_0.1.6 lava_1.6.9 globals_0.14.0
#> [77] ellipsis_0.3.2 ipred_0.9-12
Created on 2022-01-04 by the reprex package (v2.0.0)
Thank you, thank you!!