Can you use reprex to make your examples to ensure that you are running this in a fresh session and there are no other packages loaded that may be interfering with your options?
This is what I get with your example code:
library(tidyverse)
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#> method from
#> required_pkgs.model_spec parsnip
options(tidymodels.dark = TRUE)
parks <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-22/parks.csv')
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> .default = col_double(),
#> city = col_character(),
#> park_pct_city_data = col_character(),
#> pct_near_park_data = col_character(),
#> spend_per_resident_data = col_character(),
#> city_dup = col_character()
#> )
#> ℹ Use `spec()` for the full column specifications.
modeling_df <- parks %>%
select(pct_near_park_data, spend_per_resident_data, med_park_size_data) %>%
rename(nearness = "pct_near_park_data",
spending = "spend_per_resident_data",
acres = "med_park_size_data") %>%
mutate(nearness = (parse_number(nearness)/100)) %>%
mutate(spending = parse_number(spending))
set.seed(123)
park_split <- initial_split(modeling_df)
park_train <- training(park_split)
park_test <- testing(park_split)
tree_rec <- recipe(nearness ~., data = park_train)
tree_prep <- prep(tree_rec)
juiced <- juice(tree_prep)
tune_spec <- rand_forest(
mtry = tune(),
trees = 1000,
min_n = tune()
) %>%
set_mode("regression") %>%
set_engine("ranger")
tune_wf <- workflow() %>%
add_recipe(tree_rec) %>%
add_model(tune_spec)
set.seed(234)
park_folds <- vfold_cv(park_train, v = 5)
set.seed(345)
tune_res <- tune_grid(
tune_wf,
resamples = park_folds,
grid = 20,
control = control_grid(verbose = TRUE)
)
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Created on 2021-06-27 by the reprex package (v2.0.0)
I do see the verbose output.
I suspect that you may have turned on parallel processing at some point, which turns off the ability to return verbose logging. It's not currently possible to collect logging from the workers with the parallel backends we support.