Tidymodels - 如何在 control = control_grid 中正确使用 verbose = TRUE

Question

我正在尝试使用 verbose = TRUE 来查看调整网格的进度。它似乎对我不起作用，是我把它放在错误的位置还是我使用不当？底部是我想要的屏幕截图，下面是没有所需输出的可重现代码。

library(tidyverse)
library(tidymodels)
options(tidymodels.dark = TRUE)

parks <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-22/parks.csv')

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)

set.seed(345)
tune_res <- tune_grid(
  tune_wf,
  resamples = park_folds,
  grid = 20,
 control = control_grid(verbose = TRUE)
)

# Results of running the code
> tune_res <- tune_grid(
+   tune_wf,
+   resamples = park_folds,
+   grid = 20,
+   control = control_grid(verbose = TRUE)
+ )
i Creating pre-processing data to finalize unknown parameter: mtry

这是我希望它看起来像的一个例子

Answer 1

您可以使用reprex来制作示例，以确保您在新的会话中运行它，并且没有加载其他可能干扰您的选项的包吗？

\n

这是我从你的示例代码中得到的结果：

\n

library(tidyverse)\nlibrary(tidymodels)\n#> Registered S3 method overwritten by \'tune\':\n#>   method                   from   \n#>   required_pkgs.model_spec parsnip\noptions(tidymodels.dark = TRUE)\n\nparks <- readr::read_csv(\'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-22/parks.csv\')\n#> \n#> \xe2\x94\x80\xe2\x94\x80 Column specification \xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\xe2\x94\x80\n#> cols(\n#>   .default = col_double(),\n#>   city = col_character(),\n#>   park_pct_city_data = col_character(),\n#>   pct_near_park_data = col_character(),\n#>   spend_per_resident_data = col_character(),\n#>   city_dup = col_character()\n#> )\n#> \xe2\x84\xb9 Use `spec()` for the full column specifications.\n\nmodeling_df <- parks %>% \n   select(pct_near_park_data, spend_per_resident_data, med_park_size_data) %>% \n   rename(nearness = "pct_near_park_data",\n          spending = "spend_per_resident_data",\n          acres = "med_park_size_data") %>% \n   mutate(nearness = (parse_number(nearness)/100)) %>% \n   mutate(spending = parse_number(spending))\n\nset.seed(123)\npark_split <- initial_split(modeling_df)\npark_train <- training(park_split)\npark_test <- testing(park_split)\n\ntree_rec <- recipe(nearness ~., data = park_train)\ntree_prep <- prep(tree_rec)\njuiced <- juice(tree_prep)\n\ntune_spec <- rand_forest(\n   mtry = tune(),\n   trees = 1000,\n   min_n = tune()\n) %>% \n   set_mode("regression") %>% \n   set_engine("ranger")\n\ntune_wf <- workflow() %>% \n   add_recipe(tree_rec) %>% \n   add_model(tune_spec)\n\nset.seed(234)\npark_folds <- vfold_cv(park_train, v = 5)\n\nset.seed(345)\ntune_res <- tune_grid(\n   tune_wf,\n   resamples = park_folds,\n   grid = 20,\n   control = control_grid(verbose = TRUE)\n)\n#> i Creating pre-processing data to finalize unknown parameter: mtry\n#> i Fold1: preprocessor 1/1\n#> \xe2\x9c\x93 Fold1: preprocessor 1/1\n#> i Fold1: preprocessor 1/1, model 1/20\n#> \xe2\x9c\x93 Fold1: preprocessor 1/1, model 1/20\n#> i Fold1: preprocessor 1/1, model 1/20 (predictions)\n#> i Fold1: preprocessor 1/1, model 2/20\n#> \xe2\x9c\x93 Fold1: preprocessor 1/1, model 2/20\n#> i Fold1: preprocessor 1/1, model 2/20 (predictions)\n#> i Fold1: preprocessor 1/1, model 3/20\n#> \xe2\x9c\x93 Fold1: preprocessor 1/1, model 3/20\n#> i Fold1: preprocessor 1/1, model 3/20 (predictions)\n#> i Fold1: preprocessor 1/1, model 4/20\n#> \xe2\x9c\x93 Fold1: preprocessor 1/1, model 4/20\n#> i Fold1: preprocessor 1/1, model 4/20 (predictions)\n#> i Fold1: preprocessor 1/1, model 5/20\n#> \xe2\x9c\x93 Fold1: preprocessor 1/1, model 5/20\n#> i Fold1: preprocessor 1/1, model 5/20 (predictions)\n#> i Fold1: preprocessor 1/1, model 6/20\n#> \xe2\x9c\x93 Fold1: 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preprocessor 1/1, model 9/20\n#> i Fold5: preprocessor 1/1, model 9/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 10/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 10/20\n#> i Fold5: preprocessor 1/1, model 10/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 11/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 11/20\n#> i Fold5: preprocessor 1/1, model 11/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 12/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 12/20\n#> i Fold5: preprocessor 1/1, model 12/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 13/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 13/20\n#> i Fold5: preprocessor 1/1, model 13/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 14/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 14/20\n#> i Fold5: preprocessor 1/1, model 14/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 15/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 15/20\n#> i Fold5: preprocessor 1/1, model 15/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 16/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 16/20\n#> i Fold5: preprocessor 1/1, model 16/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 17/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 17/20\n#> i Fold5: preprocessor 1/1, model 17/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 18/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 18/20\n#> i Fold5: preprocessor 1/1, model 18/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 19/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 19/20\n#> i Fold5: preprocessor 1/1, model 19/20 (predictions)\n#> i Fold5: preprocessor 1/1, model 20/20\n#> \xe2\x9c\x93 Fold5: preprocessor 1/1, model 20/20\n#> i Fold5: preprocessor 1/1, model 20/20 (predictions)\n

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^{由reprex 包(v2.0.0)于 2021-06-27 创建}

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我确实看到了详细的输出。

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我怀疑您可能在某个时候打开了并行处理，这会关闭返回详细日志记录的能力。目前无法使用我们支持的并行后端从工作人员那里收集日志记录。

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