Tidymodels - 如何在控件中正确使用 verbose = TRUE = control_grid
Tidymodels - How to correctly use verbose = TRUE in control = control_grid
我正在尝试使用 verbose = TRUE 查看调整网格的进度。
它似乎对我不起作用，是我放错地方了还是我使用不当？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示例
您能否使用 reprex 制作您的示例以确保您 运行 这是在一个新的会话中并且没有加载可能干扰您的选项的其他包？
这是我从您的示例代码中得到的结果：
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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由 reprex package (v2.0.0)
于 2021-06-27 创建
我确实看到了详细的输出。
我怀疑您可能在某个时候打开了并行处理，这会关闭 return 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收集日志记录。
我正在尝试使用 verbose = TRUE 查看调整网格的进度。 它似乎对我不起作用，是我放错地方了还是我使用不当？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示例
您能否使用 reprex 制作您的示例以确保您 运行 这是在一个新的会话中并且没有加载可能干扰您的选项的其他包？
这是我从您的示例代码中得到的结果：
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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由 reprex package (v2.0.0)
于 2021-06-27 创建我确实看到了详细的输出。
我怀疑您可能在某个时候打开了并行处理，这会关闭 return 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收集日志记录。