r - 使用 tidymodels 调整工作流集时如何正确设置参数网格？

Question

我尝试使用 tidymodels 来调整配方和模型参数的工作流程。调整单个工作流时没有问题。但是当使用多个工作流调整工作流集时，它总是失败。这是我的代码:

# read the training data
train <- read_csv("../../train.csv")
train <- train %>% 
    mutate(
      id = row_number(),
      across(where(is.double), as.integer),
      across(where(is.character), as.factor),
      r_yn = fct_relevel(r_yn, "yes")) %>% 
  select(id, r_yn, everything())

# setting the recipes

# no precess
rec_no <- recipe(r_yn ~ ., data = train) %>%
  update_role(id, new_role = "ID")

# downsample: tuning the under_ratio
rec_ds_tune <- rec_no %>% 
  step_downsample(r_yn, under_ratio = tune(), skip = TRUE, seed = 100) %>%
  step_nzv(all_predictors(), freq_cut = 100)

# setting the models

# randomforest
spec_rf_tune <- rand_forest(trees = 100, mtry = tune(), min_n = tune()) %>%
  set_engine("ranger", seed = 100) %>%
  set_mode("classification")

# xgboost
spec_xgb_tune <- boost_tree(trees = 100, mtry = tune(), tree_depth = tune(), learn_rate = tune(), min_n = tune()) %>% 
   set_engine("xgboost") %>% 
   set_mode("classification")

# setting the workflowsets
wf_tune_list <- workflow_set(
  preproc = list(no = rec_no, ds = rec_ds_tune),
  models = list(rf = spec_rf_tune, xgb = spec_xgb_tune),
  cross = TRUE)

# finalize the parameters, I'm not sure it is correct or not
rf_params <- spec_rf_tune %>% parameters() %>% update(mtry = mtry(c(1, 15)))
xgb_params <- spec_xgb_tune %>% parameters() %>% update(mtry = mtry(c(1, 15)))
ds_params <- rec_ds_tune %>% parameters() %>% update(under_ratio = under_ratio(c(1, 5)))

wf_tune_list_finalize <- wf_tune_list %>% 
  option_add(param = ds_params, id = c("ds_rf", "ds_xgb")) %>% 
  option_add(param = rf_params, id = c("no_rf", "ds_rf")) %>% 
  option_add(param = xgb_params, id = c("no_xgb", "ds_xgb"))

我查了 选项 在 wf_tune_list_finalize 表明:

> wf_tune_list_finalize$option
[[1]]
a list of options with names:  'param'

[[2]]
a list of options with names:  'param'

[[3]]
a list of options with names:  'param'

[[4]]
a list of options with names:  'param'

然后我调整这个工作流集:

# tuning the workflowset
cl <- makeCluster(detectCores())
registerDoParallel(cl)
wf_tune_race <- wf_tune_list_finalize %>%
  workflow_map(fn = "tune_race_anova",
               seed = 100,
               resamples = cv_5,
               grid = 3,
               metrics = metric_auc,
               control = control_race(parallel_over = "everything"), 
               verbose = TRUE)
stopCluster(cl)

详细消息表明我的工作流程中的参数有问题 ds_rf 和 ds_xgb :

i 1 of 4 tuning:     no_rf
i Creating pre-processing data to finalize unknown parameter: mtry
�� 1 of 4 tuning:     no_rf (1m 44.4s)
i 2 of 4 tuning:     no_xgb
i Creating pre-processing data to finalize unknown parameter: mtry
�� 2 of 4 tuning:     no_xgb (28.9s)
i 3 of 4 tuning:     ds_rf
x 3 of 4 tuning:     ds_rf failed with: Some tuning parameters require finalization but there are recipe parameters that require tuning. Please use `parameters()` to finalize the parameter ranges.
i 4 of 4 tuning:     ds_xgb
x 4 of 4 tuning:     ds_xgb failed with: Some tuning parameters require finalization but there are recipe parameters that require tuning. Please use `parameters()` to finalize the parameter ranges.

结果是:

> wf_tune_race
# A workflow set/tibble: 4 x 4
  wflow_id info             option      result        
  <chr>    <list>           <list>      <list>        
1 no_rf    <tibble [1 x 4]> <wrkflw__ > <race[+]>     
2 no_xgb   <tibble [1 x 4]> <wrkflw__ > <race[+]>     
3 ds_rf    <tibble [1 x 4]> <wrkflw__ > <try-errr [1]>
4 ds_xgb   <tibble [1 x 4]> <wrkflw__ > <try-errr [1]>

更重要的是，虽然 no_rf 和 no_xgb 有调优结果，我发现 的范围mtry 这两个工作流中的范围不是我上面设置的范围，这意味着参数范围设置步骤完全失败。我遵循了 https://www.tmwr.org/workflow-sets.html 中的教程和 https://workflowsets.tidymodels.org/但仍然没有想法。
那么如何在调整工作流集时正确设置配方和模型参数呢？
train.csv 在我的代码中: https://github.com/liuyifeikim/Some-data

Answer 1

我修改了参数设置步骤，现在调优结果是正确的:

# setting the parameters on each workflow seperately
no_rf_params <- wf_set_tune_list %>% 
  extract_workflow("no_rf") %>% 
  parameters() %>% 
  update(mtry = mtry(c(1, 15)))

no_xgb_params <- wf_set_tune_list %>% 
  extract_workflow("no_xgb") %>% 
  parameters() %>% 
  update(mtry = mtry(c(1, 15)))

ds_rf_params <- wf_set_tune_list %>% 
  extract_workflow("ds_rf") %>% 
  parameters() %>% 
  update(mtry = mtry(c(1, 15)), under_ratio = under_ratio(c(1, 5)))

ds_xgb_params <- wf_set_tune_list %>% 
  extract_workflow("ds_xgb") %>% 
  parameters() %>% 
  update(mtry = mtry(c(1, 15)), under_ratio = under_ratio(c(1, 5)))

# update the workflowset
wf_set_tune_list_finalize <- wf_set_tune_list %>% 
  option_add(param_info = no_rf_params, id = "no_rf") %>%
  option_add(param_info = no_xgb_params, id = "no_xgb") %>% 
  option_add(param_info = ds_rf_params, id = "ds_rf") %>% 
  option_add(param_info = ds_xgb_params, id = "ds_xgb")

其余的保持不变。我认为可能有一些有效的方法来设置参数。