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 通过配方和模型参数调整工作流程。调整单个工作流时没有问题。但是，在调整具有多个工作流的工作流集时，它总是会失败。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"))

我检查了 wf_tune_list_finalize 中的选项它显示：

> 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)

详细消息表明我在工作流 ds_rf 和 ds_xgb[=41= 中的参数有问题]:

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]>

另外，no_rf和no_xgb虽然有调优结果，但我发现范围这两个工作流程中的mtry不是我上面设置的范围，也就是说参数范围设置步骤完全失败。我已经按照 https://www.tmwr.org/workflow-sets.html and https://workflowsets.tidymodels.org/ 的教程进行操作，但仍然没有任何想法。

那么在调整工作流集时如何正确设置配方和模型参数呢？

我代码中的train.csv在这里：https://github.com/liuyifeikim/Some-data

Answer 1

修改了参数设置步骤，现在调优结果正确：

# 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")

其余不变。我认为可能有一些有效的方法来设置参数。

使用 tidymodels 调整工作流集时如何正确设置参数网格？

How to set the parameters grids correctly when tuning the workflowset with tidymodels?

r

machine-learning

workflow

tidymodels

r-recipes