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我正在使用单个数字变量调整随机森林。

数据表结构如下： tibble [617,622 x 29] (S3: tbl_df/tbl/data.frame) 和我拆分数据：

set.seed(123)
data_split <- initial_split(data, strata = var_class, prop = .70)
data_train <- training(data_split )
data_test <- as.data.frame(testing(data_split ))

使用以下配方对象和工作流程：

rec_1v_s <- data_train %>% 
  recipe(var_class ~ var1) %>%
  step_naomit(everything(), skip = TRUE) %>% 
  step_normalize(all_numeric()) %>%
  step_smote(var_class)

model_to_tune <- rand_forest(mode = "classification",
                                 trees = tune())%>% set_engine("ranger")
wflow_rf_1v <-
  workflow() %>%
  add_model(model_to_tune) %>%
  add_recipe(rec_1v_s )

我想调整树木。

set.seed(123)
rf_grid <- grid_latin_hypercube(
  trees(),
  size = 40)
race_ctrl <-
  control_race(
    save_pred = TRUE,
    parallel_over = "everything",
    save_workflow = FALSE
  )

然后我调整随机森林引擎的树：

tictoc::tic()
all_cores <- parallel::detectCores(logical = FALSE)
library(doFuture)
registerDoFuture()
cl <- parallel::makeCluster(all_cores-4)
plan(cluster, workers = cl)

# Option 1 tune_race

rf_tune_race <- wflow_rf_1v %>% 
  tune_race_win_loss(resamples = folds,
            grid = rf_grid,
            control = race_ctrl,
            metrics = metric_set(roc_auc, accuracy))

# Option 2 tune_grid
rf_tune_grid <- wflow_rf_1v %>% 
  tune_grid(resamples = folds,
            grid = rf_grid,
            control = race_ctrl,
            metrics = metric_set(roc_auc, accuracy))
tictoc::toc()

使用相同的规范，如果我运行 tune_grid 不会产生错误并且我有结果但是如果我运行 tune_race (anova or win_loss) 我会收到以下错误：

错误：arrange() 在隐式 mutate() 步骤失败。x 无法将大小 0 的输入回收到大小 1

tune_race_anova 和 tune_race_loss_win 仍然存在错误

该错误没有提供太多信息，我无法检测到它的来源。

以防万一它有帮助，我添加了 rlang 提供的错误的详细信息

rlang::last_error()
<error/dplyr_error>
arrange() failed at implicit mutate() step. 
x Can't recycle input of size 0 to size 1.
Backtrace:
Run `rlang::last_trace()` to see the full context.

rlang::last_trace()
<error/dplyr_error>
arrange() failed at implicit mutate() step. 
x Can't recycle input of size 0 to size 1.
Backtrace:
     x
  1. +-`%>%`(...)
  2. +-finetune::tune_race_win_loss(...)
  3. +-finetune:::tune_race_win_loss.workflow(...)
  4. | \-finetune:::tune_race_win_loss_workflow(...)
  5. |   \-`%>%`(...)
  6. +-tune::tune_grid(...)
  7. +-tune:::tune_grid.workflow(...)
  8. | \-tune:::tune_grid_workflow(...)
  9. |   \-tune:::tune_grid_loop(...)
 10. |     \-tune:::pull_metrics(resamples, results, control)
 11. |       \-tune:::pulley(resamples, res, ".metrics")
 12. |         +-dplyr::arrange(resamples, !!!syms(id_cols))
 13. |         \-dplyr:::arrange.data.frame(resamples, !!!syms(id_cols))
 14. |           \-dplyr:::arrange_rows(.data, dots)
 15. |             +-base::withCallingHandlers(...)
 16. |             +-dplyr::transmute(new_data_frame(.data), !!!quosures)
 17. |             \-dplyr:::transmute.data.frame(new_data_frame(.data), !!!quosures)
 18. |               +-dplyr::mutate(.data, !!!dots, .keep = "none")
 19. |               \-dplyr:::mutate.data.frame(.data, !!!dots, .keep = "none")
 20. |                 +-dplyr::dplyr_col_modify(.data, cols)
 21. |                 \-dplyr:::dplyr_col_modify.data.frame(.data, cols)
 22. |                   \-vctrs::vec_recycle_common(!!!cols, .size = nrow(data))
 23. +-vctrs:::stop_recycle_incompatible_size(...)
 24. | \-vctrs:::stop_vctrs(...)
 25. |   \-rlang::abort(message, class = c(class, "vctrs_error"), ...)
 26. |     \-rlang:::signal_abort(cnd)
 27. |       \-base::signalCondition(cnd)
 28. \-(function (cnd) ...
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