r - Tidymodels 包 : Visualising a random forest model using ggplot() to show the most important predictors

Question

概览

我正在按照教程(见下文)从袋装树、随机森林、提升树和一般线性模型中找到最适合的模型。

教程(参见下面的示例)

https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/

问题

在这种情况下，我想进一步探索数据，并为我在随机森林模型中的数据可视化最重要的预测变量(见下图)。

我的数据框称为FID，随机森林模型中的预测变量涉及:

1. 年份(数字)
2. 月份(因子)
3. 天数(数字)

因变量是频率(数字)

当我尝试运行绘图以可视化最重要的预测变量时，我不断收到此错误消息:-

Error: Problem with `mutate()` input `oob_rmse`.
x non-numeric argument to mathematical function
ℹ Input `oob_rmse` is `map_dbl(fit, ~sqrt(.x$prediction.error))`.
Run `rlang::last_error()` to see where the error occurred.
Called from: signal_abort(cnd)

如果有人对如何修复错误消息有任何建议，我将不胜感激。

提前致谢

如何从教程中的 R 代码生成绘图的示例

可视化模型

绘制以显示教程中 R 代码中最重要的预测变量

我的 R 代码

##Open libraries
library(tidymodels)
library(parsnip)
library(forcats)
library(ranger)
library(baguette)

###########################################################
#split this single dataset into two: a training set and a testing set
data_split <- initial_split(FID)
# Create data frames for the two sets:
train_data <- training(data_split)
test_data  <- testing(data_split)

 # resample the data with 10-fold cross-validation (10-fold by default)
  cv <- vfold_cv(train_data, v=3)
###########################################################

##Produce the recipe

rec <- recipe(Frequency ~ ., data = FID) %>% 
          step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
          step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels 
          step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars"))  %>% # replaces missing numeric observations with the median
          step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables

###################################################################################


    ###################################################
    ##Random forests
    ###################################################
    
    mod_rf <-rand_forest(trees = 1e3) %>%
                                  set_engine("ranger",
                                  num.threads = parallel::detectCores(), 
                                  importance = "permutation", 
                                  verbose = TRUE) %>% 
                                  set_mode("regression") 
                                  
    ##Create Workflow
    
    wflow_rf <- workflow() %>% 
                   add_model(mod_rf) %>% 
                         add_recipe(rec)
    
    ##Fit the model
    
    plan(multisession)
    
    fit_rf<-fit_resamples(
                 wflow_rf,
                 cv,
                 metrics = metric_set(rmse, rsq),
                 control = control_resamples(save_pred = TRUE,
                                             extract = function(x) extract_model(x)))
    
    
    # extract roots
    rf_tree_roots <- function(x){
                         map_chr(1:1000, 
                            ~ranger::treeInfo(x, tree = .)[1, "splitvarName"])
                                }
    
    rf_roots <- function(x){
                           x %>% 
                            dplyr::select(.extracts) %>% 
                            unnest(cols = c(.extracts)) %>% 
                            dplyr::mutate(fit = map(.extracts,
                            ~.x$fit$fit$fit),
                            oob_rmse = map_dbl(fit,
                                  ~sqrt(.x$prediction.error)),
                             roots = map(fit, 
                            ~rf_tree_roots(.))
                                   ) %>% 
                            dplyr::select(roots) %>% 
                            unnest(cols = c(roots))
                            }
    
    ##Open a plotting window
    dev.new()
    
    # plot
    rf_roots(fit_rf) %>% 
                group_by(roots) %>% 
                count() %>% 
                dplyr::arrange(desc(n)) %>% 
                dplyr::filter(n > 75) %>% 
                ggplot(aes(fct_reorder(roots, n), n)) +
                geom_col() + 
                coord_flip() + 
                labs(x = "root", y = "count")

##Error message

Error: Problem with `mutate()` input `oob_rmse`.
x non-numeric argument to mathematical function
ℹ Input `oob_rmse` is `map_dbl(fit, ~sqrt(.x$prediction.error))`.
Run `rlang::last_error()` to see where the error occurred.
Called from: signal_abort(cnd)

数据框 - FID

  structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015, 
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March", 
"April", "May", "June", "July", "August", "September", "October", 
"November", "December"), class = "factor"), Frequency = c(36, 
28, 39, 46, 5, 0, 0, 22, 10, 15, 8, 33, 33, 29, 31, 23, 8, 9, 
7, 40, 41, 41, 30, 30, 44, 37, 41, 42, 20, 0, 7, 27, 35, 27, 
43, 38), Days = c(31, 28, 31, 30, 6, 0, 0, 29, 15, 
29, 29, 31, 31, 29, 30, 30, 7, 0, 7, 30, 30, 31, 30, 27, 31, 
28, 30, 30, 21, 0, 7, 26, 29, 27, 29, 29)), row.names = c(NA, 
-36L), class = "data.frame")

Answer 1

它不会正确提取错误，如果您查看包含所有模型的小标题:

fit_rf$.extracts
[[1]]
# A tibble: 1 x 1
  .extracts
  <list>   
1 <ranger> 

它嵌入在列表或列表中，但没有名称:

names(fit_rf$.extracts[[1]][[1]])
NULL

因此这部分将失败:

map(fit_rf$.extracts,~.x$fit$fit$fit)

如果您查看第一个 unnest 之后的结构，这已经是合适的:

fit_rf %>% dplyr::select(.extracts) %>% unnest(cols = c(.extracts)) 
# A tibble: 3 x 1
  .extracts
  <list>   
1 <ranger> 
2 <ranger> 
3 <ranger> 

所以我们可以这样做:

rf_roots <- function(x){
                       x %>% 
                       select(.extracts) %>% 
                       unnest(cols = c(.extracts)) %>% 
                       mutate(oob_rmse = map_dbl(.extracts,
                                  ~sqrt(.x$prediction.error)),
                              roots = map(.extracts, 
                                  ~rf_tree_roots(.))
                               ) %>% 
                        dplyr::select(roots) %>% 
                        unnest(cols = c(roots))
                        }

这现在可以工作了:

rf_roots(fit_rf)
# A tibble: 3,000 x 1
   roots          
   <chr>          
 1 Month_August   
 2 Year           
 3 Month_July     
 4 Month_September
 5 Month_December 
 6 Month_March    
 7 Month_July     
 8 Month_September
 9 Month_December 
10 Days        

附加组件:如果目标是获取每个模型中每棵树的根变量，可以简单地执行:

root_vars = unnest(fit_rf,.extracts) %>% 
pull(.extracts) %>% 
map(rf_tree_roots)

或以 R 为基数:

lapply(fit_rf$.extracts,function(i)rf_tree_roots(i[[1]][[1]]))

你可以很容易地取消列出它来制作条形图。