CODEWITHSUNDEEP
rmachine-learningregressionrandom-foresttidymodels
Alice Hobbs
Alice Hobbs

Reputation: 1215

Tidymodels(Fitting a random forest with fit_samples()): Fold01: internal: Error: Must group by variables found in `.data`

Overview

I have produced a random forest regression model, and, my aim is to fit the model using the function fit_samples() function, and then tune the hyperparameters. However, I am experiencing this error message below:

Error Message:

   ! Fold01: model: tune columns were requested but there were 14 predictors in the data. 14 will be u...
   x Fold01: internal: Error: Must group by variables found in `.data`.
   * Column `mtry` is not found.

   ! Fold02: model: tune columns were requested but there were 14 predictors in the data. 14 will be u...
   x Fold02: internal: Error: Must group by variables found in `.data`.
   * Column `mtry` is not found.

   ! Fold03: model: tune columns were requested but there were 14 predictors in the data. 14 will be u...
   x Fold03: internal: Error: Must group by variables found in `.data`.
   * Column `mtry` is not found.

I have done an online search for a solution, but I cannot find a question that aligns with my particular issue. I am not an advanced R user, and I am trying my very best to slowly manoeuvre myself through the Tidymodels package

If anyone can help with this error message, I would be deeply appreciative.

Many thanks in advance

R-code

   seed(45L)

   #Open libraries
   library(tidymodels)
   library(ranger)
   library(dplyr)

   #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=10)

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

  #Produce the random forest model

       mod_rf <- rand_forest(
                            mtry = tune(),
                            trees = 1000,
                            min_n = tune()
                             ) %>%
                           set_mode("regression") %>%
                           set_engine("ranger")  

   ##Workflow
      wflow_rf <- workflow() %>% 
                            add_model(mod_rf) %>% 
                                        add_recipe(rec)

    ##Fit 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)))

   #Error Message

   ! Fold01: model: tune columns were requested but there were 14 predictors in the data. 14 will be u...
   x Fold01: internal: Error: Must group by variables found in `.data`.
   * Column `mtry` is not found.

   ! Fold02: model: tune columns were requested but there were 14 predictors in the data. 14 will be u...
   x Fold02: internal: Error: Must group by variables found in `.data`.
   * Column `mtry` is not found.

   ! Fold03: model: tune columns were requested but there were 14 predictors in the data. 14 will be u...
   x Fold03: internal: Error: Must group by variables found in `.data`.
   * Column `mtry` is not found.

Data Frame 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")

Upvotes: 2

Views: 1173

Answers (1)

StupidWolf
StupidWolf

Reputation: 46908

If you check the help page for fit_resamples:

fit_resamples() computes a set of performance metrics across one or more resamples. It does not perform any tuning (see tune_grid() and tune_bayes() for that)

Most likely you need to tune first, and then run fit_resamples() using the parameters obtained from the tuning, for example:

rf_grid <- expand.grid(mtry = 2:4, min_n = c(10,15,20))

mod_rf <- rand_forest(
                      mtry = tune(),
                      trees = 1000,
                      min_n = tune()
                      ) %>%
                      set_mode("regression") %>%
                      set_engine("ranger")  

wflow_rf <- workflow() %>% 
            add_model(mod_rf) %>% 
            add_recipe(rec)

rf_res <- 
  wflow_rf %>% 
  tune_grid(
    resamples = cv,grid = rf_grid
    )

Find the best parameter:

show_best(rf_res,metric="rmse")
# A tibble: 5 x 7
   mtry min_n .metric .estimator  mean     n std_err
  <int> <dbl> <chr>   <chr>      <dbl> <int>   <dbl>
1     4    10 rmse    standard    7.87    10   0.743
2     4    15 rmse    standard    8.27    10   0.649
3     3    10 rmse    standard    8.49    10   0.682
4     3    15 rmse    standard    8.97    10   0.620
5     4    20 rmse    standard    9.49    10   0.605

And run it again:

mod_rf <- rand_forest(mtry = 4,trees = 1000,min_n = 10) %>%
          set_mode("regression") %>%
          set_engine("ranger")  

wflow_rf <- workflow() %>% 
            add_model(mod_rf) %>% 
            add_recipe(rec)

fit_rf<-fit_resamples(
                    wflow_rf,
                    cv,
                    metrics = metric_set(rmse, rsq),
                    control = control_resamples(save_pred = TRUE,
                    extract = function(x) extract_model(x)))

Upvotes: 3

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