CODEWITHSUNDEEP
rmachine-learningfeature-engineeringtidymodels
Kim.L
Kim.L

Reputation: 161

Error: "Missing data in columns" when using tidymodels workflow to predict test set

Recently I learn to use tidymodels to build up machine learning workflow, but when I use the workflow to make the prediction on test set, it raises the error "Missing data in columns", but I am sure that neither the train and the test set has missing data. Here is my code and example:

# Imformation of the data：the Primary_type in test set has several novel levels
str(train_sample)
tibble [500,000 x 3] (S3: tbl_df/tbl/data.frame)
 $ ID          : num [1:500000] 6590508 2902772 6162081 7777470 7134849 ...
 $ Primary_type: Factor w/ 29 levels "ARSON","ASSAULT",..: 16 8 3 3 28 7 3 4 25 15 ...
 $ Arrest      : Factor w/ 2 levels "FALSE","TRUE": 2 1 1 1 1 2 1 1 1 1 ...

str(test_sample)
tibble [300,000 x 3] (S3: tbl_df/tbl/data.frame)
 $ ID          : num [1:300000] 8876633 9868538 9210518 9279377 8707153 ...
 $ Primary_type: Factor w/ 32 levels "ARSON","ASSAULT",..: 3 7 31 7 2 8 7 2 31 18 ...
 $ Arrest      : Factor w/ 2 levels "FALSE","TRUE": 1 1 1 1 2 1 1 1 2 2 ...

# set the recipe
rec <- recipe(Arrest ~ ., data = train_sample) %>% 
  update_role(ID, new_role = "ID") %>% 
  step_novel(Primary_type)

# set the model
rf_model <- rand_forest(trees = 10) %>%
  set_engine("ranger", seed = 100, num.threads = 12, verbose = TRUE) %>%
  set_mode("classification")

# set the workflow
wf <- workflow() %>% 
  add_recipe(rec) %>% 
  add_model(rf_model)

# fit the train data
wf_fit <- wf %>% fit(train_sample)

# predict the test data
wf_pred <- wf_fit %>% predict(test_sample)

The prediction raises the following errer:

ERROR:Missing data in columns: Primary_type.

However, when I build up the workflow seperately using prep() and bake(), the prediction does not raise error:

# set the workflow seperately
train_prep <- prep(rec, training = train_sample)
train_bake <- bake(train_prep, new_data = NULL)
test_bake <- bake(train_prep, new_data = test_sample)

# fit the baked train data
rf_model_fit <- rf_model %>% fit(Arrest ~ Primary_type, train_bake)

# predict the baked test data
rf_model_pred <- rf_model_fit %>% predict(test_bake) # No missing data error

I find that the levels of Primary_type in both baked datasets are indentical, that means the step_novel() works.

# compare the levels bewteen baked data sets
identical(levels(train_bake$Primary_type), levels(test_bake$Primary_type))
[1] TRUE

So, why the prediction fails in the workflow and succeeds when do it seperately? And how the missing data generates? Thanks a lot.

Upvotes: 0

Views: 2515

Answers (1)

Julia Silge
Julia Silge

Reputation: 11613

I recommend that you check out this advice on "Ordering of Steps", especially the section on handling levels in categorical data. You should use step_novel() before other factor handling operations.

Upvotes: 6

Related Questions