woe.md
In embed: Extra Recipes for Encoding Predictors

woe_table do not accept different length inputs

Code
  embed:::woe_table(rep(c(0, 1), 20), rep(letters[1:4], 5))
Condition
  Error in `embed:::woe_table()`:
  ! 'outcome' must have exactly 2 categories (has 4)

woe_table accepts only outcome with 2 distinct categories

Code
  embed:::woe_table(rep(letters[1:3], 10), rep(c(0, 1, 2), 10))
Condition
  Error in `embed:::woe_table()`:
  ! 'outcome' must have exactly 2 categories (has 3)

Code
  embed:::woe_table(rep(letters[1:3], 10), rep(c(0), 30))
Condition
  Error in `embed:::woe_table()`:
  ! 'outcome' must have exactly 2 categories (has 1)

Code
  embed:::woe_table(df$x2, df$x1)
Condition
  Error in `embed:::woe_table()`:
  ! 'outcome' must have exactly 2 categories (has 3)

add_woe accepts only outcome with 2 distinct categories

Code
  dictionary(df %>% filter(y %in% "B"), "y")
Condition
  Error in `dictionary()`:
  ! 'outcome' must have exactly 2 categories (has 1)

add_woe do not accept dictionary with unexpected layout

Code
  add_woe(df, outcome = "y", x1, dictionary = iris)
Condition
  Error in `add_woe()`:
  ! column "variable" is missing in dictionary.

Code
  add_woe(df, outcome = "y", x1, dictionary = iris %>% mutate(variable = 1))
Condition
  Error in `add_woe()`:
  ! column "predictor" is missing in dictionary.

step_woe

Code
  woe_models <- prep(rec, training = credit_tr)
Condition
  Warning:
  Some columns used by `step_woe()` have categories with less than 10 values: 'Home', 'Job'

Code
  prep(rec_all_nominal, training = credit_tr, verbose = TRUE)
Output
  oper 1 step woe [training] 
Condition
  Warning:
  Some columns used by `step_woe()` have categories with less than 10 values: 'Home', 'Job'
Output
  The retained training set is ~ 0.14 Mb  in memory.

Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 13

  -- Training information 
  Training data contained 2000 data points and 186 incomplete rows.

  -- Operations 
  * WoE version against outcome Status for: Home, Marital, Records, Job | Trained

Code
  prep(rec_all_numeric, training = credit_tr)
Condition
  Error in `step_woe()`:
  Caused by error in `prep()`:
  ! All columns selected for the step should be string, factor, or ordered.

2-level factors

Code
  recipe(Species ~ ., data = iris3) %>% step_woe(group, outcome = vars(Species)) %>%
    prep()
Condition
  Error in `step_woe()`:
  Caused by error in `dictionary()`:
  ! 'outcome' must have exactly 2 categories (has 3)

empty printing

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Operations 
  * WoE version against outcome mpg for: <none>

Code
  rec
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 10

  -- Training information 
  Training data contained 32 data points and no incomplete rows.

  -- Operations 
Condition
  Warning:
  Unknown or uninitialised column: `variable`.
Message
  * WoE version against outcome mpg for: <none> | Trained

keep_original_cols - can prep recipes with it missing

Code
  rec <- prep(rec)
Condition
  Warning:
  'keep_original_cols' was added to `step_woe()` after this recipe was created.
  Regenerate your recipe to avoid this warning.

printing

Code
  print(rec)
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 13

  -- Operations 
  * WoE version against outcome Status for: Job, Home

Code
  prep(rec)
Condition
  Warning:
  Some columns used by `step_woe()` have categories with less than 10 values: 'Home', 'Job'
Message

  -- Recipe ----------------------------------------------------------------------

  -- Inputs 
  Number of variables by role
  outcome:    1
  predictor: 13

  -- Training information 
  Training data contained 4454 data points and 415 incomplete rows.

  -- Operations 
  * WoE version against outcome Status for: Job, Home | Trained