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Pipeable steps for feature engineering and data preprocessing to prepare for modeling
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`step_impute_knn()` errors at bake time on character columns that work at prep time #926

Open mdsteiner opened 2 years ago

mdsteiner commented 2 years ago

The problem

When imputing missing values with step_impute_knn(all_predictors()) the error Error in gower_work(x = x, y = y, pair_x = pair_x, pair_y = pair_y, n = n, : Column 2 of x is of class character while matching column 2 of y is of class factor is thrown when calling the predict.workflows() function. The recipe seems to be applied correctly in the fitting process, but not in the predict function. A workaround is to call step_string2factor(all_nominal_predictors()) before the step_impute_knn(all_predictors()) in the recipe but given that this is not necessary in the fitting process it may be desirable to have the same behavior when calling predict.workflow().

Reproducible example

library(tibble)
library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
tidymodels_prefer()

# set up data
set.seed(42)
dat <- tibble(
  criterion = rnorm(50),
  num_pred_a = rnorm(50) + .8*criterion,
  num_pred_b = rnorm(50) + .6*criterion,
  char_pred = ifelse(criterion < .2,
                     sample(c("a", "b"), 1, prob = c(.75, .25)),
                     sample(c("a", "b"), 1, prob = c(.5, .5))))

dat[sample(1:nrow(dat), 8), 2] <- NA
dat[sample(1:nrow(dat), 8), 4] <- NA

dat_split <- initial_split(dat)
dat_train <- training(dat_split)
dat_test <- testing(dat_split)

# create recipe
lm_recipe <- 
  recipe(criterion ~ ., data = dat_train) %>% 
  step_impute_knn(all_predictors()) %>% 
  step_other(all_nominal_predictors()) %>% 
  step_dummy(all_nominal_predictors())

# set up the regression model
lm_model <- 
  linear_reg() %>% 
  set_engine("lm") %>% 
  set_mode("regression")

# lm workflow 
lm_workflow <- 
  workflow() %>% 
  add_recipe(lm_recipe) %>% 
  add_model(lm_model)

# Fit the regression model
lm_fit <-
  lm_workflow %>% 
  fit(dat_train)

# get predicted values
predict(lm_fit, new_data = dat_test)
#> Error in gower_work(x = x, y = y, pair_x = pair_x, pair_y = pair_y, n = n, : Column 2 of x is of class character while matching column 2 of y is of class factor

sessioninfo::session_info()
#> - Session info ---------------------------------------------------------------
#>  setting  value
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#>  os       Windows 10 x64 (build 19042)
#>  system   x86_64, mingw32
#>  ui       RTerm
#>  language (EN)
#>  collate  German_Switzerland.1252
#>  ctype    German_Switzerland.1252
#>  tz       Europe/Berlin
#>  date     2022-03-08
#>  pandoc   2.14.0.3 @ C:/Program Files/RStudio/bin/pandoc/ (via rmarkdown)
#> 
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Created on 2022-03-08 by the reprex package (v2.0.1)

juliasilge commented 2 years ago

Thank you for this report! 🙌 Overall in recipes we have some problems around how factors are handled such as #331, #715, and unfortunately others. We should plan to fix this problem that you reported together along with our overall factor problems.

DavisVaughan commented 2 years ago

Reproducible with only recipes, so I'm going to move it there:

library(tibble)
library(recipes)

# set up data
set.seed(42)

dat <- tibble(
  criterion = rnorm(50),
  num_pred_a = rnorm(50) + .8*criterion,
  char_pred = ifelse(
    criterion < .2,
    sample(c("a", "b"), 1, prob = c(.75, .25)),
    sample(c("a", "b"), 1, prob = c(.5, .5))
  )
)

dat[sample(1:nrow(dat), 8), 2] <- NA
dat[sample(1:nrow(dat), 8), 3] <- NA

rec <- recipe(criterion ~ ., data = dat) %>% 
  step_impute_knn(all_predictors())

rec_prepped <- prep(rec, dat)

bake(rec_prepped, dat)
#> Error in gower_work(x = x, y = y, pair_x = pair_x, pair_y = pair_y, n = n, : Column 1 of x is of class character while matching column 1 of y is of class factor

Created on 2022-03-09 by the reprex package (v2.0.1)

JosiahParry commented 1 year ago

I am running into this same issue but with step_other() using an ordered factor. I'm using step_other() on an integer (age field) that previously was accepted by step_other(). It has now been cast as an ordered to keep up with changes.

EmilHvitfeldt commented 1 year ago

@JosiahParry would you be able to produce a reprex? If this is true we might have a larger issue at hand

zlk0822 commented 2 months ago

I solved this issue by placing step_impute_knn later in the sequence of steps. Therefore, I believe the correct approach should be to perform transformations first and then imputation