How to save Tidymodels Lightgbm model for reuse

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I have the following codes for creating a tidymodels workflow with lightgbm model. However, there is some problem when I tried to save into a .rds object and predict

library(AmesHousing)
library(treesnip)
library(lightgbm)
library(tidymodels)
tidymodels_prefer()

### Model ###

# data
data <- make_ames() %>%
  janitor::clean_names()

data <- subset(data, select = c(sale_price, bedroom_abv_gr, bsmt_full_bath, bsmt_half_bath, enclosed_porch, fireplaces,
                                full_bath, half_bath, kitchen_abv_gr, garage_area, garage_cars, gr_liv_area, lot_area,
                                lot_frontage, year_built, year_remod_add, year_sold))

data$id <- c(1:nrow(data))

data <- data %>%
  mutate(id = as.character(id)) %>%
  select(id, everything())

# model specification

lgbm_model <- boost_tree(
  mtry = 7,
  trees = 347,
  min_n = 10,
  tree_depth = 12,
  learn_rate = 0.0106430579211173,
  loss_reduction = 0.000337948798058139,
) %>%
  set_mode("regression") %>%
  set_engine("lightgbm", objective = "regression")

# recipe and workflow

lgbm_recipe <- recipe(sale_price ~., data = data) %>%
  update_role(id, new_role = "ID") %>%
  step_corr(all_predictors(), threshold = 0.7) %>%
  prep()

lgbm_workflow <- workflow() %>% 
  add_recipe(lgbm_recipe) %>%
  add_model(lgbm_model)  
  
# fit workflow

fit_lgbm_workflow <- lgbm_workflow %>%
  fit(data = data)

# predict

data_predict <- subset(data, select = -c(sale_price))
predict(fit_lgbm_workflow, new_data = data_predict)


### CASE 1: Save the workflow with SaveRDS()

saveRDS(object = fit_lgbm_workflow, file = "lgbm_workflow.rds")
new_lgbm_workflow <- readRDS(file = "lgbm_workflow.rds")

# Predict - error: Attempting to use a Booster which no longer exists

predict(new_lgbm_workflow, new_data = data_predict)



### CASE 2: Save the workflow and the fitted model separately

fitted_model <- (fit_lgbm_workflow %>% extract_fit_parsnip())$fit
saveRDS(object = fit_lgbm_workflow, file = "lgbm_workflow.rds")
lightgbm::saveRDS.lgb.Booster(object = fitted_model, file = "lgbm_model.rds")


new_lgbm_workflow <- readRDS(file = "lgbm_workflow.rds")
new_lgbm_model <- lightgbm::readRDS.lgb.Booster(file = "lgbm_model.rds")
new_lgbm_workflow$fit$fit <- new_lgbm_model


# Predict - error: cannot predict on data of class ‘tbl_df’‘tbl’‘data.frame’

predict(new_lgbm_workflow, new_data = data_predict)

Only workflows with lightgbm model seem to have this problem. For other types of models (random forest, xgboost, glm, etc), I can save the fitted workflow with saveRDS(), read with readRDS(), and predict using new data just fine

For Case 2, apparently the underlying predict function will be changed to predict.lgb.Booster(), which take a matrix as input. But my id variable has character format whereas all columns in a matrix must have the same format

Is there a way to save the entire workflow for future use?

lightgbm tidymodels prediction r save
2 Answers

I figured out a solution to saving out lightgbm for future reference. It doesn't use the tidymodel framework, but instead you are forced to covert it into lightgbm model format first. The same is true if you want to evaluate variable importance.

Based on the above code:

# Convert to lightgbm booster model
lgb_model <- parsnip::extract_fit_engine(fit_lgbm_workflow) 

# If you want you can now evaluate variable importance. 
# Tidymodels does not support variable importance of lgb via bonsai currently

loss_varimp <- lgb_model %>%
    lgb.importance(.) 

# Save the booster out
lightgbm::lgb.save(lgb_model, filename_x)

# Read the booster in
lightgbm::lgb.load(filename_x)

I haven't figured out whether you can merge the loaded lightgbm back into a tidymodel format, but now you can at least predict, use, and evaluate without having to re-run the model each time. Hope this helps and please post if you found a cleaner/more current solution!

user_53294270

After much digging, I found the solution in this closed issue.

library(tidymodels)
#> Warning: package 'tidymodels' was built under R version 4.2.1
#> Warning: package 'broom' was built under R version 4.2.1
#> Warning: package 'scales' was built under R version 4.2.1
#> Warning: package 'infer' was built under R version 4.2.1
#> Warning: package 'modeldata' was built under R version 4.2.1
#> Warning: package 'parsnip' was built under R version 4.2.1
#> Warning: package 'rsample' was built under R version 4.2.1
#> Warning: package 'tibble' was built under R version 4.2.1
#> Warning: package 'workflows' was built under R version 4.2.1
#> Warning: package 'workflowsets' was built under R version 4.2.1
library(bonsai)
library(lightgbm)
#> Warning: package 'lightgbm' was built under R version 4.2.1
#> Loading required package: R6
#> 
#> Attaching package: 'lightgbm'
#> The following object is masked from 'package:dplyr':
#> 
#>     slice

# data

data <- modeldata::ames %>%
  janitor::clean_names()

data <- subset(data, select = c(sale_price, bedroom_abv_gr, bsmt_full_bath, bsmt_half_bath, enclosed_porch, fireplaces,
                                full_bath, half_bath, kitchen_abv_gr, garage_area, garage_cars, gr_liv_area, lot_area,
                                lot_frontage, year_built, year_remod_add, year_sold))

data$id <- c(1:nrow(data))

data <- data %>%
  mutate(id = as.character(id)) %>%
  select(id, everything())

# model specification

lgbm_model <- boost_tree(
  mtry = 7,
  trees = 347,
  min_n = 10,
  tree_depth = 12,
  learn_rate = 0.0106430579211173,
  loss_reduction = 0.000337948798058139,
) %>%
  set_mode("regression") %>%
  set_engine("lightgbm", objective = "regression")

# recipe and workflow

lgbm_recipe <- recipe(sale_price ~., data = data) %>%
  update_role(id, new_role = "ID") %>%
  step_corr(all_predictors(), threshold = 0.7)

lgbm_workflow <- workflow(preprocessor = lgbm_recipe,
                          spec = lgbm_model)

# fit workflow

fit_lgbm_workflow <- lgbm_workflow %>%
  fit(data = data)

# predict

data_predict <- subset(data, select = -c(sale_price))
predict(fit_lgbm_workflow, new_data = data_predict)
#> # A tibble: 2,930 × 1
#>      .pred
#>      <dbl>
#>  1 201911.
#>  2 124695.
#>  3 138983.
#>  4 221095.
#>  5 198972.
#>  6 188613.
#>  7 198730.
#>  8 170893.
#>  9 243899.
#> 10 196875.
#> # … with 2,920 more rows

# save the trained workflow and lgb.booster object separately

saveRDS(fit_lgbm_workflow, "lgbm_wflw.rds")
saveRDS.lgb.Booster(extract_fit_engine(fit_lgbm_workflow), "lgbm_booster.rds")

# load trained workflow and merge it with lgb.booster

new_lgbm_wflow <- readRDS("lgbm_wflw.rds")
new_lgbm_wflow$fit$fit$fit <- readRDS.lgb.Booster("lgbm_booster.rds")

predict(new_lgbm_wflow, data_predict)
#> # A tibble: 2,930 × 1
#>      .pred
#>      <dbl>
#>  1 201911.
#>  2 124695.
#>  3 138983.
#>  4 221095.
#>  5 198972.
#>  6 188613.
#>  7 198730.
#>  8 170893.
#>  9 243899.
#> 10 196875.
#> # … with 2,920 more rows

Created on 2022-09-07 with reprex v2.0.2

In my reprex above, I've used a workflow to fit. If you're using a parsnip object to fit, use this approach instead:


saveRDS(bonsai_fit, path1)
saveRDS.lgb.Booster(extract_fit_engine(bonsai_fit), path2)
bonsai_fit_read <- readRDS(path1)
bonsai_fit_engine_read <- readRDS.lgb.Booster(path2)
bonsai_fit_read$fit <- bonsai_fit_engine_read

Refer to this comment for more details.

The silver lining is:

Just want to add to this conversation that since December 2021, {lightgbm}'s development version has supported using readsRDS() / saveRDS() directly for {lightgbm} models

user_114853700
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