如何保存Tidymodels模型以供重用

Question

下面是使用tidymodels模型创建lightgbm工作流的代码。但是，当我试图保存到.rds对象并预测

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)

只有使用lightgbm模型的工作流似乎存在此问题。对于其他类型的模型(随机森林、xgboost、glm等)，我可以使用saveRDS()保存已安装的工作流，用readRDS()读取并使用新的数据进行预测。

对于案例2，显然底层的预测函数将更改为predict.lgb.Booster()，后者以matrix作为输入。但是我的id变量是character格式，而matrix中的所有列都必须具有相同的格式。

有没有办法将整个workflow保存起来供将来使用？

Answer 1

我想出了一种解决方案来保存lightgbm以供将来参考。它不使用tidymodel框架，而是被迫首先将其转换为lightgbm模型格式。如果您想要评估变量的重要性，情况也是如此。

根据上述守则：

# 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)

我还没有弄清楚是否可以将加载的lightgbm合并回tidymodel格式，但现在您至少可以预测、使用和评估，而不必每次都重新运行模型。希望这有帮助，如果你找到一个更清洁/更当前的解决方案，请张贴！