missing decision types #28

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1. krzyzinskim commented on Sep 27, 2023

   

   krzyzinskim

   on Sep 27, 2023

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   Missing values are expected in this column as they occur for every leaf node, so it is unlikely that this is the cause.

   However, I wasn't able to reproduce this error using tidymodels framework. But please note that an object of class lgb.Booster must be provided to the lightgbm.unify function (this can be extracted with the extract_fit_engine() function, see here). If this is not the solution, please provide a reproducible example for such an error.
2. cgoo4 commented on Oct 1, 2024

   

   cgoo4

   on Oct 1, 2024

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   I get this error too and have been able to reproduce it with a toy example.

   If the step_dummy() line is uncommented, then it works.

   lightgbm does though support categorical data without the need to dummy these variables. This introduces the decision type == where a categorical variable equals a specific value. This may be seen in the object lgb_trees which has a column showing the decision_type used after fitting the model, e.g. for the variable neighbourhood.

   library(bonsai)
   library(treeshap)
   library(tidymodels)
   library(shapviz)
   library(jsonlite)
   
   set.seed(123)
   split <- initial_split(ames, prop = 0.8)
   train <- training(split)
   test <- testing(split)
   
   recipe <- recipe(train) |> 
     update_role(Sale_Price, new_role = "outcome") |> 
     update_role(-has_role("outcome"), new_role = "predictor") |> 
     # step_dummy(all_nominal_predictors()) |> 
     step_zv(all_predictors()) 
   
   spec <- 
     boost_tree(trees = 100, tree_depth = 6) |> 
     set_engine("lightgbm") |> 
     set_mode("regression")
   
   fit <- workflow() |> 
     add_recipe(recipe) |> 
     add_model(spec) |> 
     fit(data = train)
   
   lgb_trees <- lightgbm::lgb.model.dt.tree(extract_fit_engine(fit))
   
   data <- recipe |>
     prep() |> 
     bake(train |> slice_sample(n = 100), has_role("predictor"))
   
   x <- recipe |>
     prep() |>
     bake(test, has_role("predictor"))
   
   shap <- extract_fit_engine(fit) |> 
     unify(data, type = "numeric") 
   #> Error in ifelse(decision_type %in% c(">=", ">"), ret.second(split_index), : Unknown decision_type

   ^{Created on 2024-10-01 with reprex v2.1.1}
3. ck37 commented on Nov 21, 2024

   

   ck37

   on Nov 21, 2024

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   I also ran into this - had to convert factors to indicators and it was resolved.

   👍React with 👍1Reacted by Michael Mayer
4. mayer79 commented on Apr 24, 2026

   

   mayer79

   on Apr 24, 2026

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   Let's use @ck37 workaround, thanks.
5. mayer79

   closed this as completedon Apr 24, 2026