tuning `trees` in `catboost` throws `multi_predict` error

[frankiethull]

Hi all -

I am tuning the catboost model, (separate from the mtry discussion), and receive an error relating to multi_predict.

################################################################
#                       cat cross-val
################################################################
library(catboost)
library(parsnip)
library(bonsai)
library(rsample)
library(recipes)
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
#> 
#> Attaching package: 'recipes'
#> The following object is masked from 'package:stats':
#> 
#>     step
library(stacks)
#> Registered S3 method overwritten by 'butcher':
#>   method                 from    
#>   as.character.dev_topic generics
library(yardstick)
library(workflows)
library(tune)
# taken from stacks --------------------------------
# https://stacks.tidymodels.org/articles/basics.html
data("tree_frogs")

# subset the data
tree_frogs <- tree_frogs |>
  filter(!is.na(latency)) |>
  select(-c(clutch, hatched))
# some setup: resampling and a basic recipe
set.seed(1)
tree_frogs_split <- initial_split(tree_frogs)
tree_frogs_train <- training(tree_frogs_split)
tree_frogs_test  <- testing(tree_frogs_split)

set.seed(1)
folds <- rsample::vfold_cv(tree_frogs_train, v = 5)

tree_frogs_rec <-
  recipe(latency ~ ., data = tree_frogs_train)

metric <- metric_set(rmse)

ctrl_grid <- control_stack_grid()
ctrl_res <- control_stack_resamples()


### ~~
### change the knn to catboost, test mtry tuning:
cat_spec <-
  boost_tree(
    mode = "regression",
    learn_rate = tune(),
    trees = tune()
  ) |>
  set_engine("catboost")

# extend the recipe
#
tree_frogs_rec <-
 recipe(latency ~ ., data = tree_frogs_train)

cat_rec <-
  tree_frogs_rec |>
  step_dummy(all_nominal_predictors()) |>
  step_zv(all_predictors()) |>
  step_impute_mean(all_numeric_predictors()) |>
  step_normalize(all_numeric_predictors())

#cat_rec

cat_wflow <-
  workflow() |>
  add_model(cat_spec) |>
  add_recipe(cat_rec)

# create my own grid
tuning_grid <- expand.grid(
  learn_rate = c(.99, .01, .004),
  trees = c(3, 4, 100, 200)
)

# tune eta, iterations and fit to the 5-fold cv
set.seed(2020)
cat_res <-
  tune_grid(
    cat_wflow,
    resamples = folds,
    metrics = metric,
    grid = tuning_grid,
    control = ctrl_grid
  )
#> → A | error:   No `multi_predict()` method exists for objects with classes
#>                <_catboost.Model/model_fit>.
#> There were issues with some computations   A: x1
#> There were issues with some computations   A: x15
#> 
#> Warning: All models failed. Run `show_notes(.Last.tune.result)` for more
#> information.

tune::show_best(cat_res)
#> Warning in tune::show_best(cat_res): No value of `metric` was given; "rmse"
#> will be used.
#> Error in `estimate_tune_results()`:
#> ! All models failed. Run `show_notes(.Last.tune.result)` for more information.

^{Created on 2025-08-04 with reprex v2.1.1}

[zecojls]

I'm also getting this error, wonder if there is any workaround?