I'm using {stacks} for the first time, and I'm hitting an error regarding the resampling object.
Error: It seems like the new candidate member 'nnet_res' doesn't make use of the same resampling object as the existing candidates.
My reproducible example comes from the docs: Classification Models With stacks • stacks. I modified the mlp model to use tune_bayes() instead of tune_grid() as in the other models. This is the model that's giving me problems. Could someone explain why the resampling object is different?
library(tidymodels)
library(tidyverse)
library(stacks)
data("tree_frogs")
# subset the data
tree_frogs <- tree_frogs %>%
dplyr::select(-c(clutch, latency))
set.seed(1)
tree_frogs_split <- initial_split(tree_frogs)
tree_frogs_train <- training(tree_frogs_split)
tree_frogs_test <- testing(tree_frogs_split)
folds <- rsample::vfold_cv(tree_frogs_train, v = 5)
tree_frogs_rec <-
recipe(reflex ~ ., data = tree_frogs_train) %>%
step_dummy(all_nominal(), -reflex) %>%
step_zv(all_predictors())
tree_frogs_wflow <-
workflow() %>%
add_recipe(tree_frogs_rec)
ctrl_grid <- control_stack_grid()
rand_forest_spec <-
rand_forest(
mtry = tune(),
min_n = tune(),
trees = 500
) %>%
set_mode("classification") %>%
set_engine("ranger")
rand_forest_wflow <-
tree_frogs_wflow %>%
add_model(rand_forest_spec)
rand_forest_res <-
tune_grid(
object = rand_forest_wflow,
resamples = folds,
grid = 10,
control = ctrl_grid
)
# from original example
# nnet_spec <-
# mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>%
# set_mode("classification") %>%
# set_engine("nnet")
#
# nnet_rec <-
# tree_frogs_rec %>%
# step_normalize(all_predictors())
#
# nnet_wflow <-
# tree_frogs_wflow %>%
# add_model(nnet_spec)
#
# nnet_res <-
# tune_grid(
# object = nnet_wflow,
# resamples = folds,
# grid = 10,
# control = ctrl_grid
# )
nnet_spec <-
mlp(hidden_units = tune(), penalty = tune(), epochs = tune()) %>%
set_mode("classification") %>%
set_engine("nnet")
nnet_rec <-
tree_frogs_rec %>%
step_normalize(all_predictors())
nnet_wflow <-
tree_frogs_wflow %>%
add_model(nnet_spec)
par_nnet <- dials::parameters(nnet_wflow)
nnet_res <-
tune_bayes(
object = nnet_wflow,
param_info = par_nnet,
resamples = folds,
control = control_bayes(verbose=TRUE,
save_pred = TRUE,
save_workflow = TRUE,
no_improve = 10,
seed = 1)
)
tree_frogs_model_st <-
# initialize the stack
stacks() %>%
# add candidate members
add_candidates(rand_forest_res) %>%
add_candidates(nnet_res) %>%
# determine how to combine their predictions
blend_predictions() %>%
# fit the candidates with nonzero stacking coefficients
fit_members()