Unused arguement error while tuning

[TheMadHatter666]

The problem

I cannot seem to tune with H20, I keep getting an "unused argument" error. I was using an example that used keras (which works fine) and I just switched the engine from keras to h2o and thought it should also work. But it didn't.

To track it down, I decided to run the code from https://agua.tidymodels.org/articles/tune.html

which is given below:

copied R code

library(tidymodels)
library(agua)
library(ggplot2)
theme_set(theme_bw())
doParallel::registerDoParallel()
h2o_start()
data(ames)

set.seed(4595)
data_split <- ames %>%
  mutate(Sale_Price = log10(Sale_Price)) %>%
  initial_split(strata = Sale_Price)
ames_train <- training(data_split)
ames_test <- testing(data_split)
cv_splits <- vfold_cv(ames_train, v = 10, strata = Sale_Price)

ames_rec <-
  recipe(Sale_Price ~ Gr_Liv_Area + Longitude + Latitude, data = ames_train) %>%
  step_log(Gr_Liv_Area, base = 10) %>%
  step_ns(Longitude, deg_free = tune("long df")) %>%
  step_ns(Latitude, deg_free = tune("lat df"))

lm_mod <- linear_reg(penalty = tune()) %>%
  set_engine("h2o")

lm_wflow <- workflow() %>%
  add_model(lm_mod) %>%
  add_recipe(ames_rec)

grid <- lm_wflow %>%
  extract_parameter_set_dials() %>%
  grid_regular(levels = 5)

ames_res <- tune_grid(
  lm_wflow,
  resamples = cv_splits,
  grid = grid,
  control = control_grid(save_pred = TRUE,
    backend_options = agua_backend_options(parallelism = 5))
)

ames_res

The output is :

Tuning results

10-fold cross-validation using stratification

There were issues with some computations:

• Error(s) x10: Error in fn(...): unused arguments (metrics_info = list(c("rmse", "rsq"), c("minimiz...

Run show_notes(.Last.tune.result) for more information.

Follow up on the suggestion

If I run show_notes …, this is the output:

unique notes: ─────────────────────────────────────────────────────────────────────────────────────────────────────── Error in fn(...): unused arguments (metrics_info = list(c("rmse", "rsq"), c("minimize", "maximize"), c("numeric", "numeric")), list(c("penalty", "deg_free", "deg_free"), c("penalty", "long df", "lat df"), c("model_spec", "recipe", "recipe"), c("linear_reg", "step_ns", "step_ns"), c("main", "ns_PCP7q", "ns_33KwK"), list(list("double", list(-10, 0), c(TRUE, TRUE), list("log-10", function (x) log(x, base), function (x) base^x, function (x, n = n_default) { raw_rng <- suppressWarnings(range(x, na.rm = TRUE)) if (any(!is.finite(raw_rng))) { return(numeric()) } rng <- log(raw_rng, base = base) min <- floor(rng[1]) max <- ceiling(rng[2]) if (max == min) { return(base^min) } by <- floor((max - min)/n) + 1 breaks <- base^seq(min, max, by = by) relevant_breaks <- base^rng[1] <= breaks & breaks <= base^rng[2] if (sum(relevant_breaks) >= (n - 2)) { return(breaks) } while (by > 1) { by <- by - 1 breaks <- base^seq(min, max, by = by)