grid_max_entropy need better error message for unfinalized parameters

[SewerynGrodny]

Hi,
thanks for great tidymodels packages. (Great job!)
While training random forest models, I've encounter an issue with tune grid and parameters. It seems that mtry() is not supported (case 2 in below code).
There is also minor problem with show_best function which throw an error if there are NA in .metric.

Best
Sewe

Reproducible example

#tidymodels
cars_split = initial_split(mtcars)

car_recipe = recipe(mpg ~., data = training(cars_split)) %>% 
  step_center(all_numeric()) %>% 
  prep()

cars_cv_folds <- training(cars_split) %>% 
  bake(car_recipe, new_data = .) %>%
  vfold_cv(v = 5)


#case 1
# model
rf_model_cars = rand_forest(
  mode = "regression",
  min_n = tune(),
  ) %>% 
  set_engine("ranger")

#params
rf_params_cars = parameters(min_n())
rf_grid_cars = grid_max_entropy(rf_params_cars, size = 20)

# tune
rf_stage_1_cv_results_tbl_oto = tune_grid(
  formula = mpg ~.,
  model = rf_model_cars,
  resamples = cars_cv_folds,
  grid = rf_grid_cars,
  metrics = metric_set(mae, mape, rmse, rsq),
  control = control_grid(verbose = TRUE)
)
# error because of NA
rf_stage_1_cv_results_tbl_oto %>% show_best()

rf_stage_1_cv_results_tbl_oto %>% unnest(.metrics) %>% 
  filter(.metric == "rsq")

# case 2
rf_model_cars = rand_forest(
  mode = "regression",
  min_n = tune(),
  mtry = tune()
) %>% 
  set_engine("ranger")

#params
rf_params_cars = parameters(mtry(), min_n())
#error 
rf_grid_cars = grid_max_entropy(rf_params_cars, size = 20)
 

[topepo]

mtry depends on the number of columns so the upper part of the range cannot be set. The finalize() method can do this if you pass in the predictors:

library(tidymodels)
#> ── Attaching packages ────────────────────────────────────────────────────────────────────────────────── tidymodels 0.0.4 ──
#> ✓ broom     0.5.4     ✓ recipes   0.1.9
#> ✓ dials     0.0.4     ✓ rsample   0.0.5
#> ✓ dplyr     0.8.4     ✓ tibble    2.1.3
#> ✓ ggplot2   3.2.1     ✓ tune      0.0.1
#> ✓ infer     0.5.1     ✓ workflows 0.1.0
#> ✓ parsnip   0.0.5     ✓ yardstick 0.0.5
#> ✓ purrr     0.3.3
#> ── Conflicts ───────────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard()    masks scales::discard()
#> x dplyr::filter()     masks stats::filter()
#> x dplyr::lag()        masks stats::lag()
#> x ggplot2::margin()   masks dials::margin()
#> x recipes::step()     masks stats::step()
#> x recipes::yj_trans() masks scales::yj_trans()

rf_params_cars = parameters(mtry(), min_n())
rf_params_cars
#> Collection of 2 parameters for tuning
#> 
#>     id parameter type object class
#>   mtry           mtry    nparam[?]
#>  min_n          min_n    nparam[+]
#> 
#> Parameters needing finalization:
#>    # Randomly Selected Predictors ('mtry')
#> 
#> See `?dials::finalize` or `?dials::update.parameters` for more information.

rf_params_cars <- 
  rf_params_cars %>% 
  update(mtry = finalize(mtry(), mtcars %>% select(-mpg)))
rf_params_cars
#> Collection of 2 parameters for tuning
#> 
#>     id parameter type object class
#>   mtry           mtry    nparam[+]
#>  min_n          min_n    nparam[+]

set.seed(131)
rf_grid_cars = grid_max_entropy(rf_params_cars, size = 3)
rf_grid_cars
#> # A tibble: 3 x 2
#>    mtry min_n
#>   <int> <int>
#> 1     4    34
#> 2     9    21
#> 3     2    16

^{Created on 2020-02-24 by the reprex package (v0.3.0)}

We need a better error message though.

(edit - hit wrong key)

[topepo]

I'm going to move this to dials and update the title.

[topepo]

There is also minor problem with show_best function which throw an error if there are NA in .metric.

That's because the message (and entries in the .notes column) tell you that

> rf_stage_1_cv_results_tbl_oto$.notes[[5]]$.notes
[1] "internal: A correlation computation is required, but `estimate` is constant 
and has 0 standard deviation, resulting in a divide by 0 error. `NA` will be 
returned."

This happens when a model predicts the same value for all samples.

The main error in the code was the lack of metric argument:

> rf_stage_1_cv_results_tbl_oto %>% show_best()
Error in check_metric_choice(metric, maximize) : 
  argument "metric" is missing, with no default
> rf_stage_1_cv_results_tbl_oto %>% show_best(metric = "rmse", maximize = FALSE)
# A tibble: 5 x 6
  min_n .metric .estimator  mean     n std_err
  <int> <chr>   <chr>      <dbl> <int>   <dbl>
1     2 rmse    standard    2.06     5   0.292
2     5 rmse    standard    2.24     5   0.286
3     7 rmse    standard    2.49     5   0.271
4     9 rmse    standard    2.65     5   0.247
5    11 rmse    standard    2.94     5   0.204

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