Create a biregular grid around a center point

Biregular grids can be created for any number of parameter objects.

grid_biregular(
  x,
  ...,
  center = NULL,
  levels = 3,
  original = TRUE,
  filter = NULL
)

Arguments

x: A param object, list, or parameters.
...: One or more param objects (such as mtry() or penalty()). None of the objects can have unknown() values in the parameter ranges or values.
center: A numeric vector specifying the point onto which the biregular grid should be centered. Defaults to NULL, in which case grid_regular is used instead.
levels: An integer for the number of values of each parameter to use to make the regular grid. levels can be a single integer or a vector of integers that is the same length as the number of parameters in .... levels can be a named integer vector, with names that match the id values of parameters.
original: A logical: should the parameters be in the original units or in the transformed space (if any)?
filter: A logical: should the parameters be filtered prior to generating the grid. Must be a single expression referencing parameter names that evaluates to a logical vector.

Value

A tibble. There are columns for each parameter and a row for every parameter combination.

Details

Note that there may a difference in grids depending on how the function is called. If the call uses the parameter objects directly the possible ranges come from the objects in dials. For example:

mixture()

## Proportion of Lasso Penalty (quantitative)
## Range: [0, 1]

set.seed(283)
mix_grid_1 <- grid_random(mixture(), size = 1000)
range(mix_grid_1$mixture)

## [1] 0.001490161 0.999741096

However, in some cases, the parsnip and recipe packages overrides the default ranges for specific models and preprocessing steps. If the grid function uses a parameters object created from a model or recipe, the ranges may have different defaults (specific to those models). Using the example above, the mixture argument above is different for glmnet models:

library(parsnip)
library(tune)

# When used with glmnet, the range is [0.05, 1.00]
glmn_mod <-
  linear_reg(mixture = tune()) %>%
  set_engine("glmnet")

set.seed(283)
mix_grid_2 <- grid_random(extract_parameter_set_dials(glmn_mod), size = 1000)
range(mix_grid_2$mixture)

## [1] 0.05141565 0.99975404

Examples

grid_biregular(dials::mixture(), center = 0.2)
#> # A tibble: 3 × 1
#>   mixture
#>     <dbl>
#> 1     0  
#> 2     0.2
#> 3     1