grid_hybrid: Hybrid space-filling/regular parameter grids

In dials: Tools for Creating Tuning Parameter Values

Hybrid space-filling/regular parameter grids

Description

A grid can be created that is efficient for some parameter(s) but fine-grained for others. This can be especially helpful when a model has a "submodel" parameter that can be tuned with virtually no cost.

Usage

grid_hybrid(
  x,
  ...,
  sfd_size = 10,
  sfd_type = "any",
  reg_parameters = NULL,
  reg_levels = 20,
  original = TRUE
)

## Default S3 method:
grid_hybrid(
  x,
  ...,
  sfd_size = 10,
  sfd_type = "any",
  reg_parameters = NULL,
  reg_levels = 20,
  original = TRUE
)

## S3 method for class 'parameters'
grid_hybrid(
  x,
  ...,
  sfd_size = 10,
  sfd_type = "any",
  reg_parameters = NULL,
  reg_levels = 20,
  original = TRUE
)

## S3 method for class 'list'
grid_hybrid(
  x,
  ...,
  sfd_size = 10,
  sfd_type = "any",
  reg_parameters = NULL,
  reg_levels = 20,
  original = TRUE
)

## S3 method for class 'param'
grid_hybrid(
  x,
  ...,
  sfd_size = 10,
  sfd_type = "any",
  reg_parameters = NULL,
  reg_levels = 20,
  original = TRUE
)

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.
sfd_size	An integer for the maximum size of the space-filling portion of the design.
sfd_type	The type of space-filling design. See the type argument of grid_space_filling() for possible values.
reg_parameters	<tidy-select> The parameter(s) used to make the regular portion of the grid, selected by their identifiers. If no parameters are selected, a space-filling design with sfd_size candidates is created. If all parameters are selected, a regular grid with reg_levels^p candidates is created where p is the number of selected parameters.
reg_levels	The number of values for each regular grid parameter.
original	A logical: should the parameters be in the original units or in the transformed space (if any)?

Details

This function first creates a space-filling design for the parameters that are not selected by reg_parameters (with sfd_size total candidates). Then a regular grid is created with reg_levels^p candidates where p is the number of parameters selected by reg_parameters. These two grids are crossed to produce the end result.

Examples

if (rlang::is_installed("ggplot2")) {

  library(ggplot2)

  # Most boosting methods can make many predictions across a number of trees
  # from a single model fit object. Those are nearly free (computationally) so
  # we would do many tree values for each of the other parameters.

  # To illustrate, we'll only show four tree values:

  boost_example <-
    parameters(list(trees = trees(), learn_rate = learn_rate(), min_n = min_n()))

  boost_example |>
    grid_hybrid(reg_parameters = trees, sfd_size = 20, reg_levels = 4) |>
    ggplot(aes(learn_rate, min_n)) +
    geom_point() +
    facet_wrap(~ trees, labeller = "label_both") +
    scale_x_log10()

  # In other cases, we have 1+ parameters with very few values. We can make a
  # small regular grid over these and a much larger space-filling design

  nnet_example <-
    parameters(
      list(
        learn_rate = learn_rate(),
        dropout = dropout(),
        # Only a few values:
        activation = activation(c("relu", "tanh", "elu")),
        schedule = rate_schedule(c("none", "cyclic", "step")) # note different name
      )
    )

  nnet_example |>
    grid_hybrid(
      reg_parameters = c(activation, schedule),
      sfd_size = 20,
      reg_levels = 3
    ) |>
    ggplot(aes(learn_rate, dropout)) +
    geom_point() +
    facet_grid(activation ~ schedule, labeller = "label_both") +
    scale_x_log10()
}

dials documentation built on Nov. 11, 2026, 5:06 p.m.