grid_regular {dials}

Create grids of tuning parameters

Description

Random and regular grids can be created for any number of parameter objects.

Usage

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

## S3 method for class 'parameters'
grid_regular(x, ..., levels = 3, original = TRUE, filter = NULL)

## S3 method for class 'list'
grid_regular(x, ..., levels = 3, original = TRUE, filter = NULL)

## S3 method for class 'param'
grid_regular(x, ..., levels = 3, original = TRUE, filter = NULL)

## S3 method for class 'workflow'
grid_regular(x, ..., levels = 3, original = TRUE, filter = NULL)

grid_random(x, ..., size = 5, original = TRUE, filter = NULL)

## S3 method for class 'parameters'
grid_random(x, ..., size = 5, original = TRUE, filter = NULL)

## S3 method for class 'list'
grid_random(x, ..., size = 5, original = TRUE, filter = NULL)

## S3 method for class 'param'
grid_random(x, ..., size = 5, original = TRUE, filter = NULL)

## S3 method for class 'workflow'
grid_random(x, ..., size = 5, 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.

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.

size

A single integer for the total number of parameter value combinations returned for the random grid. If duplicate combinations are generated from this size, the smaller, unique set is returned.

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

Value

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

Examples

# filter arg will allow you to filter subsequent grid data frame based on some condition.
p <- parameters(penalty(), mixture())
grid_regular(p)
grid_regular(p, filter = penalty <= .01)

# Will fail due to unknowns:
# grid_regular(mtry(), min_n())

grid_regular(penalty(), mixture())
grid_regular(penalty(), mixture(), levels = 3:4)
grid_regular(penalty(), mixture(), levels = c(mixture = 4, penalty = 3))
grid_random(penalty(), mixture())