Create grids of tuning parameters — grid

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

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

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

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

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

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

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

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

# S3 method for 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.

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

# filter arg will allow you to filter subsequent grid data frame based on some condition.
p <- parameters(penalty(), mixture())
grid_regular(p)
#> # A tibble: 9 × 2
#>        penalty mixture
#>          <dbl>   <dbl>
#> 1 0.0000000001     0  
#> 2 0.00001          0  
#> 3 1                0  
#> 4 0.0000000001     0.5
#> 5 0.00001          0.5
#> 6 1                0.5
#> 7 0.0000000001     1  
#> 8 0.00001          1  
#> 9 1                1  
grid_regular(p, filter = penalty <= .01)
#> # A tibble: 6 × 2
#>        penalty mixture
#>          <dbl>   <dbl>
#> 1 0.0000000001     0  
#> 2 0.00001          0  
#> 3 0.0000000001     0.5
#> 4 0.00001          0.5
#> 5 0.0000000001     1  
#> 6 0.00001          1  

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

grid_regular(penalty(), mixture())
#> # A tibble: 9 × 2
#>        penalty mixture
#>          <dbl>   <dbl>
#> 1 0.0000000001     0  
#> 2 0.00001          0  
#> 3 1                0  
#> 4 0.0000000001     0.5
#> 5 0.00001          0.5
#> 6 1                0.5
#> 7 0.0000000001     1  
#> 8 0.00001          1  
#> 9 1                1  
grid_regular(penalty(), mixture(), levels = 3:4)
#> # A tibble: 12 × 2
#>         penalty mixture
#>           <dbl>   <dbl>
#>  1 0.0000000001   0    
#>  2 0.00001        0    
#>  3 1              0    
#>  4 0.0000000001   0.333
#>  5 0.00001        0.333
#>  6 1              0.333
#>  7 0.0000000001   0.667
#>  8 0.00001        0.667
#>  9 1              0.667
#> 10 0.0000000001   1    
#> 11 0.00001        1    
#> 12 1              1    
grid_regular(penalty(), mixture(), levels = c(mixture = 4, penalty = 3))
#> # A tibble: 12 × 2
#>         penalty mixture
#>           <dbl>   <dbl>
#>  1 0.0000000001   0    
#>  2 0.00001        0    
#>  3 1              0    
#>  4 0.0000000001   0.333
#>  5 0.00001        0.333
#>  6 1              0.333
#>  7 0.0000000001   0.667
#>  8 0.00001        0.667
#>  9 1              0.667
#> 10 0.0000000001   1    
#> 11 0.00001        1    
#> 12 1              1    
grid_random(penalty(), mixture())
#> # A tibble: 5 × 2
#>         penalty mixture
#>           <dbl>   <dbl>
#> 1 0.00000000433   0.312
#> 2 0.0000000435    0.174
#> 3 0.00359         0.423
#> 4 0.00000299      0.192
#> 5 0.00000146      0.633