grid_regular {dials} | R Documentation |

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

```
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)
```

`x` |
A |

`...` |
One or more |

`levels` |
An integer for the number of values of each parameter to use
to make the regular grid. |

`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. |

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

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

```
# 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())
```

[Package *dials* version 1.2.0 Index]