Experimental designs for computer experiments are used to construct parameter grids that try to cover the parameter space such that any portion of the space has an observed combination that is not too far from it.

grid_max_entropy( x, ..., size = 3, original = TRUE, variogram_range = 0.5, iter = 1000 ) # S3 method for parameters grid_max_entropy( x, ..., size = 3, original = TRUE, variogram_range = 0.5, iter = 1000 ) # S3 method for list grid_max_entropy( x, ..., size = 3, original = TRUE, variogram_range = 0.5, iter = 1000 ) # S3 method for param grid_max_entropy( x, ..., size = 3, original = TRUE, variogram_range = 0.5, iter = 1000 ) # S3 method for workflow grid_max_entropy( x, ..., size = 3, original = TRUE, variogram_range = 0.5, iter = 1000 ) grid_latin_hypercube(x, ..., size = 3, original = TRUE) # S3 method for parameters grid_latin_hypercube(x, ..., size = 3, original = TRUE) # S3 method for list grid_latin_hypercube(x, ..., size = 3, original = TRUE) # S3 method for param grid_latin_hypercube(x, ..., size = 3, original = TRUE) # S3 method for workflow grid_latin_hypercube(x, ..., size = 3, original = TRUE)

x | A |
---|---|

... | One or more |

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

original | A logical: should the parameters be in the original units or in the transformed space (if any)? |

variogram_range | A numeric value greater than zero. Larger values reduce the likelihood of empty regions in the parameter space. |

iter | An integer for the maximum number of iterations used to find a good design. |

The types of designs supported here are latin hypercube designs and designs that attempt to maximize the determinant of the spatial correlation matrix between coordinates. Both designs use random sampling of points in the parameter space.

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:

```
cost()
```

## Cost (quantitative) ## Transformer: log-2 ## Range (transformed scale): [-10, -1]

set.seed(283) cost_grid_1 <- grid_latin_hypercube(cost(), size = 1000) range(log2(cost_grid_1$cost))

```
## [1] -9.998623 -1.000423
```

However, in some cases, the `tune`

package overrides the default ranges
for specific models. If the grid function uses a `parameters`

object
created from a model or recipe, the ranges my have different defaults
(specific to those models). Using the example above, the `cost`

argument
above is different for SVM models:

library(parsnip) library(tune) # When used in tune, the log2 range is [-10, 5] svm_mod <- svm_rbf(cost = tune()) %>% set_engine("kernlab") set.seed(283) cost_grid_2 <- grid_latin_hypercube(parameters(svm_mod), size = 1000) range(log2(cost_grid_2$cost))

```
## [1] -9.997704 4.999296
```

Sacks, Jerome & Welch, William & J. Mitchell, Toby, and Wynn, Henry. (1989). Design and analysis of computer experiments. With comments and a rejoinder by the authors. Statistical Science. 4. 10.1214/ss/1177012413.

Santner, Thomas, Williams, Brian, and Notz, William. (2003). The Design and Analysis of Computer Experiments. Springer.

Dupuy, D., Helbert, C., and Franco, J. (2015). DiceDesign and DiceEval: Two R packages for design and analysis of computer experiments. Journal of Statistical Software, 65(11)

grid_max_entropy( hidden_units(), penalty(), epochs(), activation(), learn_rate(c(0, 1), trans = scales::log_trans()), size = 10, original = FALSE)#> # A tibble: 10 x 5 #> hidden_units penalty epochs activation learn_rate #> <dbl> <dbl> <dbl> <chr> <dbl> #> 1 4.83 -8.55 988. softmax 0.678 #> 2 9.12 -8.11 786. softmax 0.887 #> 3 4.46 -2.86 122. relu 0.271 #> 4 8.10 -9.52 250. softmax 0.219 #> 5 8.81 -1.65 903. elu 0.151 #> 6 2.76 -2.67 526. elu 0.930 #> 7 6.13 -2.31 207. softmax 0.902 #> 8 3.70 -8.10 441. elu 0.332 #> 9 9.21 -6.37 853. linear 0.0388 #> 10 2.19 -5.66 542. linear 0.130#> # A tibble: 3 x 2 #> penalty mixture #> <dbl> <dbl> #> 1 0.513 0.343 #> 2 0.000423 0.215 #> 3 0.0000000230 0.690