R: Optimization via Grid Search

mlr_optimizers_grid_search {bbotk} | R Documentation

## Optimization via Grid Search

### Description

`OptimizerGridSearch` class that implements grid search. The grid is constructed as a Cartesian product over discretized values per parameter, see `paradox::generate_design_grid()`. The points of the grid are evaluated in a random order.

In order to support general termination criteria and parallelization, we evaluate points in a batch-fashion of size `batch_size`. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria.

### Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function `opt()`:

    mlr_optimizers$get("grid_search")
    opt("grid_search")

### Parameters

`resolution`

  `integer(1)`
  Resolution of the grid, see `paradox::generate_design_grid()`.

`param_resolutions`

  named `integer()`
  Resolution per parameter, named by parameter ID, see `paradox::generate_design_grid()`.

`batch_size`

  `integer(1)`
  Maximum number of points to try in a batch.

### Progress Bars

`$optimize()` supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in `progressr::with_progress()` to enable them. We recommend to use package progress as backend; enable with `progressr::handlers("progress")`.

### Super class

`bbotk::Optimizer` -> `OptimizerGridSearch`

### Methods

#### Public methods

- `OptimizerGridSearch$new()`

- `OptimizerGridSearch$clone()`

Inherited methods

- `bbotk::Optimizer$format()`

- `bbotk::Optimizer$optimize()`

- `bbotk::Optimizer$print()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    OptimizerGridSearch$new()

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    OptimizerGridSearch$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### Examples

    library(paradox)

    domain = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))

    search_space = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))

    codomain = ParamSet$new(list(ParamDbl$new("y", tags = "minimize")))

    objective_function = function(xs) {
      list(y = as.numeric(xs)^2)
    }

    objective = ObjectiveRFun$new(fun = objective_function,
                                  domain = domain,
                                  codomain = codomain)
    terminator = trm("evals", n_evals = 10)
    instance = OptimInstanceSingleCrit$new(
     objective = objective,
     search_space = search_space,
     terminator = terminator)

    optimizer = opt("grid_search")

    # Modifies the instance by reference
    optimizer$optimize(instance)

    # Returns best scoring evaluation
    instance$result

    # Allows access of data.table of full path of all evaluations
    as.data.table(instance$archive$data)

[Package bbotk version 0.3.1 Index]