R: Optimization via Random Search

mlr_optimizers_random_search {bbotk} | R Documentation

## Optimization via Random Search

### Description

`OptimizerRandomSearch` class that implements a simple Random Search.

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("random_search")
    opt("random_search")

### Parameters

`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` -> `OptimizerRandomSearch`

### Methods

#### Public methods

- `OptimizerRandomSearch$new()`

- `OptimizerRandomSearch$clone()`

Inherited methods

- `bbotk::Optimizer$format()`

- `bbotk::Optimizer$optimize()`

- `bbotk::Optimizer$print()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    OptimizerRandomSearch$new()

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    OptimizerRandomSearch$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281–305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

### Examples

    search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))

    codomain = ps(y = p_dbl(tags = "minimize"))

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

    objective = ObjectiveRFun$new(
     fun = objective_function,
     domain = domain,
     codomain = codomain)

    instance = OptimInstanceSingleCrit$new(
     objective = objective,
     search_space = search_space,
     terminator = trm("evals", n_evals = 10))

    optimizer = opt("random_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 0.5.0 Index]