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OptimizerAsyncGridSearch class that implements a 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.

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.

Dictionary

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

mlr_optimizers$get("async_grid_search")
opt("async_grid_search")

Parameters

batch_size

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

Super classes

bbotk::Optimizer -> bbotk::OptimizerAsync -> OptimizerAsyncGridSearch

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.


Method optimize()

Starts the asynchronous optimization.

Usage

OptimizerAsyncGridSearch$optimize(inst)

Arguments

inst

(OptimInstance).


Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerAsyncGridSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.