Asynchronous Optimization via Grid Search

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

Public methods

• OptimizerAsyncGridSearch$new()

• OptimizerAsyncGridSearch$optimize()

• OptimizerAsyncGridSearch$clone()

Inherited methods

• bbotk::Optimizer$format()
• bbotk::Optimizer$help()
• bbotk::Optimizer$print()

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Method new()

Creates a new instance of this R6 class.

Usage

OptimizerAsyncGridSearch$new()

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Method optimize()

Starts the asynchronous optimization.

Usage

OptimizerAsyncGridSearch$optimize(inst)

Arguments

inst

(OptimInstance).

Returns

data.table::data.table.

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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.