smac.random_design.modulus_design¶

Classes¶

`DynamicModulusRandomDesign`([start_modulus, ...])	Interleave a random configuration, decreasing the fraction of random configurations over time.
`ModulusRandomDesign`([modulus, seed])	Interleave a random configuration after a constant number of configurations found by Bayesian optimization.

Interfaces¶

class smac.random_design.modulus_design.DynamicModulusRandomDesign(start_modulus=2.0, modulus_increment=0.3, end_modulus=inf, seed=0)[source]¶

Bases: AbstractRandomDesign

Interleave a random configuration, decreasing the fraction of random configurations over time.

Parameters:

start_modulus (float, defaults to 2.0) – Initially, every modulus-th configuration will be at random.
modulus_increment (float, defaults to 0.3) – Increase modulus by this amount in every iteration.
end_modulus (float, defaults to np.inf) – The maximum modulus ever used. If the value is reached before the optimization is over, it is not further increased. If it is not reached before the optimization is over, there will be no adjustment to make sure that the end_modulus is reached.
seed (int, defaults to 0) – Integer used to initialize the random state (not used)

check(iteration)[source]¶

Check if the next configuration should be random.

Parameters:: iteration (int) – Number of the i-th configuration evaluated in a SMBO iteration.
Returns:: Whether the next configuration should be random.
Return type:: bool

property meta: dict[str, Any]¶

Returns the meta data of the created object.

Return type:: dict[str, Any]

next_iteration()[source]¶

Indicates the beginning of the next SMBO iteration.

Return type:: None

class smac.random_design.modulus_design.ModulusRandomDesign(modulus=2.0, seed=0)[source]¶

Bases: AbstractRandomDesign

Interleave a random configuration after a constant number of configurations found by Bayesian optimization.