smac.optimizer.random_configuration_chooser module

class smac.optimizer.random_configuration_chooser.ChooserCosineAnnealing(rng: numpy.random.mtrand.RandomState, prob_max: float, prob_min: float, restart_iteration: int)

Bases: smac.optimizer.random_configuration_chooser.RandomConfigurationChooser

Interleave a random configuration according to a given probability which is decreased according to a cosine annealing schedule.

Parameters
  • prob_max (float) – Initial probility of a random configuration

  • prob_min (float) – Lowest probility of a random configuration

  • restart_iteration (int) – Restart the annealing schedule every restart_iteration iterations.

  • rng (np.random.RandomState) – Random state

_abc_impl = <_abc._abc_data object>
check(iteration: int) bool

Check if the next configuration should be at random

next_smbo_iteration() None

Indicate beginning of next SMBO iteration

class smac.optimizer.random_configuration_chooser.ChooserLinearCoolDown(rng: numpy.random.mtrand.RandomState, start_modulus: float = 2.0, modulus_increment: float = 0.3, end_modulus: float = inf)

Bases: smac.optimizer.random_configuration_chooser.RandomConfigurationChooser

_abc_impl = <_abc._abc_data object>
check(iteration: int) bool

Check if the next configuration should be at random

next_smbo_iteration() None

Indicate beginning of next SMBO iteration

class smac.optimizer.random_configuration_chooser.ChooserNoCoolDown(rng: numpy.random.mtrand.RandomState, modulus: float = 2.0)

Bases: smac.optimizer.random_configuration_chooser.RandomConfigurationChooser

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

Parameters

modulus (float) – Every modulus-th configuration will be at random.

_abc_impl = <_abc._abc_data object>
check(iteration: int) bool

Check if the next configuration should be at random

next_smbo_iteration() None

Indicate beginning of next SMBO iteration

class smac.optimizer.random_configuration_chooser.ChooserProb(rng: numpy.random.mtrand.RandomState, prob: float)

Bases: smac.optimizer.random_configuration_chooser.RandomConfigurationChooser

_abc_impl = <_abc._abc_data object>
check(iteration: int) bool

Check if the next configuration should be at random

next_smbo_iteration() None

Indicate beginning of next SMBO iteration

class smac.optimizer.random_configuration_chooser.ChooserProbCoolDown(rng: numpy.random.mtrand.RandomState, prob: float, cool_down_fac: float)

Bases: smac.optimizer.random_configuration_chooser.RandomConfigurationChooser

_abc_impl = <_abc._abc_data object>
check(iteration: int) bool

Check if the next configuration should be at random

next_smbo_iteration() None

Indicate beginning of next SMBO iteration

class smac.optimizer.random_configuration_chooser.RandomConfigurationChooser(rng: numpy.random.mtrand.RandomState)

Bases: abc.ABC

Abstract base of helper classes to configure interleaving of random configurations in a list of challengers.

_abc_impl = <_abc._abc_data object>
abstract check(iteration: int) bool

Check if the next configuration should be at random

abstract next_smbo_iteration() None

Indicate beginning of next SMBO iteration