from __future__ import annotations
import numpy as np
from ConfigSpace import Configuration
from smac.acquisition.function.abstract_acquisition_function import (
AbstractAcquisitionFunction,
)
from smac.acquisition.maximizer.random_search import RandomSearch
from smac.facade.abstract_facade import AbstractFacade
from smac.initial_design.default_design import DefaultInitialDesign
from smac.intensifier.intensifier import Intensifier
from smac.model.random_model import RandomModel
from smac.multi_objective.aggregation_strategy import MeanAggregationStrategy
from smac.random_design import AbstractRandomDesign
from smac.runhistory.encoder.encoder import RunHistoryEncoder
from smac.scenario import Scenario
__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"
[docs]class RandomFacade(AbstractFacade):
"""
Facade to use Random Online Aggressive Racing (ROAR).
*Aggressive Racing:*
When we have a new configuration θ, we want to compare it to the current best
configuration, the incumbent θ*. ROAR uses the 'racing' approach, where we run few times for unpromising θ and many
times for promising configurations. Once we are confident enough that θ is better than θ*, we update the
incumbent θ* ⟵ θ. `Aggressive` means rejecting low-performing configurations very early, often after a single run.
This together is called `aggressive racing`.
*ROAR Loop:*
The main ROAR loop looks as follows:
1. Select a configuration θ uniformly at random.
2. Compare θ to incumbent θ* online (one θ at a time):
- Reject/accept θ with `aggressive racing`
*Setup:*
Uses a random model and random search for the optimization of the acquisition function.
Note
----
The surrogate model and the acquisition function is not used during the optimization and therefore replaced
by dummies.
"""
[docs] @staticmethod
def get_acquisition_function(scenario: Scenario) -> AbstractAcquisitionFunction:
"""The random facade is not using an acquisition function. Therefore, we simply return a dummy function."""
class DummyAcquisitionFunction(AbstractAcquisitionFunction):
def _compute(self, X: np.ndarray) -> np.ndarray:
return X
return DummyAcquisitionFunction()
[docs] @staticmethod
def get_intensifier(
scenario: Scenario,
*,
max_config_calls: int = 3,
max_incumbents: int = 10,
) -> Intensifier:
"""Returns ``Intensifier`` as intensifier.
Note
----
Please use the ``HyperbandFacade`` if you want to incorporate budgets.
Warning
-------
If you are in an algorithm configuration setting, consider increasing ``max_config_calls``.
Parameters
----------
max_config_calls : int, defaults to 3
Maximum number of configuration evaluations. Basically, how many instance-seed keys should be max evaluated
for a configuration.
max_incumbents : int, defaults to 10
How many incumbents to keep track of in the case of multi-objective.
"""
return Intensifier(
scenario=scenario,
max_config_calls=max_config_calls,
max_incumbents=max_incumbents,
)
[docs] @staticmethod
def get_initial_design(
scenario: Scenario,
*,
additional_configs: list[Configuration] = [],
) -> DefaultInitialDesign:
"""Returns an initial design, which returns the default configuration.
Parameters
----------
additional_configs: list[Configuration], defaults to []
Adds additional configurations to the initial design.
"""
return DefaultInitialDesign(
scenario=scenario,
additional_configs=additional_configs,
)
[docs] @staticmethod
def get_random_design(scenario: Scenario) -> AbstractRandomDesign:
"""Just like the acquisition function, we do not use a random design. Therefore, we return a dummy design."""
class DummyRandomDesign(AbstractRandomDesign):
def check(self, iteration: int) -> bool:
return True
return DummyRandomDesign()
[docs] @staticmethod
def get_model(scenario: Scenario) -> RandomModel:
"""The model is used in the acquisition function. Since we do not use an acquisition function, we return a
dummy model (returning random values in this case).
"""
return RandomModel(
configspace=scenario.configspace,
instance_features=scenario.instance_features,
seed=scenario.seed,
)
[docs] @staticmethod
def get_acquisition_maximizer(scenario: Scenario) -> RandomSearch:
"""We return ``RandomSearch`` as maximizer which samples configurations randomly from the configuration
space and therefore neither uses the acquisition function nor the model.
"""
return RandomSearch(
scenario.configspace,
seed=scenario.seed,
)
[docs] @staticmethod
def get_multi_objective_algorithm( # type: ignore
scenario: Scenario,
*,
objective_weights: list[float] | None = None,
) -> MeanAggregationStrategy:
"""Returns the mean aggregation strategy for the multi-objective algorithm.
Parameters
----------
scenario : Scenario
objective_weights : list[float] | None, defaults to None
Weights for averaging the objectives in a weighted manner. Must be of the same length as the number of
objectives.
"""
return MeanAggregationStrategy(
scenario=scenario,
objective_weights=objective_weights,
)
[docs] @staticmethod
def get_runhistory_encoder(scenario: Scenario) -> RunHistoryEncoder:
"""Returns the default runhistory encoder."""
return RunHistoryEncoder(scenario)