def __init__(self, experiment, optimizer_params=None):
"""
Initializes the random search optimizer.
Parameters
----------
experiment : Experiment
The experiment representing the current state of the execution.
optimizer_params : dict, optional
Dictionary of the optimizer parameters. If None, some standard
parameters will be assumed.
Available parameters are
"random_state" : randomstate, optional
The random state to use. See numpy random states.
Raises
------
ValueError
Iff the experiment is not supported.
"""
self._logger = logging_utils.get_logger(self)
self._logger.debug(
"Initializing random search. experiment is %s,"
"optimizer_params %s", experiment, optimizer_params)
if optimizer_params is None:
optimizer_params = {}
self.random_state = optimizer_params.get("random_state", None)
self._logger.debug("Initialized random state to %s", self.random_state)
Optimizer.__init__(self, experiment, optimizer_params)
def __init__(self, experiment, optimizer_params=None):
"""
Initializes a bayesian optimizer.
Parameters
----------
experiment : Experiment
The experiment for which to optimize.
optimizer_arguments: dict of string keys
Sets the possible arguments for this optimizer. Available are:
"initial_random_runs" : int, optional
The number of initial random runs before using the GP. Default
is 10.
"random_state" : scipy random state, optional
The scipy random state or object to initialize one. Default is
None.
"acquisition_hyperparameters" : dict, optional
dictionary of acquisition-function hyperparameters
"num_gp_restarts" : int
GPy's optimization requires restarts to find a good solution.
This parameter controls this. Default is 10.
"acquisition" : AcquisitionFunction
The acquisition function to use. Default is
ExpectedImprovement.
"num_precomputed" : int
The number of points that should be kept precomputed for faster
multiple workers.
"""
self._logger = get_logger(self)
if optimizer_params is None:
optimizer_params = {}
self.random_state = optimizer_params.get("random_state", None)
self.initial_random_runs = optimizer_params.get(
'initial_random_runs', self.initial_random_runs)
self.random_state = check_random_state(
optimizer_params.get('random_state', None))
self.acquisition_hyperparams = optimizer_params.get(
'acquisition_hyperparams', None)
self.num_gp_restarts = optimizer_params.get(
'num_gp_restarts', self.num_gp_restarts)
if not isinstance(optimizer_params.get('acquisition'), AcquisitionFunction):
self.acquisition_function = optimizer_params.get(
'acquisition', ExpectedImprovement)(self.acquisition_hyperparams)
else:
self.acquisition_function = optimizer_params.get("acquisition")
self.kernel_params = optimizer_params.get("kernel_params", {})
self.kernel = optimizer_params.get("kernel", "matern52")
self.random_searcher = RandomSearch(experiment, optimizer_params)
Optimizer.__init__(self, experiment, optimizer_params)
def __init__(self, experiment, optimizer_params=None):
"""
Initializes the random search optimizer.
Parameters
----------
experiment : Experiment
The experiment representing the current state of the execution.
optimizer_params : dict, optional
Dictionary of the optimizer parameters. If None, some standard
parameters will be assumed.
Available parameters are
"random_state" : randomstate, optional
The random state to use. See numpy random states.
Raises
------
ValueError
Iff the experiment is not supported.
"""
if optimizer_params is None:
optimizer_params = {}
self.random_state = optimizer_params.get("random_state", None)
Optimizer.__init__(self, experiment, optimizer_params)
def __init__(self, experiment, optimizer_params=None):
"""
Initializes a bayesian optimizer.
Parameters
----------
experiment : Experiment
The experiment for which to optimize.
optimizer_arguments: dict of string keys
Sets the possible arguments for this optimizer. Available are:
"initial_random_runs" : int, optional
The number of initial random runs before using the GP. Default
is 10.
"random_state" : scipy random state, optional
The scipy random state or object to initialize one. Default is
None.
"acquisition_hyperparameters" : dict, optional
dictionary of acquisition-function hyperparameters
"num_gp_restarts" : int
GPy's optimization requires restarts to find a good solution.
This parameter controls this. Default is 10.
"acquisition" : AcquisitionFunction
The acquisition function to use. Default is
ExpectedImprovement.
"num_precomputed" : int
The number of points that should be kept precomputed for faster
multiple workers.
"""
self._logger = get_logger(self)
self._logger.debug(
"Initializing bayesian optimizer. Experiment is %s,"
" optimizer_params %s", experiment, optimizer_params)
if optimizer_params is None:
optimizer_params = {}
self.random_state = optimizer_params.get("random_state", None)
self.initial_random_runs = optimizer_params.get(
'initial_random_runs', self.initial_random_runs)
self.random_state = check_random_state(
optimizer_params.get('random_state', None))
self.acquisition_hyperparams = optimizer_params.get(
'acquisition_hyperparams', None)
self.num_gp_restarts = optimizer_params.get('num_gp_restarts',
self.num_gp_restarts)
self._logger.debug(
"Initialized relevant parameters. "
"initial_random_runs is %s, random_state is %s, "
"acquisition_hyperparams %s, num_gp_restarts %s",
self.initial_random_runs, self.random_state,
self.acquisition_hyperparams, self.num_gp_restarts)
if not isinstance(optimizer_params.get('acquisition'),
AcquisitionFunction):
self.acquisition_function = optimizer_params.get(
"acquisition", ExpectedImprovement)
self.acquisition_function = check_acquisition(
acquisition=self.acquisition_function,
acquisition_params=self.acquisition_hyperparams)
self._logger.debug(
"acquisition is no AcquisitionFunction. Set "
"it to %s", self.acquisition_function)
else:
self.acquisition_function = optimizer_params.get("acquisition")
self._logger.debug(
"Loaded acquisition function from "
"optimizer_params. Is %s", self.acquisition_function)
self.kernel_params = optimizer_params.get("kernel_params", {})
self.kernel = optimizer_params.get("kernel", "matern52")
self._logger.debug("Kernel details: Kernel is %s, kernel_params %s",
self.kernel, self.kernel_params)
self.random_searcher = RandomSearch(experiment, optimizer_params)
self._logger.debug("Initialized required RandomSearcher; is %s",
self.random_searcher)
Optimizer.__init__(self, experiment, optimizer_params)
self._logger.debug("Finished initializing bayOpt.")
