def setExperiment(self, experiment):
parameters = Parameter.parametersToDict(experiment.parameters)
parameters_linearly_spaced_vals = []
# parameter start from the lower bound to higher bound
for idx, parameter in enumerate(experiment.parameters):
ncpp = self.num_configs_per_param[idx]
# step_size = (parameter.maximum - parameter.minimum) / (ncpp - 1)
if ncpp == 1:
step_size = 0
else:
step_size = (parameter.maximum - parameter.minimum) / (ncpp -
1)
parameter_linearly_spaced_vals = [
parameter.minimum + (i * step_size) for i in range(ncpp)
]
parameter_linearly_spaced_vals = reversed(
parameter_linearly_spaced_vals)
parameters_linearly_spaced_vals.append(
parameter_linearly_spaced_vals)
# get cartesian product of configs
parameter_configs_product = itertools.product(
*parameters_linearly_spaced_vals)
# create collections of ParameterConfigs from config values
for parameter_config_collection in parameter_configs_product:
parameter_configs = []
for parameter, value in zip(experiment.parameters,
parameter_config_collection):
parameter_configs.append(
ParameterConfig(parameter=parameter, value=value))
self.grid_parameter_configs.append(parameter_configs)
def setExperiment(self, experiment):
"""
This is called by the runner after the experiment is properly initialized
"""
self.parameters_by_name = {parameter.name: parameter for parameter in experiment.parameters}
self.optimizer = RandomSearchOptimizer(pbounds=Parameter.parametersToDict(experiment.parameters), random_seed=self.random_seed)
self.experiment_id = experiment.id
self.previous_trials = experiment.trials
def setExperiment(self, experiment):
"""
This is called by the runner after the experiment is properly initialized
"""
self.parameters_by_name = {
parameter.name: parameter
for parameter in experiment.parameters
}
self.optimizer = BayesianOptimization(
f=None,
pbounds=Parameter.parametersToDict(experiment.parameters),
verbose=2,
random_state=randint(1, 100),
)
self.experiment_id = experiment.id
self.previous_trials = experiment.trials
def setExperiment(self, experiment):
parameters = Parameter.parametersToDict(experiment.parameters)
ncpp = self.num_configs_per_param
parameters_linearly_spaced_vals = []
for parameter in experiment.parameters:
step_size = (parameter.maximum - parameter.minimum) / (ncpp - 1)
parameter_linearly_spaced_vals = [
parameter.minimum + (i * step_size) for i in range(ncpp)
]
parameters_linearly_spaced_vals.append(
parameter_linearly_spaced_vals)
# get cartesian product of configs
parameter_configs_product = itertools.product(
*parameters_linearly_spaced_vals)
# create collections of ParameterConfigs from config values
for parameter_config_collection in parameter_configs_product:
parameter_configs = []
for parameter, value in zip(experiment.parameters,
parameter_config_collection):
parameter_configs.append(
ParameterConfig(parameter=parameter, value=value))
self.grid_parameter_configs.append(parameter_configs)