def evaluation_by_type(self, current_configuration: Configuration):
"""
Forwards a call to specific Repeater Type to evaluate if Configuration was measured precisely.
:param current_configuration: instance of Configuration class.
:returns: int Number of measurements of Configuration which need to be performed, EvaluationStatus
"""
if self._type is None:
raise TypeError("Repeater evaluation Type was not selected!")
else:
if current_configuration.status == Configuration.Status.NEW:
number_of_measurements = self._type.evaluate(
current_configuration=current_configuration,
experiment=self.experiment)
current_configuration.status = Configuration.Status.EVALUATED
if self.experiment.try_add_configuration(current_configuration) or \
current_configuration.type == Configuration.Type.DEFAULT: # for repeating default configuration
return number_of_measurements
else:
current_configuration.status = Configuration.Status.EVALUATED
return 0
else:
if current_configuration.results:
number_of_measurements = self._type.evaluate(
current_configuration=current_configuration,
experiment=self.experiment)
if number_of_measurements > 0:
current_configuration.status = Configuration.Status.REPEATED_MEASURING
return number_of_measurements
else:
current_configuration.status = Configuration.Status.MEASURED
return 0
else:
current_configuration.Status = Configuration.Status.EVALUATED
return 0
def measure_task(configurations_sample: list, tasks_sample: list,
experiment_description: dict, search_space: Hyperparameter,
measured_tasks: int, config_type: Configuration.Type,
config_status: Configuration.Status):
"""
Test function for Repeater module.
Main steps:
0. Take default tasks sample.
1. Create instances of Repeater, Experiment, Default Configuration according to test requirements.
2. Create instance of current measurement.
3. Call Repeater function.
:param configurations_sample: default sample of measured configurations
:param tasks_sample: default sample of measured tasks
:param experiment_description: Experiment Description sample in json format
:param search_space: Search Space sample
:param measured_tasks: specify number of measured tasks in current measurement.
:param config_type: specify current measurement configuration type.
:param config_status: specify current measurement configuration status.
:return: list of configuration status and number of tasks to measure.
"""
experiment = Experiment(experiment_description, search_space)
Configuration.set_task_config(experiment.description["TaskConfiguration"])
configuration = Configuration(configurations_sample[1]["Params"],
config_type, experiment.unique_id)
configuration.status = config_status
for i in range(0, measured_tasks):
configuration.add_task(tasks_sample[i])
orchestrator = RepeaterOrchestration(experiment)
if config_type == Configuration.Type.DEFAULT:
orchestrator._type = orchestrator.get_repeater(True)
else:
orchestrator._type = orchestrator.get_repeater()
default_configuration = Configuration(
configurations_sample[0]["Params"], Configuration.Type.DEFAULT,
experiment.unique_id)
default_configuration.status = Configuration.Status.MEASURED
default_configuration._task_number = configurations_sample[0]["Tasks"]
default_configuration.results = configurations_sample[0]["Results"]
default_configuration._standard_deviation = configurations_sample[0][
"STD"]
experiment.default_configuration = default_configuration
task = json.dumps({"configuration": configuration.to_json()})
dummy_channel = None
dummy_method = None
dummy_properties = None
results_measurement = orchestrator.measure_configurations(
dummy_channel, dummy_method, dummy_properties, task)
return results_measurement
def test_1_write_exp_state_record(self):
# Test #1. Format and write Experiment state (ES) record to the database
# Expected result: record can be read from the database and contains all required ES fields. The Experiment id matches
experiment, _ = self.initialize_exeriment()
c1 = Configuration(OrderedDict({
"frequency": 2900.0,
"threads": 32
}), Configuration.Type.DEFAULT, experiment.unique_id)
c1.status = c1.Status.MEASURED
experiment.default_configuration = c1
database.write_one_record("Experiment_state",
experiment.get_experiment_state_record())
written_record = database.get_all_records("Experiment_state")[0]
assert experiment.unique_id == written_record["Exp_unique_ID"]
assert experiment.get_number_of_measured_configurations(
) == written_record["Number_of_measured_configs"]
assert experiment.get_bad_configuration_number(
) == written_record["Number_of_bad_configs"]
assert experiment.get_current_solution().get_configuration_record(
) == written_record["Current_solution"]
assert experiment.get_model_state() == written_record["is_model_valid"]