def test_initialization(self):
factory = DummyMCOFactory(self.plugin)
self.assertEqual(factory.id, "pid.factory.dummy_mco")
self.assertEqual(factory.name, "Dummy MCO")
self.assertIsInstance(factory.create_optimizer(), DummyMCO)
self.assertIsInstance(factory.create_communicator(),
DummyMCOCommunicator)
self.assertIsInstance(factory.create_model(), DummyMCOModel)
def test_base_object_parameter_factories(self):
factory = DummyMCOFactory(self.plugin)
self.assertEqual(1, len(factory.parameter_factories))
for parameter_factory in factory.parameter_factories:
self.assertIsInstance(parameter_factory, DummyMCOParameterFactory)
self.assertRaisesRegex(
KeyError,
"not a valid id",
factory.parameter_factory_by_id,
"not a valid id",
)
self.assertIs(
factory.parameter_factory_by_id(factory.parameter_factories[0].id),
factory.parameter_factories[0],
)
def setUp(self):
self.plugin = {"id": "pid", "name": "Plugin"}
self.factory = DummyMCOFactory(self.plugin)
workflow_file = ProbeWorkflowFile(path=fixtures.get("test_probe.json"))
workflow_file.read()
self.workflow = workflow_file.workflow
self.parameters = [1, 1, 1, 1]
self.optimizer_engine = DummyOptimizerEngine(
single_point_evaluator=self.workflow)
def setUp(self):
self.plugin = {"id": "pid", "name": "Plugin"}
self.factory = DummyMCOFactory(self.plugin)
self.parameters = [
RangedMCOParameterFactory(self.factory).create_model(
{
"lower_bound": 0.0,
"upper_bound": 1.0
})
] * 4
self.engine = AposterioriOptimizerEngine(
single_point_evaluator=ProbeEvaluator(),
optimizer=ProbeOptimizer(),
parameters=self.parameters)
def setUp(self):
self.plugin = {"id": "pid", "name": "Plugin"}
self.factory = DummyMCOFactory(self.plugin)
self.parameters = [
RangedMCOParameterFactory(self.factory).create_model({
"lower_bound":
0.0,
"upper_bound":
1.0
}) for _ in range(4)
]
self.kpis = [KPISpecification()] * 2
self.optimizer = DummyOptimizerEngine(
kpis=self.kpis,
parameters=self.parameters,
single_point_evaluator=GaussProbeEvaluator())
self.scaling_values = self.optimizer.scaling_values.tolist()
def test_transfer_parameters(self):
# create old model
old_factory = DummyMCOFactory(plugin={'id': 'bcy356', 'name': 'old'})
old_factory.parameter_factory_classes = [
RangedVectorMCOParameterFactory, RangedMCOParameterFactory,
CategoricalMCOParameterFactory
]
old_model = BaseMCOModel(factory=old_factory)
old_model.parameters = [
RangedMCOParameter(
initial_value=1.0,
factory=RangedVectorMCOParameterFactory(old_factory)),
RangedVectorMCOParameter(
initial_value=[1.0, 1.0],
factory=RangedMCOParameterFactory(old_factory)),
CategoricalMCOParameter(
categories=['A', 'B', 'C'],
factory=CategoricalMCOParameterFactory(old_factory))
]
old_model.kpis = []
# create new model, that we want to transfer the parameters and
# kpis to.
new_factory = DummyMCOFactory(plugin={'id': 'xxui1', 'name': 'new'})
new_factory.parameter_factory_classes = [
RangedVectorMCOParameterFactory,
RangedMCOParameterFactory,
]
new_model = BaseMCOModel(factory=new_factory)
# workflow tree to work with
workflow_tree = WorkflowTree(
model=Workflow(),
_factory_registry=self.factory_registry,
system_state=self.system_state,
)
# transfer the parameters and kpis
workflow_tree.transfer_parameters_and_kpis(old_model, new_model)
# have ONLY two parameters been transferred?
# (the third categorical parameter of the old_model
# should not be filtered out as it is not allowed by the new model).
self.assertEqual(2, len(new_model.parameters))
for p in new_model.parameters:
self.assertNotIsInstance(p, CategoricalMCOParameter)
# has the correct plugin id been transferred?
self.assertEqual('xxui1',
new_model.parameters[0].factory.mco_factory.plugin_id)
def setUp(self):
self.plugin = {"id": "pid", "name": "Plugin"}
self.factory = DummyMCOFactory(self.plugin)
self.kpis = [KPISpecification(), KPISpecification()]
self.parameters = [1, 1, 1, 1]
self.parameters = [
RangedMCOParameterFactory(self.factory).create_model({
"lower_bound":
0.0,
"upper_bound":
1.0
}) for _ in self.parameters
]
self.optimizer = WeightedScipyEngine(parameters=self.parameters,
kpis=self.kpis)
self.mocked_optimizer = DummyOptimizerEngine(
parameters=self.parameters,
kpis=self.kpis,
single_point_evaluator=GaussProbeEvaluator())
def setUp(self):
self.factory = DummyMCOFactory({"id": "pid", "name": "test"})
self.model = BaseModel(self.factory)
def _mco_factories_default(self):
return [DummyMCOFactory(self.plugin)]
def setUp(self):
self.optimizer = ScipyOptimizer()
self.plugin = {"id": "pid", "name": "Plugin"}
self.factory = DummyMCOFactory(self.plugin)