def test_verify_mco_parameters(self):
optimizer = ScipyOptimizer()
parameter_factory = self.factory.parameter_factories[0]
wrong_parameter = parameter_factory.create_model()
with self.assertRaises(ScipyTypeError):
optimizer.verify_mco_parameters([wrong_parameter])
def run(self, evaluator):
model = evaluator.mco_model
optim = ScipyOptimizer(algorithms=model.algorithms)
optimizer = WeightedOptimizerEngine(
kpis=model.kpis,
parameters=model.parameters,
num_points=model.num_points,
space_search_mode=model.space_search_mode,
single_point_evaluator=evaluator,
verbose_run=model.verbose_run,
optimizer=optim,
)
for (
optimal_point,
optimal_kpis,
scaled_weights,
) in optimizer.optimize():
# When there is new data, this operation informs the system that
# new data has been received. It must be a dictionary as given.
evaluator.mco_model.notify_progress_event(
[DataValue(value=v) for v in optimal_point],
[DataValue(value=v) for v in optimal_kpis],
weights=scaled_weights,
)
class WeightedMCOModel(BaseMCOModel):
#: Algorithms available to work with
algorithms = Enum(
*ScipyOptimizer.class_traits()["algorithms"].handler.values)
#: Search grid resolution per KPI
num_points = PositiveInt(7)
#: Display the generated points at runtime
verbose_run = Bool(True)
#: Space search distribution for weight points sampling
space_search_mode = Enum("Uniform", "Dirichlet")
#: 'Subprocess' mode performs evaluation of a state in the workflow via
#: calling force_bdss on a new subprocess with SubprocessWorkflowEvaluator
evaluation_mode = Enum("Internal", "Subprocess")
def default_traits_view(self):
return View(
Item("evaluation_mode"),
Item("algorithms"),
Item("num_points", label="Weights grid resolution per KPI"),
Item("space_search_mode"),
Item("verbose_run"),
)
def __start_event_type_default(self):
return WeightedMCOStartEvent
def __progress_event_type_default(self):
return WeightedMCOProgressEvent
class TestScipyOptimizer(TestCase):
def setUp(self):
self.optimizer = ScipyOptimizer()
self.plugin = {"id": "pid", "name": "Plugin"}
self.factory = DummyMCOFactory(self.plugin)
def test_init(self):
self.assertEqual("SLSQP", self.optimizer.algorithms)
def test_verify_mco_parameters(self):
optimizer = ScipyOptimizer()
parameter_factory = self.factory.parameter_factories[0]
wrong_parameter = parameter_factory.create_model()
with self.assertRaises(ScipyTypeError):
optimizer.verify_mco_parameters([wrong_parameter])
def test_optimize_function(self):
factory = RangedVectorMCOParameterFactory(self.factory)
params = [
RangedVectorMCOParameter(factory=factory,
name='coordinates',
dimension=2,
lower_bound=[-2, -2],
upper_bound=[2, 2],
initial_value=[1, 1])
]
# test single-objective (scalar) function
for point in self.optimizer.optimize_function(
lambda arg: arg[0][0]**2 + arg[0][1]**2, params):
x, y = point[0]
self.assertAlmostEqual(x, 0.0)
self.assertAlmostEqual(y, 0.0)
# test multi-objective function (objectives should be summed)
for point in self.optimizer.optimize_function(
lambda arg: [arg[0][0]**2, arg[0][1]**2], params):
x, y = point[0]
self.assertAlmostEqual(x, 0.0)
self.assertAlmostEqual(y, 0.0)
def setUp(self):
self.optimizer = ScipyOptimizer()
self.plugin = {"id": "pid", "name": "Plugin"}
self.factory = DummyMCOFactory(self.plugin)
def _optimizer_default(self):
return ScipyOptimizer()