def testInit(self):
config1 = OptimizationConfig(
objective=self.objective,
outcome_constraints=self.outcome_constraints)
self.assertEqual(str(config1), CONFIG_STR)
with self.assertRaises(ValueError):
config1.objective = self.m2_objective
# updating constraints is fine.
config1.outcome_constraints = [self.outcome_constraint]
self.assertEqual(len(config1.metrics), 2)
# objective without outcome_constraints is also supported
config2 = OptimizationConfig(objective=self.objective)
self.assertEqual(config2.outcome_constraints, [])
# setting objective is fine too, if it's compatible with constraints..
config2.objective = self.m2_objective
# setting incompatible constraints is not fine.
with self.assertRaises(ValueError):
config2.outcome_constraints = self.outcome_constraints
def testInit(self):
config1 = OptimizationConfig(
objective=self.objective,
outcome_constraints=self.outcome_constraints)
self.assertEqual(str(config1), OC_STR)
with self.assertRaises(ValueError):
config1.objective = self.alt_objective # constrained Objective.
# updating constraints is fine.
config1.outcome_constraints = [self.outcome_constraint]
self.assertEqual(len(config1.metrics), 2)
# objective without outcome_constraints is also supported
config2 = OptimizationConfig(objective=self.objective)
self.assertEqual(config2.outcome_constraints, [])
# setting objective is fine too, if it's compatible with constraints..
config2.objective = self.m2_objective
# setting constraints on objectives is fine for MultiObjective components.
config2.outcome_constraints = self.outcome_constraints
self.assertEqual(config2.outcome_constraints, self.outcome_constraints)
def testGen(self, mock_init, mock_best_point, mock_gen):
# Test with constraints
optimization_config = OptimizationConfig(
objective=Objective(Metric("a"), minimize=True),
outcome_constraints=[
OutcomeConstraint(Metric("b"), ComparisonOp.GEQ, 2, False)
],
)
ma = NumpyModelBridge()
ma.parameters = ["x", "y", "z"]
ma.outcomes = ["a", "b"]
ma.transforms = OrderedDict()
observation_features, weights, best_obsf, _ = ma._gen(
n=3,
search_space=self.search_space,
optimization_config=optimization_config,
pending_observations=self.pending_observations,
fixed_features=ObservationFeatures({"z": 3.0}),
model_gen_options=self.model_gen_options,
)
gen_args = mock_gen.mock_calls[0][2]
self.assertEqual(gen_args["n"], 3)
self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0),
(0.0, 5.0)])
self.assertTrue(
np.array_equal(gen_args["objective_weights"], np.array([-1.0,
0.0])))
self.assertTrue(
np.array_equal(gen_args["outcome_constraints"][0],
np.array([[0.0, -1.0]])))
self.assertTrue(
np.array_equal(gen_args["outcome_constraints"][1],
np.array([[-2]])))
self.assertTrue(
np.array_equal(
gen_args["linear_constraints"][0],
np.array([[1.0, -1, 0.0], [-1.0, 0.0, -1.0]]),
))
self.assertTrue(
np.array_equal(gen_args["linear_constraints"][1],
np.array([[0.0], [-3.5]])))
self.assertEqual(gen_args["fixed_features"], {2: 3.0})
self.assertTrue(
np.array_equal(gen_args["pending_observations"][0], np.array([])))
self.assertTrue(
np.array_equal(gen_args["pending_observations"][1],
np.array([[0.6, 1.6, 3.0]])))
self.assertEqual(gen_args["model_gen_options"], {"option": "yes"})
self.assertEqual(observation_features[0].parameters, {
"x": 1.0,
"y": 2.0,
"z": 3.0
})
self.assertEqual(observation_features[1].parameters, {
"x": 3.0,
"y": 4.0,
"z": 3.0
})
self.assertTrue(np.array_equal(weights, np.array([1.0, 2.0])))
# Test with multiple objectives.
oc2 = OptimizationConfig(objective=ScalarizedObjective(
metrics=[Metric(name="a"), Metric(name="b")], minimize=True))
observation_features, weights, best_obsf, _ = ma._gen(
n=3,
search_space=self.search_space,
optimization_config=oc2,
pending_observations=self.pending_observations,
fixed_features=ObservationFeatures({"z": 3.0}),
model_gen_options=self.model_gen_options,
)
gen_args = mock_gen.mock_calls[1][2]
self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0),
(0.0, 5.0)])
self.assertIsNone(gen_args["outcome_constraints"])
self.assertTrue(
np.array_equal(gen_args["objective_weights"],
np.array([-1.0, -1.0])))
# Test with MultiObjective (unweighted multiple objectives)
oc3 = MultiObjectiveOptimizationConfig(objective=MultiObjective(
metrics=[Metric(name="a"),
Metric(name="b", lower_is_better=True)],
minimize=True,
))
search_space = SearchSpace(self.parameters) # Unconstrained
observation_features, weights, best_obsf, _ = ma._gen(
n=3,
search_space=search_space,
optimization_config=oc3,
pending_observations=self.pending_observations,
fixed_features=ObservationFeatures({"z": 3.0}),
model_gen_options=self.model_gen_options,
)
gen_args = mock_gen.mock_calls[2][2]
self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0),
(0.0, 5.0)])
self.assertIsNone(gen_args["outcome_constraints"])
self.assertTrue(
np.array_equal(gen_args["objective_weights"], np.array([1.0,
-1.0])))
# Test with no constraints, no fixed feature, no pending observations
search_space = SearchSpace(self.parameters[:2])
optimization_config.outcome_constraints = []
ma.parameters = ["x", "y"]
ma._gen(3, search_space, {}, ObservationFeatures({}), None,
optimization_config)
gen_args = mock_gen.mock_calls[3][2]
self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0)])
self.assertIsNone(gen_args["outcome_constraints"])
self.assertIsNone(gen_args["linear_constraints"])
self.assertIsNone(gen_args["fixed_features"])
self.assertIsNone(gen_args["pending_observations"])
# Test validation
optimization_config = OptimizationConfig(
objective=Objective(Metric("a"), minimize=False),
outcome_constraints=[
OutcomeConstraint(Metric("b"), ComparisonOp.GEQ, 2, False)
],
)
with self.assertRaises(ValueError):
ma._gen(
n=3,
search_space=self.search_space,
optimization_config=optimization_config,
pending_observations={},
fixed_features=ObservationFeatures({}),
)
optimization_config.objective.minimize = True
optimization_config.outcome_constraints[0].relative = True
with self.assertRaises(ValueError):
ma._gen(
n=3,
search_space=self.search_space,
optimization_config=optimization_config,
pending_observations={},
fixed_features=ObservationFeatures({}),
)
def testGen(self, mock_init):
# Test with constraints
optimization_config = OptimizationConfig(
objective=Objective(Metric("a"), minimize=True),
outcome_constraints=[
OutcomeConstraint(Metric("b"), ComparisonOp.GEQ, 2, False)
],
)
ma = DiscreteModelBridge()
model = mock.MagicMock(DiscreteModel, autospec=True, instance=True)
model.gen.return_value = ([[0.0, 2.0, 3.0], [1.0, 1.0,
3.0]], [1.0, 2.0])
ma.model = model
ma.parameters = ["x", "y", "z"]
ma.outcomes = ["a", "b"]
observation_features, weights, best_observation = ma._gen(
n=3,
search_space=self.search_space,
optimization_config=optimization_config,
pending_observations=self.pending_observations,
fixed_features=ObservationFeatures({}),
model_gen_options=self.model_gen_options,
)
gen_args = model.gen.mock_calls[0][2]
self.assertEqual(gen_args["n"], 3)
self.assertEqual(gen_args["parameter_values"],
[[0.0, 1.0], ["foo", "bar"], [True]])
self.assertTrue(
np.array_equal(gen_args["objective_weights"], np.array([-1.0,
0.0])))
self.assertTrue(
np.array_equal(gen_args["outcome_constraints"][0],
np.array([[0.0, -1.0]])))
self.assertTrue(
np.array_equal(gen_args["outcome_constraints"][1],
np.array([[-2]])))
self.assertEqual(gen_args["pending_observations"][0], [])
self.assertEqual(gen_args["pending_observations"][1],
[[0, "foo", True]])
self.assertEqual(gen_args["model_gen_options"], {"option": "yes"})
self.assertEqual(observation_features[0].parameters, {
"x": 0.0,
"y": 2.0,
"z": 3.0
})
self.assertEqual(observation_features[1].parameters, {
"x": 1.0,
"y": 1.0,
"z": 3.0
})
self.assertEqual(weights, [1.0, 2.0])
# Test with no constraints, no fixed feature, no pending observations
search_space = SearchSpace(self.parameters[:2])
optimization_config.outcome_constraints = []
ma.parameters = ["x", "y"]
ma._gen(
n=3,
search_space=search_space,
optimization_config=optimization_config,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options={},
)
gen_args = model.gen.mock_calls[1][2]
self.assertEqual(gen_args["parameter_values"],
[[0.0, 1.0], ["foo", "bar"]])
self.assertIsNone(gen_args["outcome_constraints"])
self.assertIsNone(gen_args["pending_observations"])
# Test validation
optimization_config = OptimizationConfig(
objective=Objective(Metric("a"), minimize=False),
outcome_constraints=[
OutcomeConstraint(Metric("b"), ComparisonOp.GEQ, 2, True)
],
)
with self.assertRaises(ValueError):
ma._gen(
n=3,
search_space=search_space,
optimization_config=optimization_config,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options={},
)
