def testConstraintValidation(self):
# Can build OptimizationConfig with MultiObjective
with self.assertRaises(ValueError):
OptimizationConfig(objective=self.multi_objective)
# Can't constrain on objective metric.
objective_constraint = OutcomeConstraint(metric=self.objective.metric,
op=ComparisonOp.GEQ,
bound=0)
with self.assertRaises(ValueError):
OptimizationConfig(objective=self.objective,
outcome_constraints=[objective_constraint])
# Two outcome_constraints on the same metric with the same op
# should raise.
duplicate_constraint = OutcomeConstraint(
metric=self.outcome_constraint.metric,
op=self.outcome_constraint.op,
bound=self.outcome_constraint.bound + 1,
)
with self.assertRaises(ValueError):
OptimizationConfig(
objective=self.objective,
outcome_constraints=[
self.outcome_constraint, duplicate_constraint
],
)
# Three outcome_constraints on the same metric should raise.
opposing_constraint = OutcomeConstraint(
metric=self.outcome_constraint.metric,
op=not self.outcome_constraint.op,
bound=self.outcome_constraint.bound,
)
with self.assertRaises(ValueError):
OptimizationConfig(
objective=self.objective,
outcome_constraints=self.outcome_constraints +
[opposing_constraint],
)
# Two outcome_constraints on the same metric with different ops and
# flipped bounds (lower < upper) should raise.
add_bound = 1 if self.outcome_constraint.op == ComparisonOp.LEQ else -1
opposing_constraint = OutcomeConstraint(
metric=self.outcome_constraint.metric,
op=not self.outcome_constraint.op,
bound=self.outcome_constraint.bound + add_bound,
)
with self.assertRaises(ValueError):
OptimizationConfig(
objective=self.objective,
outcome_constraints=([
self.outcome_constraint, opposing_constraint
]),
)
# Two outcome_constraints on the same metric with different ops and
# bounds should not raise.
opposing_constraint = OutcomeConstraint(
metric=self.outcome_constraint.metric,
op=not self.outcome_constraint.op,
bound=self.outcome_constraint.bound + 1,
)
config = OptimizationConfig(
objective=self.objective,
outcome_constraints=([
self.outcome_constraint, opposing_constraint
]),
)
self.assertEqual(config.outcome_constraints,
[self.outcome_constraint, opposing_constraint])
def testClone(self):
config1 = OptimizationConfig(
objective=self.objective,
outcome_constraints=self.outcome_constraints)
self.assertEqual(config1, config1.clone())
def test_best_point(
self,
_mock_gen,
_mock_best_point,
_mock_fit,
_mock_predict,
_mock_gen_arms,
_mock_unwrap,
_mock_obs_from_data,
):
exp = Experiment(search_space=get_search_space_for_range_value(), name="test")
oc = OptimizationConfig(
objective=Objective(metric=Metric("a"), minimize=False),
outcome_constraints=[],
)
modelbridge = ArrayModelBridge(
search_space=get_search_space_for_range_value(),
model=NumpyModel(),
transforms=[t1, t2],
experiment=exp,
data=Data(),
optimization_config=oc,
)
self.assertEqual(list(modelbridge.transforms.keys()), ["Cast", "t1", "t2"])
# test check that optimization config is required
with self.assertRaises(ValueError):
run = modelbridge.gen(n=1, optimization_config=None)
# _fit is mocked, which typically sets this.
modelbridge.outcomes = ["a"]
run = modelbridge.gen(
n=1,
optimization_config=oc,
)
arm, predictions = run.best_arm_predictions
self.assertEqual(arm.parameters, {})
self.assertEqual(predictions[0], {"m": 1.0})
self.assertEqual(predictions[1], {"m": {"m": 2.0}})
model_arm, model_predictions = modelbridge.model_best_point()
self.assertEqual(model_predictions[0], {"m": 1.0})
self.assertEqual(model_predictions[1], {"m": {"m": 2.0}})
# test optimization config validation - raise error when
# ScalarizedOutcomeConstraint contains a metric that is not in the outcomes
with self.assertRaises(ValueError):
run = modelbridge.gen(
n=1,
optimization_config=OptimizationConfig(
objective=Objective(metric=Metric("a"), minimize=False),
outcome_constraints=[
ScalarizedOutcomeConstraint(
metrics=[Metric("wrong_metric_name")],
weights=[1.0],
op=ComparisonOp.LEQ,
bound=0,
)
],
),
)
def testModelBridge(self, mock_fit, mock_gen_arms,
mock_observations_from_data):
# Test that on init transforms are stored and applied in the correct order
transforms = [transform_1, transform_2]
exp = get_experiment_for_value()
ss = get_search_space_for_value()
modelbridge = ModelBridge(
search_space=ss,
model=Model(),
transforms=transforms,
experiment=exp,
data=0,
)
self.assertFalse(
modelbridge._experiment_has_immutable_search_space_and_opt_config)
self.assertEqual(list(modelbridge.transforms.keys()),
["Cast", "transform_1", "transform_2"])
fit_args = mock_fit.mock_calls[0][2]
self.assertTrue(
fit_args["search_space"] == get_search_space_for_value(8.0))
self.assertTrue(fit_args["observation_features"] == [])
self.assertTrue(fit_args["observation_data"] == [])
self.assertTrue(mock_observations_from_data.called)
# Test prediction on out of design features.
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
side_effect=ValueError("Out of Design"),
)
# This point is in design, and thus failures in predict are legitimate.
with mock.patch.object(ModelBridge,
"model_space",
return_value=get_search_space_for_range_values):
with self.assertRaises(ValueError):
modelbridge.predict([get_observation2().features])
# This point is out of design, and not in training data.
with self.assertRaises(ValueError):
modelbridge.predict([get_observation_status_quo0().features])
# Now it's in the training data.
with mock.patch.object(
ModelBridge,
"get_training_data",
return_value=[get_observation_status_quo0()],
):
# Return raw training value.
self.assertEqual(
modelbridge.predict([get_observation_status_quo0().features]),
unwrap_observation_data([get_observation_status_quo0().data]),
)
# Test that transforms are applied correctly on predict
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
return_value=[get_observation2trans().data],
)
modelbridge.predict([get_observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([get_observation2().features])
# Check that _single_predict is equivalent here.
modelbridge._single_predict([get_observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([get_observation2().features])
# Test transforms applied on gen
modelbridge._gen = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._gen",
autospec=True,
return_value=([get_observation1trans().features], [2], None, {}),
)
oc = OptimizationConfig(objective=Objective(metric=Metric(
name="test_metric")))
modelbridge._set_kwargs_to_save(model_key="TestModel",
model_kwargs={},
bridge_kwargs={})
gr = modelbridge.gen(
n=1,
search_space=get_search_space_for_value(),
optimization_config=oc,
pending_observations={"a": [get_observation2().features]},
fixed_features=ObservationFeatures({"x": 5}),
)
self.assertEqual(gr._model_key, "TestModel")
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc,
pending_observations={"a": [get_observation2trans().features]},
fixed_features=ObservationFeatures({"x": 36}),
model_gen_options=None,
)
mock_gen_arms.assert_called_with(
arms_by_signature={},
observation_features=[get_observation1().features])
# Gen with no pending observations and no fixed features
modelbridge.gen(n=1,
search_space=get_search_space_for_value(),
optimization_config=None)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=None,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Gen with multi-objective optimization config.
oc2 = OptimizationConfig(objective=ScalarizedObjective(
metrics=[Metric(name="test_metric"),
Metric(name="test_metric_2")]))
modelbridge.gen(n=1,
search_space=get_search_space_for_value(),
optimization_config=oc2)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc2,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Test transforms applied on cross_validate
modelbridge._cross_validate = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._cross_validate",
autospec=True,
return_value=[get_observation1trans().data],
)
cv_training_data = [get_observation2()]
cv_test_points = [get_observation1().features]
cv_predictions = modelbridge.cross_validate(
cv_training_data=cv_training_data, cv_test_points=cv_test_points)
modelbridge._cross_validate.assert_called_with(
search_space=SearchSpace(
[FixedParameter("x", ParameterType.FLOAT, 8.0)]),
obs_feats=[get_observation2trans().features],
obs_data=[get_observation2trans().data],
cv_test_points=[get_observation1().features
], # untransformed after
)
self.assertTrue(cv_predictions == [get_observation1().data])
# Test stored training data
obs = modelbridge.get_training_data()
self.assertTrue(obs == [get_observation1(), get_observation2()])
self.assertEqual(modelbridge.metric_names, {"a", "b"})
self.assertIsNone(modelbridge.status_quo)
self.assertTrue(
modelbridge.model_space == get_search_space_for_value())
self.assertEqual(modelbridge.training_in_design, [False, False])
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
# Test feature_importances
with self.assertRaises(NotImplementedError):
modelbridge.feature_importances("a")
# Test transform observation features
with mock.patch(
"ax.modelbridge.base.ModelBridge._transform_observation_features",
autospec=True,
) as mock_tr:
modelbridge.transform_observation_features(
[get_observation2().features])
mock_tr.assert_called_with(modelbridge,
[get_observation2trans().features])
def get_map_optimization_config() -> OptimizationConfig:
objective = get_map_objective()
return OptimizationConfig(objective=objective)
def get_branin_optimization_config(
minimize: bool = False) -> OptimizationConfig:
return OptimizationConfig(objective=get_branin_objective(
minimize=minimize))
def get_branin_multi_objective_optimization_config() -> OptimizationConfig:
return OptimizationConfig(objective=get_branin_multi_objective())
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({}),
)
### Hartmann6 problem, D=100 and D=1000
# Relevant parameters were chosen randomly using
# x = np.arange(100)
# np.random.seed(10)
# np.random.shuffle(x)
# print(x[:6]) # [19 14 43 37 66 3]
hartmann6_100 = BenchmarkProblem(
name="Hartmann6, D=100",
optimal_value=-3.32237,
optimization_config=OptimizationConfig(
objective=Objective(
metric=Hartmann6Metric(
name="objective",
param_names=["x19", "x14", "x43", "x37", "x66", "x3"],
noise_sd=0.0,
),
minimize=True,
)
),
search_space=SearchSpace(
parameters=[
RangeParameter(
name=f"x{i}", parameter_type=ParameterType.FLOAT, lower=0.0, upper=1.0
)
for i in range(100)
]
),
)
def testModelBridge(self, mock_fit, mock_gen_arms, mock_observations_from_data):
# Test that on init transforms are stored and applied in the correct order
transforms = [t1, t2]
exp = get_experiment()
modelbridge = ModelBridge(search_space_for_value(), 0, transforms, exp, 0)
self.assertEqual(list(modelbridge.transforms.keys()), ["t1", "t2"])
fit_args = mock_fit.mock_calls[0][2]
self.assertTrue(fit_args["search_space"] == search_space_for_value(8.0))
self.assertTrue(
fit_args["observation_features"]
== [observation1trans().features, observation2trans().features]
)
self.assertTrue(
fit_args["observation_data"]
== [observation1trans().data, observation2trans().data]
)
self.assertTrue(mock_observations_from_data.called)
# Test that transforms are applied correctly on predict
modelbridge._predict = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._predict",
autospec=True,
return_value=[observation2trans().data],
)
modelbridge.predict([observation2().features])
# Observation features sent to _predict are un-transformed afterwards
modelbridge._predict.assert_called_with([observation2().features])
# Test transforms applied on gen
modelbridge._gen = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._gen",
autospec=True,
return_value=([observation1trans().features], [2], None),
)
oc = OptimizationConfig(objective=Objective(metric=Metric(name="test_metric")))
modelbridge._set_kwargs_to_save(
model_key="TestModel", model_kwargs={}, bridge_kwargs={}
)
gr = modelbridge.gen(
n=1,
search_space=search_space_for_value(),
optimization_config=oc,
pending_observations={"a": [observation2().features]},
fixed_features=ObservationFeatures({"x": 5}),
)
self.assertEqual(gr._model_key, "TestModel")
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace([FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc,
pending_observations={"a": [observation2trans().features]},
fixed_features=ObservationFeatures({"x": 36}),
model_gen_options=None,
)
mock_gen_arms.assert_called_with(
arms_by_signature={}, observation_features=[observation1().features]
)
# Gen with no pending observations and no fixed features
modelbridge.gen(
n=1, search_space=search_space_for_value(), optimization_config=None
)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace([FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=None,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Gen with multi-objective optimization config.
oc2 = OptimizationConfig(
objective=ScalarizedObjective(
metrics=[Metric(name="test_metric"), Metric(name="test_metric_2")]
)
)
modelbridge.gen(
n=1, search_space=search_space_for_value(), optimization_config=oc2
)
modelbridge._gen.assert_called_with(
n=1,
search_space=SearchSpace([FixedParameter("x", ParameterType.FLOAT, 8.0)]),
optimization_config=oc2,
pending_observations={},
fixed_features=ObservationFeatures({}),
model_gen_options=None,
)
# Test transforms applied on cross_validate
modelbridge._cross_validate = mock.MagicMock(
"ax.modelbridge.base.ModelBridge._cross_validate",
autospec=True,
return_value=[observation1trans().data],
)
cv_training_data = [observation2()]
cv_test_points = [observation1().features]
cv_predictions = modelbridge.cross_validate(
cv_training_data=cv_training_data, cv_test_points=cv_test_points
)
modelbridge._cross_validate.assert_called_with(
obs_feats=[observation2trans().features],
obs_data=[observation2trans().data],
cv_test_points=[observation1().features], # untransformed after
)
self.assertTrue(cv_predictions == [observation1().data])
# Test stored training data
obs = modelbridge.get_training_data()
self.assertTrue(obs == [observation1(), observation2()])
self.assertEqual(modelbridge.metric_names, {"a", "b"})
self.assertIsNone(modelbridge.status_quo)
self.assertTrue(modelbridge.model_space == search_space_for_value())
self.assertEqual(modelbridge.training_in_design, [True, True])
modelbridge.training_in_design = [True, False]
with self.assertRaises(ValueError):
modelbridge.training_in_design = [True, True, False]
ood_obs = modelbridge.out_of_design_data()
self.assertTrue(ood_obs == unwrap_observation_data([observation2().data]))
def make_experiment(
parameters: List[TParameterRepresentation],
name: Optional[str] = None,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
status_quo: Optional[TParameterization] = None,
experiment_type: Optional[str] = None,
# Single-objective optimization arguments:
objective_name: Optional[str] = None,
minimize: bool = False,
# Multi-objective optimization arguments:
objectives: Optional[Dict[str, str]] = None,
objective_thresholds: Optional[List[str]] = None,
) -> Experiment:
"""Instantiation wrapper that allows for Ax `Experiment` creation
without importing or instantiating any Ax classes.
Args:
parameters: List of dictionaries representing parameters in the
experiment search space.
Required elements in the dictionaries are:
1. "name" (name of parameter, string),
2. "type" (type of parameter: "range", "fixed", or "choice", string),
and one of the following:
3a. "bounds" for range parameters (list of two values, lower bound
first),
3b. "values" for choice parameters (list of values), or
3c. "value" for fixed parameters (single value).
Optional elements are:
1. "log_scale" (for float-valued range parameters, bool),
2. "value_type" (to specify type that values of this parameter should
take; expects "float", "int", "bool" or "str"),
3. "is_fidelity" (bool) and "target_value" (float) for fidelity
parameters,
4. "is_ordered" (bool) for choice parameters,
5. "is_task" (bool) for task parameters, and
6. "digits" (int) for float-valued range parameters.
name: Name of the experiment to be created.
parameter_constraints: List of string representation of parameter
constraints, such as "x3 >= x4" or "-x3 + 2*x4 - 3.5*x5 >= 2". For
the latter constraints, any number of arguments is accepted, and
acceptable operators are "<=" and ">=".
parameter_constraints: List of string representation of parameter
constraints, such as "x3 >= x4" or "-x3 + 2*x4 - 3.5*x5 >= 2". For
the latter constraints, any number of arguments is accepted, and
acceptable operators are "<=" and ">=".
outcome_constraints: List of string representation of outcome
constraints of form "metric_name >= bound", like "m1 <= 3."
status_quo: Parameterization of the current state of the system.
If set, this will be added to each trial to be evaluated alongside
test configurations.
experiment_type: String indicating type of the experiment (e.g. name of
a product in which it is used), if any.
objective_name: Name of the metric used as objective in this experiment,
if experiment is single-objective optimization.
minimize: Whether this experiment represents a minimization problem, if
experiment is a single-objective optimization.
objectives: Mapping from an objective name to "minimize" or "maximize"
representing the direction for that objective. Used only for
multi-objective optimization experiments.
objective_thresholds: A list of objective threshold constraints for multi-
objective optimization, in the same string format as `outcome_constraints`
argument.
"""
if objective_name is not None and (objectives is not None
or objective_thresholds is not None):
raise UnsupportedError(
"Ambiguous objective definition: for single-objective optimization "
"`objective_name` and `minimize` arguments expected. For multi-objective "
"optimization `objectives` and `objective_thresholds` arguments expected."
)
status_quo_arm = None if status_quo is None else Arm(parameters=status_quo)
if objectives is None:
optimization_config = OptimizationConfig(
objective=Objective(
metric=Metric(
name=objective_name or DEFAULT_OBJECTIVE_NAME,
lower_is_better=minimize,
),
minimize=minimize,
),
outcome_constraints=make_outcome_constraints(
outcome_constraints or [], status_quo_arm is not None),
)
else:
optimization_config = make_optimization_config(
objectives,
objective_thresholds or [],
outcome_constraints or [],
status_quo_arm is not None,
)
return Experiment(
name=name,
search_space=make_search_space(parameters, parameter_constraints
or []),
optimization_config=optimization_config,
status_quo=status_quo_arm,
experiment_type=experiment_type,
)
def test_REMBOStrategy(self, mock_fit_gpytorch_model, mock_optimize_acqf):
# Construct a high-D test experiment with multiple metrics
hartmann_search_space = SearchSpace(
parameters=[
RangeParameter(
name=f"x{i}",
parameter_type=ParameterType.FLOAT,
lower=0.0,
upper=1.0,
)
for i in range(20)
]
)
exp = Experiment(
name="test",
search_space=hartmann_search_space,
optimization_config=OptimizationConfig(
objective=Objective(
metric=Hartmann6Metric(
name="hartmann6", param_names=[f"x{i}" for i in range(6)]
),
minimize=True,
),
outcome_constraints=[
OutcomeConstraint(
metric=L2NormMetric(
name="l2norm",
param_names=[f"x{i}" for i in range(6)],
noise_sd=0.2,
),
op=ComparisonOp.LEQ,
bound=1.25,
relative=False,
)
],
),
runner=SyntheticRunner(),
)
# Instantiate the strategy
gs = REMBOStrategy(D=20, d=6, k=4, init_per_proj=4)
# Check that arms and data are correctly segmented by projection
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
self.assertEqual(len(gs.arms_by_proj[0]), 2)
self.assertEqual(len(gs.arms_by_proj[1]), 0)
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
self.assertEqual(len(gs.arms_by_proj[0]), 2)
self.assertEqual(len(gs.arms_by_proj[1]), 2)
# Iterate until the first projection fits a GP
for _ in range(4):
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
mock_fit_gpytorch_model.assert_not_called()
self.assertEqual(len(gs.arms_by_proj[0]), 4)
self.assertEqual(len(gs.arms_by_proj[1]), 4)
self.assertEqual(len(gs.arms_by_proj[2]), 2)
self.assertEqual(len(gs.arms_by_proj[3]), 2)
# Keep iterating until GP is used for gen
for i in range(4):
# First two trials will go towards 3rd and 4th proj. getting enough
if i < 1: # data for GP.
self.assertLess(len(gs.arms_by_proj[2]), 4)
if i < 2:
self.assertLess(len(gs.arms_by_proj[3]), 4)
exp.new_batch_trial(generator_run=gs.gen(experiment=exp, n=2)).run()
if i < 2:
mock_fit_gpytorch_model.assert_not_called()
else:
# After all proj. have > 4 arms' worth of data, GP can be fit.
self.assertFalse(any(len(x) < 4 for x in gs.arms_by_proj.values()))
mock_fit_gpytorch_model.assert_called()
self.assertTrue(len(gs.model_transitions) > 0)
gs2 = gs.clone_reset()
self.assertEqual(gs2.D, 20)
self.assertEqual(gs2.d, 6)
def testTransformOptimizationConfig(self):
# basic test
m1 = Metric(name="m1")
objective_m1 = Objective(metric=m1, minimize=False)
oc = OptimizationConfig(objective=objective_m1, outcome_constraints=[])
tf = LogY(
search_space=None,
observation_features=None,
observation_data=[self.obsd1, self.obsd2],
config={"metrics": ["m1"]},
)
oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
self.assertEqual(oc_tf, oc)
# output constraint on a different metric should work
m2 = Metric(name="m2")
oc = OptimizationConfig(
objective=objective_m1,
outcome_constraints=self.get_constraint(
metric=m2, bound=-1, relative=False
),
)
oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
self.assertEqual(oc_tf, oc)
# output constraint with a negative bound should fail
objective_m2 = Objective(metric=m2, minimize=False)
oc = OptimizationConfig(
objective=objective_m2,
outcome_constraints=self.get_constraint(
metric=m1, bound=-1.234, relative=False
),
)
with self.assertRaises(ValueError) as cm:
tf.transform_optimization_config(oc, None, None)
self.assertEqual(
"LogY transform cannot be applied to metric m1 since the "
"bound isn't positive, got: -1.234.",
str(cm.exception),
)
# output constraint with a zero bound should also fail
oc = OptimizationConfig(
objective=objective_m2,
outcome_constraints=self.get_constraint(metric=m1, bound=0, relative=False),
)
with self.assertRaises(ValueError) as cm:
tf.transform_optimization_config(oc, None, None)
self.assertEqual(
"LogY transform cannot be applied to metric m1 since the "
"bound isn't positive, got: 0.",
str(cm.exception),
)
# output constraint with a positive bound should work
oc = OptimizationConfig(
objective=objective_m2,
outcome_constraints=self.get_constraint(
metric=m1, bound=2.345, relative=False
),
)
oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
oc.outcome_constraints[0].bound = math.log(2.345)
self.assertEqual(oc_tf, oc)
# output constraint with a relative bound should fail
oc = OptimizationConfig(
objective=objective_m2,
outcome_constraints=self.get_constraint(
metric=m1, bound=2.345, relative=True
),
)
with self.assertRaises(ValueError) as cm:
tf.transform_optimization_config(oc, None, None)
self.assertEqual(
"LogY transform cannot be applied to metric m1 since it is "
"subject to a relative constraint.",
str(cm.exception),
)
def get_hartmann_optimization_config() -> OptimizationConfig:
return OptimizationConfig(objective=get_hartmann_objective())
search_space: search space, on which this problem is defined
"""
name: str
fbest: float
optimization_config: OptimizationConfig
search_space: SearchSpace
# Branin problems
branin = BenchmarkProblem(
name=branin_function.name,
fbest=branin_function.fmin,
optimization_config=OptimizationConfig(objective=Objective(
metric=BraninMetric(
name="branin_objective", param_names=["x1", "x2"], noise_sd=5.0),
minimize=True,
)),
search_space=get_branin_search_space(),
)
branin_max = BenchmarkProblem(
name=branin_function.name,
fbest=branin_function.fmax,
optimization_config=OptimizationConfig(objective=Objective(
metric=NegativeBraninMetric(
name="neg_branin", param_names=["x1", "x2"], noise_sd=5.0),
minimize=False,
)),
search_space=get_branin_search_space(),
)
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={},
)
def setUp(self):
self.df = pd.DataFrame([
{
"arm_name": "0_0",
"mean": 2.0,
"sem": 0.2,
"trial_index": 1,
"metric_name": "a",
"start_time": "2018-01-01",
"end_time": "2018-01-02",
},
{
"arm_name": "0_0",
"mean": 1.8,
"sem": 0.3,
"trial_index": 1,
"metric_name": "b",
"start_time": "2018-01-01",
"end_time": "2018-01-02",
},
{
"arm_name": "0_1",
"mean": float("nan"),
"sem": float("nan"),
"trial_index": 1,
"metric_name": "a",
"start_time": "2018-01-01",
"end_time": "2018-01-02",
},
{
"arm_name": "0_1",
"mean": 3.7,
"sem": 0.5,
"trial_index": 1,
"metric_name": "b",
"start_time": "2018-01-01",
"end_time": "2018-01-02",
},
{
"arm_name": "0_2",
"mean": 0.5,
"sem": None,
"trial_index": 1,
"metric_name": "a",
"start_time": "2018-01-01",
"end_time": "2018-01-02",
},
{
"arm_name": "0_2",
"mean": float("nan"),
"sem": float("nan"),
"trial_index": 1,
"metric_name": "b",
"start_time": "2018-01-01",
"end_time": "2018-01-02",
},
{
"arm_name": "0_2",
"mean": float("nan"),
"sem": float("nan"),
"trial_index": 1,
"metric_name": "c",
"start_time": "2018-01-01",
"end_time": "2018-01-02",
},
])
self.data = Data(df=self.df)
self.optimization_config = OptimizationConfig(
objective=Objective(metric=Metric(name="a")),
outcome_constraints=[
OutcomeConstraint(metric=Metric(name="b"),
op=ComparisonOp.GEQ,
bound=0)
],
)
def get_optimization_config() -> OptimizationConfig:
objective = get_objective()
outcome_constraints = [get_outcome_constraint()]
return OptimizationConfig(objective=objective,
outcome_constraints=outcome_constraints)
def testDerelativizeTransform(self, mock_predict, mock_fit,
mock_observations_from_data):
t = Derelativize(search_space=None,
observation_features=None,
observation_data=None)
# ModelBridge with in-design status quo
search_space = SearchSpace(parameters=[
RangeParameter("x", ParameterType.FLOAT, 0, 20),
RangeParameter("y", ParameterType.FLOAT, 0, 20),
])
g = ModelBridge(
search_space=search_space,
model=None,
transforms=[],
experiment=Experiment(search_space, "test"),
data=Data(),
status_quo_name="1_1",
)
# Test with no relative constraints
objective = Objective(Metric("c"))
oc = OptimizationConfig(
objective=objective,
outcome_constraints=[
OutcomeConstraint(Metric("a"),
ComparisonOp.LEQ,
bound=2,
relative=False)
],
)
oc2 = t.transform_optimization_config(oc, g, None)
self.assertTrue(oc == oc2)
# Test with relative constraint, in-design status quo
oc = OptimizationConfig(
objective=objective,
outcome_constraints=[
OutcomeConstraint(Metric("a"),
ComparisonOp.LEQ,
bound=2,
relative=False),
OutcomeConstraint(Metric("b"),
ComparisonOp.LEQ,
bound=-10,
relative=True),
],
)
oc = t.transform_optimization_config(oc, g, None)
self.assertTrue(oc.outcome_constraints == [
OutcomeConstraint(
Metric("a"), ComparisonOp.LEQ, bound=2, relative=False),
OutcomeConstraint(
Metric("b"), ComparisonOp.LEQ, bound=4.5, relative=False),
])
obsf = mock_predict.mock_calls[0][1][1][0]
obsf2 = ObservationFeatures(parameters={"x": 2.0, "y": 10.0})
self.assertTrue(obsf == obsf2)
# Test with relative constraint, out-of-design status quo
mock_predict.side_effect = Exception()
g = ModelBridge(
search_space=search_space,
model=None,
transforms=[],
experiment=Experiment(search_space, "test"),
data=Data(),
status_quo_name="1_2",
)
oc = OptimizationConfig(
objective=objective,
outcome_constraints=[
OutcomeConstraint(Metric("a"),
ComparisonOp.LEQ,
bound=2,
relative=False),
OutcomeConstraint(Metric("b"),
ComparisonOp.LEQ,
bound=-10,
relative=True),
],
)
oc = t.transform_optimization_config(oc, g, None)
self.assertTrue(oc.outcome_constraints == [
OutcomeConstraint(
Metric("a"), ComparisonOp.LEQ, bound=2, relative=False),
OutcomeConstraint(
Metric("b"), ComparisonOp.LEQ, bound=3.6, relative=False),
])
self.assertEqual(mock_predict.call_count, 2)
# Raises error if predict fails with in-design status quo
g = ModelBridge(search_space, None, [], status_quo_name="1_1")
oc = OptimizationConfig(
objective=objective,
outcome_constraints=[
OutcomeConstraint(Metric("a"),
ComparisonOp.LEQ,
bound=2,
relative=False),
OutcomeConstraint(Metric("b"),
ComparisonOp.LEQ,
bound=-10,
relative=True),
],
)
with self.assertRaises(Exception):
oc = t.transform_optimization_config(oc, g, None)
# Raises error with relative constraint, no status quo
exp = Experiment(search_space, "name")
g = ModelBridge(search_space, None, [], exp)
with self.assertRaises(ValueError):
oc = t.transform_optimization_config(oc, g, None)
# Raises error with relative constraint, no modelbridge
with self.assertRaises(ValueError):
oc = t.transform_optimization_config(oc, None, None)
def get_optimization_config_no_constraints() -> OptimizationConfig:
return OptimizationConfig(objective=Objective(
metric=Metric("test_metric")))
def make_experiment(
parameters: List[TParameterRepresentation],
name: Optional[str] = None,
parameter_constraints: Optional[List[str]] = None,
outcome_constraints: Optional[List[str]] = None,
status_quo: Optional[TParameterization] = None,
experiment_type: Optional[str] = None,
tracking_metric_names: Optional[List[str]] = None,
# Single-objective optimization arguments:
objective_name: Optional[str] = None,
minimize: bool = False,
# Multi-objective optimization arguments:
objectives: Optional[Dict[str, str]] = None,
objective_thresholds: Optional[List[str]] = None,
support_intermediate_data: Optional[bool] = False,
immutable_search_space_and_opt_config: Optional[bool] = True,
) -> Experiment:
"""Instantiation wrapper that allows for Ax `Experiment` creation
without importing or instantiating any Ax classes.
Args:
parameters: List of dictionaries representing parameters in the
experiment search space.
Required elements in the dictionaries are:
1. "name" (name of parameter, string),
2. "type" (type of parameter: "range", "fixed", or "choice", string),
and one of the following:
3a. "bounds" for range parameters (list of two values, lower bound
first),
3b. "values" for choice parameters (list of values), or
3c. "value" for fixed parameters (single value).
Optional elements are:
1. "log_scale" (for float-valued range parameters, bool),
2. "value_type" (to specify type that values of this parameter should
take; expects "float", "int", "bool" or "str"),
3. "is_fidelity" (bool) and "target_value" (float) for fidelity
parameters,
4. "is_ordered" (bool) for choice parameters,
5. "is_task" (bool) for task parameters, and
6. "digits" (int) for float-valued range parameters.
name: Name of the experiment to be created.
parameter_constraints: List of string representation of parameter
constraints, such as "x3 >= x4" or "-x3 + 2*x4 - 3.5*x5 >= 2". For
the latter constraints, any number of arguments is accepted, and
acceptable operators are "<=" and ">=".
parameter_constraints: List of string representation of parameter
constraints, such as "x3 >= x4" or "-x3 + 2*x4 - 3.5*x5 >= 2". For
the latter constraints, any number of arguments is accepted, and
acceptable operators are "<=" and ">=".
outcome_constraints: List of string representation of outcome
constraints of form "metric_name >= bound", like "m1 <= 3."
status_quo: Parameterization of the current state of the system.
If set, this will be added to each trial to be evaluated alongside
test configurations.
experiment_type: String indicating type of the experiment (e.g. name of
a product in which it is used), if any.
tracking_metric_names: Names of additional tracking metrics not used for
optimization.
objective_name: Name of the metric used as objective in this experiment,
if experiment is single-objective optimization.
minimize: Whether this experiment represents a minimization problem, if
experiment is a single-objective optimization.
objectives: Mapping from an objective name to "minimize" or "maximize"
representing the direction for that objective. Used only for
multi-objective optimization experiments.
objective_thresholds: A list of objective threshold constraints for multi-
objective optimization, in the same string format as `outcome_constraints`
argument.
support_intermediate_data: whether trials may report metrics results for
incomplete runs.
immutable_search_space_and_opt_config: Whether it's possible to update the
search space and optimization config on this experiment after creation.
Defaults to True. If set to True, we won't store or load copies of the
search space and optimization config on each generator run, which will
improve storage performance.
"""
if objective_name is not None and (objectives is not None
or objective_thresholds is not None):
raise UnsupportedError(
"Ambiguous objective definition: for single-objective optimization "
"`objective_name` and `minimize` arguments expected. For multi-objective "
"optimization `objectives` and `objective_thresholds` arguments expected."
)
status_quo_arm = None if status_quo is None else Arm(parameters=status_quo)
# TODO(jej): Needs to be decided per-metric when supporting heterogenous data.
metric_cls = MapMetric if support_intermediate_data else Metric
if objectives is None:
optimization_config = OptimizationConfig(
objective=Objective(
metric=metric_cls(
name=objective_name or DEFAULT_OBJECTIVE_NAME,
lower_is_better=minimize,
),
minimize=minimize,
),
outcome_constraints=make_outcome_constraints(
outcome_constraints or [], status_quo_arm is not None),
)
else:
optimization_config = make_optimization_config(
objectives,
objective_thresholds or [],
outcome_constraints or [],
status_quo_arm is not None,
)
tracking_metrics = (None if tracking_metric_names is None else [
Metric(name=metric_name) for metric_name in tracking_metric_names
])
default_data_type = (DataType.MAP_DATA
if support_intermediate_data else DataType.DATA)
immutable_ss_and_oc = immutable_search_space_and_opt_config
properties = ({} if not immutable_search_space_and_opt_config else {
Keys.IMMUTABLE_SEARCH_SPACE_AND_OPT_CONF.value:
immutable_ss_and_oc
})
return Experiment(
name=name,
search_space=make_search_space(parameters, parameter_constraints
or []),
optimization_config=optimization_config,
status_quo=status_quo_arm,
experiment_type=experiment_type,
tracking_metrics=tracking_metrics,
default_data_type=default_data_type,
properties=properties,
)
def get_augmented_branin_optimization_config() -> OptimizationConfig:
return OptimizationConfig(objective=get_augmented_branin_objective())
def testTransformOptimizationConfig(self):
# basic test
m1 = Metric(name="m1")
objective_m1 = Objective(metric=m1, minimize=False)
oc = OptimizationConfig(objective=objective_m1, outcome_constraints=[])
tf = PowerTransformY(
search_space=None,
observation_features=None,
observation_data=[self.obsd1, self.obsd2],
config={"metrics": ["m1"]},
)
oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
self.assertEqual(oc_tf, oc)
# Output constraint on a different metric should not transform the bound
m2 = Metric(name="m2")
for bound in [-1.234, 0, 2.345]:
oc = OptimizationConfig(
objective=objective_m1,
outcome_constraints=get_constraint(
metric=m2, bound=bound, relative=False
),
)
oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
self.assertEqual(oc_tf, oc)
# Output constraint on the same metric should transform the bound
objective_m2 = Objective(metric=m2, minimize=False)
for bound in [-1.234, 0, 2.345]:
oc = OptimizationConfig(
objective=objective_m2,
outcome_constraints=get_constraint(
metric=m1, bound=bound, relative=False
),
)
oc_tf = tf.transform_optimization_config(deepcopy(oc), None, None)
oc_true = deepcopy(oc)
tf_bound = (
tf.power_transforms["m1"].transform(np.array(bound, ndmin=2)).item()
)
oc_true.outcome_constraints[0].bound = tf_bound
self.assertEqual(oc_tf, oc_true)
# Relative constraints aren't supported
oc = OptimizationConfig(
objective=objective_m2,
outcome_constraints=get_constraint(metric=m1, bound=2.345, relative=True),
)
with self.assertRaisesRegex(
ValueError,
"PowerTransformY cannot be applied to metric m1 since it is "
"subject to a relative constraint.",
):
tf.transform_optimization_config(oc, None, None)
# Support for scalarized outcome constraints isn't implemented
m3 = Metric(name="m3")
oc = OptimizationConfig(
objective=objective_m2,
outcome_constraints=[
ScalarizedOutcomeConstraint(
metrics=[m1, m3], op=ComparisonOp.GEQ, bound=2.345, relative=False
)
],
)
with self.assertRaises(NotImplementedError) as cm:
tf.transform_optimization_config(oc, None, None)
self.assertEqual(
"PowerTransformY cannot be used for metric(s) {'m1'} "
"that are part of a ScalarizedOutcomeConstraint.",
str(cm.exception),
)