def setUp(self):
self.minimize_metric = Metric(name="bar", lower_is_better=True)
self.maximize_metric = Metric(name="baz", lower_is_better=False)
self.bound = 0
simple_metric = Metric(name="foo")
self.constraint = OutcomeConstraint(metric=simple_metric,
op=ComparisonOp.GEQ,
bound=self.bound)
def setUp(self):
self.metrics = {"m1": Metric(name="m1"), "m2": Metric(name="m2")}
self.objective = Objective(metric=self.metrics["m1"], minimize=False)
self.alt_objective = Objective(metric=self.metrics["m2"],
minimize=False)
self.multi_objective = MultiObjective(
metrics=[self.metrics["m1"], self.metrics["m2"]])
self.m2_objective = ScalarizedObjective(
metrics=[self.metrics["m1"], self.metrics["m2"]])
self.outcome_constraint = OutcomeConstraint(metric=self.metrics["m2"],
op=ComparisonOp.GEQ,
bound=-0.25)
self.additional_outcome_constraint = OutcomeConstraint(
metric=self.metrics["m2"], op=ComparisonOp.LEQ, bound=0.25)
self.outcome_constraints = [
self.outcome_constraint,
self.additional_outcome_constraint,
]
def test_feasible_hypervolume(self):
ma = Metric(name="a", lower_is_better=False)
mb = Metric(name="b", lower_is_better=True)
mc = Metric(name="c", lower_is_better=False)
optimization_config = MultiObjectiveOptimizationConfig(
objective=MultiObjective(metrics=[ma, mb]),
outcome_constraints=[
OutcomeConstraint(
mc,
op=ComparisonOp.GEQ,
bound=0,
relative=False,
)
],
objective_thresholds=[
ObjectiveThreshold(
ma,
bound=1.0,
),
ObjectiveThreshold(
mb,
bound=1.0,
),
],
)
feas_hv = feasible_hypervolume(
optimization_config,
values={
"a": np.array(
[
1.0,
3.0,
2.0,
2.0,
]
),
"b": np.array(
[
0.0,
1.0,
0.0,
0.0,
]
),
"c": np.array(
[
0.0,
-0.0,
1.0,
-2.0,
]
),
},
)
self.assertEqual(list(feas_hv), [0.0, 0.0, 1.0, 1.0])
def testEq(self):
threshold1 = ObjectiveThreshold(metric=self.minimize_metric,
bound=self.bound)
threshold2 = ObjectiveThreshold(metric=self.minimize_metric,
bound=self.bound)
self.assertEqual(threshold1, threshold2)
constraint3 = OutcomeConstraint(metric=self.minimize_metric,
op=ComparisonOp.LEQ,
bound=self.bound)
self.assertNotEqual(threshold1, constraint3)
def test_transform_ref_point(self, _mock_fit, _mock_predict, _mock_unwrap):
exp = get_branin_experiment_with_multi_objective(
has_optimization_config=True, with_batch=False)
metrics = exp.optimization_config.objective.metrics
ref_point = {metrics[0].name: 0.0, metrics[1].name: 0.0}
modelbridge = MultiObjectiveTorchModelBridge(
search_space=exp.search_space,
model=MultiObjectiveBotorchModel(),
optimization_config=exp.optimization_config,
transforms=[t1, t2],
experiment=exp,
data=exp.fetch_data(),
ref_point=ref_point,
)
self.assertIsNone(modelbridge._transformed_ref_point)
exp = get_branin_experiment_with_multi_objective(
has_optimization_config=True, with_batch=True)
exp.attach_data(
get_branin_data_multi_objective(trial_indices=exp.trials))
modelbridge = MultiObjectiveTorchModelBridge(
search_space=exp.search_space,
model=MultiObjectiveBotorchModel(),
optimization_config=exp.optimization_config,
transforms=[t1, t2],
experiment=exp,
data=exp.fetch_data(),
ref_point=ref_point,
)
self.assertIsNotNone(modelbridge._transformed_ref_point)
self.assertEqual(2, len(modelbridge._transformed_ref_point))
mixed_objective_constraints_optimization_config = OptimizationConfig(
objective=MultiObjective(
metrics=[get_branin_metric(name="branin_b")], minimize=False),
outcome_constraints=[
OutcomeConstraint(metric=Metric(name="branin_a"),
op=ComparisonOp.LEQ,
bound=1)
],
)
modelbridge = MultiObjectiveTorchModelBridge(
search_space=exp.search_space,
model=MultiObjectiveBotorchModel(),
optimization_config=mixed_objective_constraints_optimization_config,
transforms=[t1, t2],
experiment=exp,
data=exp.fetch_data(),
ref_point={"branin_b": 0.0},
)
self.assertEqual({"branin_a", "branin_b"}, modelbridge._metric_names)
self.assertEqual(["branin_b"], modelbridge._objective_metric_names)
self.assertIsNotNone(modelbridge._transformed_ref_point)
self.assertEqual(1, len(modelbridge._transformed_ref_point))
def testTransformOptimizationConfig(self):
# basic test
m1 = Metric(name="m1")
objective = Objective(metric=m1, minimize=False)
oc = OptimizationConfig(objective=objective, 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(oc, None, None)
self.assertTrue(oc_tf == oc)
# test error if transformed metric appears in outcome constraints
m2 = Metric(name="m2")
cons = [
OutcomeConstraint(metric=m2,
op=ComparisonOp.GEQ,
bound=0.0,
relative=False)
]
oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
oc_tf = tf.transform_optimization_config(oc, None, None)
self.assertTrue(oc_tf == oc)
m2 = Metric(name="m2")
cons = [
OutcomeConstraint(metric=m2,
op=ComparisonOp.GEQ,
bound=0.0,
relative=False)
]
oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
tf2 = LogY(
search_space=None,
observation_features=None,
observation_data=[self.obsd1, self.obsd2],
config={"metrics": ["m2"]},
)
with self.assertRaises(ValueError):
tf2.transform_optimization_config(oc, None, None)
def outcome_constraint_from_str(representation: str) -> OutcomeConstraint:
"""Parse string representation of an outcome constraint."""
tokens = representation.split()
assert len(tokens) == 3 and tokens[1] in COMPARISON_OPS, (
"Outcome constraint should be of form `metric_name >= x`, where x is a "
"float bound and comparison operator is >= or <=."
)
op = COMPARISON_OPS[tokens[1]]
try:
bound = float(tokens[2])
except ValueError:
raise ValueError("Outcome constraint bound should be a float.")
return OutcomeConstraint(Metric(name=tokens[0]), op=op, bound=bound, relative=False)
def testTransformOptimizationConfig(self):
m1 = Metric(name="m1")
m2 = Metric(name="m2")
m3 = Metric(name="m3")
objective = Objective(metric=m3, minimize=False)
cons = [
OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=2.0,
relative=False),
OutcomeConstraint(metric=m2,
op=ComparisonOp.LEQ,
bound=3.5,
relative=False),
]
oc = OptimizationConfig(objective=objective, outcome_constraints=cons)
oc = self.t.transform_optimization_config(oc, None, None)
cons_t = [
OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=1.0,
relative=False),
OutcomeConstraint(metric=m2,
op=ComparisonOp.LEQ,
bound=4.0,
relative=False),
]
self.assertTrue(oc.outcome_constraints == cons_t)
self.assertTrue(oc.objective == objective)
# Check fail with relative
con = OutcomeConstraint(metric=m1,
op=ComparisonOp.GEQ,
bound=2.0,
relative=True)
oc = OptimizationConfig(objective=objective, outcome_constraints=[con])
with self.assertRaises(ValueError):
oc = self.t.transform_optimization_config(oc, None, None)
def setUp(self):
self.metrics = {
"m1": Metric(name="m1", lower_is_better=True),
"m2": Metric(name="m2", lower_is_better=False),
"m3": Metric(name="m3", lower_is_better=False),
}
self.objectives = {
"o1": Objective(metric=self.metrics["m1"]),
"o2": Objective(metric=self.metrics["m2"], minimize=False),
"o3": Objective(metric=self.metrics["m3"], minimize=False),
}
self.objective = Objective(metric=self.metrics["m1"], minimize=False)
self.multi_objective = MultiObjective(
objectives=[self.objectives["o1"], self.objectives["o2"]])
self.multi_objective_just_m2 = MultiObjective(
objectives=[self.objectives["o2"]])
self.outcome_constraint = OutcomeConstraint(metric=self.metrics["m2"],
op=ComparisonOp.GEQ,
bound=-0.25)
self.additional_outcome_constraint = OutcomeConstraint(
metric=self.metrics["m2"], op=ComparisonOp.LEQ, bound=0.25)
self.outcome_constraints = [
self.outcome_constraint,
self.additional_outcome_constraint,
]
self.objective_thresholds = [
ObjectiveThreshold(metric=self.metrics["m2"],
bound=-1.0,
relative=False)
]
self.m1_constraint = OutcomeConstraint(metric=self.metrics["m1"],
op=ComparisonOp.LEQ,
bound=0.1,
relative=True)
self.m3_constraint = OutcomeConstraint(metric=self.metrics["m3"],
op=ComparisonOp.GEQ,
bound=0.1,
relative=True)
def metric_from_sqa(
self, metric_sqa: SQAMetric
) -> Union[Metric, Objective, OutcomeConstraint]:
"""Convert SQLAlchemy Metric to Ax Metric, Objective, or OutcomeConstraint."""
metric_class = REVERSE_METRIC_REGISTRY.get(metric_sqa.metric_type)
if metric_class is None:
raise SQADecodeError(
f"Cannot decode SQAMetric because {metric_sqa.metric_type} "
f"is an invalid type."
)
args = self.get_init_args_from_properties(
# pyre-fixme[6]: Expected `SQABase` for ...es` but got `SQAMetric`.
object_sqa=metric_sqa,
class_=metric_class,
)
metric = metric_class(**args)
if metric_sqa.intent == MetricIntent.TRACKING:
return metric
elif metric_sqa.intent == MetricIntent.OBJECTIVE:
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Objective because minimize is None."
)
# pyre-fixme[6]: Expected `bool` for 2nd param but got `Optional[bool]`.
return Objective(metric=metric, minimize=metric_sqa.minimize)
elif metric_sqa.intent == MetricIntent.OUTCOME_CONSTRAINT:
if (
metric_sqa.bound is None
or metric_sqa.op is None
or metric_sqa.relative is None
):
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to OutcomeConstraint because "
"bound, op, or relative is None."
)
return OutcomeConstraint(
metric=metric,
# pyre-fixme[6]: Expected `float` for 2nd param but got
# `Optional[float]`.
bound=metric_sqa.bound,
op=metric_sqa.op,
relative=metric_sqa.relative,
)
else:
raise SQADecodeError(
f"Cannot decode SQAMetric because {metric_sqa.intent} "
f"is an invalid intent."
)
def test_best_raw_objective_point_unsatisfiable(self):
exp = get_branin_experiment()
trial = exp.new_trial(
generator_run=GeneratorRun(arms=[Arm(parameters={"x1": 5.0, "x2": 5.0})])
).run()
trial.mark_completed()
opt_conf = exp.optimization_config.clone()
opt_conf.outcome_constraints.append(
OutcomeConstraint(
metric=get_branin_metric(), op=ComparisonOp.LEQ, bound=0, relative=False
)
)
with self.assertRaisesRegex(ValueError, "No points satisfied"):
get_best_raw_objective_point(exp, opt_conf)
def testEq(self):
config1 = OptimizationConfig(
objective=self.objective, outcome_constraints=self.outcome_constraints
)
config2 = OptimizationConfig(
objective=self.objective, outcome_constraints=self.outcome_constraints
)
self.assertEqual(config1, config2)
new_outcome_constraint = OutcomeConstraint(
metric=self.metrics["m2"], op=ComparisonOp.LEQ, bound=0.5
)
config3 = OptimizationConfig(
objective=self.objective,
outcome_constraints=[self.outcome_constraint, new_outcome_constraint],
)
self.assertNotEqual(config1, config3)
def testExperimentObjectiveThresholdUpdates(self):
experiment = get_experiment_with_batch_trial()
save_experiment(experiment)
self.assertEqual(get_session().query(SQAMetric).count(),
len(experiment.metrics))
# update objective threshold
# (should perform update in place)
optimization_config = get_multi_objective_optimization_config()
objective_threshold = get_objective_threshold()
optimization_config.objective_thresholds = [objective_threshold]
experiment.optimization_config = optimization_config
save_experiment(experiment)
self.assertEqual(get_session().query(SQAMetric).count(), 6)
# add outcome constraint
outcome_constraint2 = OutcomeConstraint(metric=Metric(name="outcome"),
op=ComparisonOp.GEQ,
bound=-0.5)
optimization_config.outcome_constraints = [
optimization_config.outcome_constraints[0],
outcome_constraint2,
]
experiment.optimization_config = optimization_config
save_experiment(experiment)
self.assertEqual(get_session().query(SQAMetric).count(), 7)
# remove outcome constraint
# (old one should become tracking metric)
optimization_config.outcome_constraints = []
experiment.optimization_config = optimization_config
save_experiment(experiment)
self.assertEqual(get_session().query(SQAMetric).count(), 5)
loaded_experiment = load_experiment(experiment.name)
self.assertEqual(experiment, loaded_experiment)
# Optimization config should correctly reload even with no
# objective_thresholds
optimization_config.objective_thresholds = []
save_experiment(experiment)
self.assertEqual(get_session().query(SQAMetric).count(), 4)
loaded_experiment = load_experiment(experiment.name)
self.assertEqual(experiment, loaded_experiment)
def testHasGoodOptConfigModelFit(self):
# Construct diagnostics
result = []
for i, obs in enumerate(self.training_data):
result.append(
CVResult(observed=obs, predicted=self.observation_data[i]))
diag = compute_diagnostics(result=result)
assess_model_fit_result = assess_model_fit(
diagnostics=diag,
significance_level=0.05,
)
# Test single objective
optimization_config = OptimizationConfig(objective=Objective(
metric=Metric("a")))
has_good_fit = has_good_opt_config_model_fit(
optimization_config=optimization_config,
assess_model_fit_result=assess_model_fit_result,
)
self.assertFalse(has_good_fit)
# Test multi objective
optimization_config = MultiObjectiveOptimizationConfig(
objective=MultiObjective(
metrics=[Metric("a"), Metric("b")]))
has_good_fit = has_good_opt_config_model_fit(
optimization_config=optimization_config,
assess_model_fit_result=assess_model_fit_result,
)
self.assertFalse(has_good_fit)
# Test constraints
optimization_config = OptimizationConfig(
objective=Objective(metric=Metric("a")),
outcome_constraints=[
OutcomeConstraint(metric=Metric("b"),
op=ComparisonOp.GEQ,
bound=0.1)
],
)
has_good_fit = has_good_opt_config_model_fit(
optimization_config=optimization_config,
assess_model_fit_result=assess_model_fit_result,
)
self.assertFalse(has_good_fit)
def testExperimentOutcomeConstraintUpdates(self):
experiment = get_experiment_with_batch_trial()
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAMetric).count(), len(experiment.metrics)
)
# update outcome constraint
# (should perform update in place)
optimization_config = get_optimization_config()
outcome_constraint = get_outcome_constraint()
outcome_constraint.bound = -1.0
optimization_config.outcome_constraints = [outcome_constraint]
experiment.optimization_config = optimization_config
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAMetric).count(), len(experiment.metrics)
)
# add outcome constraint
outcome_constraint2 = OutcomeConstraint(
metric=Metric(name="outcome"), op=ComparisonOp.GEQ, bound=-0.5
)
optimization_config.outcome_constraints = [
outcome_constraint,
outcome_constraint2,
]
experiment.optimization_config = optimization_config
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAMetric).count(), len(experiment.metrics)
)
# remove outcome constraint
# (old one should become tracking metric)
optimization_config.outcome_constraints = [outcome_constraint]
experiment.optimization_config = optimization_config
save_experiment(experiment)
self.assertEqual(
get_session().query(SQAMetric).count(), len(experiment.metrics)
)
loaded_experiment = load_experiment(experiment.name)
self.assertEqual(experiment, loaded_experiment)
def testErrors(self):
t = Derelativize(search_space=None,
observation_features=None,
observation_data=None)
oc = OptimizationConfig(
objective=Objective(Metric("c")),
outcome_constraints=[
OutcomeConstraint(Metric("a"),
ComparisonOp.LEQ,
bound=2,
relative=True)
],
)
search_space = SearchSpace(
parameters=[RangeParameter("x", ParameterType.FLOAT, 0, 20)])
g = ModelBridge(search_space, None, [])
with self.assertRaises(ValueError):
t.transform_optimization_config(oc, None, None)
with self.assertRaises(ValueError):
t.transform_optimization_config(oc, g, None)
def test_transform_optimization_config_with_non_relative_constraints(self):
relativize = Relativize(
search_space=None,
observation_features=[],
observation_data=[],
modelbridge=self.model,
)
optimization_config = get_branin_optimization_config()
optimization_config.outcome_constraints = [
OutcomeConstraint(
metric=BraninMetric("b2", ["x2", "x1"]),
op=ComparisonOp.GEQ,
bound=-200.0,
relative=False,
)
]
with self.assertRaisesRegex(ValueError, "All constraints must be relative"):
relativize.transform_optimization_config(
optimization_config=optimization_config,
modelbridge=None,
fixed_features=Mock(),
)
def testRelativeConstraint(self):
branin_rel = BenchmarkProblem(
name="constrained_branin",
fbest=0.397887,
optimization_config=OptimizationConfig(
objective=Objective(
metric=BraninMetric(name="branin_objective",
param_names=["x1", "x2"],
noise_sd=5.0),
minimize=True,
),
outcome_constraints=[
OutcomeConstraint(
metric=L2NormMetric(
name="branin_constraint",
param_names=["x1", "x2"],
noise_sd=5.0,
),
op=ComparisonOp.LEQ,
bound=5.0,
relative=True,
)
],
),
search_space=get_branin_search_space(),
)
suite = BOBenchmarkingSuite()
suite.run(
num_runs=1,
total_iterations=5,
bo_strategies=[
GenerationStrategy(
[GenerationStep(model=Models.SOBOL, num_arms=5)])
],
bo_problems=[branin_rel],
)
with self.assertRaises(ValueError):
suite.generate_report()
def testLowerBound(self):
branin_lb = BenchmarkProblem(
name="constrained_branin",
fbest=0.397887,
optimization_config=OptimizationConfig(
objective=Objective(
metric=BraninMetric(name="branin_objective",
param_names=["x1", "x2"],
noise_sd=5.0),
minimize=True,
),
outcome_constraints=[
OutcomeConstraint(
metric=L2NormMetric(
name="branin_constraint",
param_names=["x1", "x2"],
noise_sd=5.0,
),
op=ComparisonOp.GEQ,
bound=5.0,
relative=False,
)
],
),
search_space=get_branin_search_space(),
)
suite = BOBenchmarkingSuite()
suite.run(
num_runs=1,
batch_size=2,
total_iterations=4,
bo_strategies=[
GenerationStrategy(
[GenerationStep(model=Models.SOBOL, num_arms=5)])
],
bo_problems=[branin_lb],
)
suite.generate_report(include_individual=True)
def test_best_raw_objective_point_unsatisfiable_relative(self):
exp = get_branin_experiment()
# Optimization config with unsatisfiable constraint
opt_conf = exp.optimization_config.clone()
opt_conf.outcome_constraints.append(
OutcomeConstraint(
metric=get_branin_metric(),
op=ComparisonOp.GEQ,
bound=9999,
relative=True,
))
trial = exp.new_trial(generator_run=GeneratorRun(
arms=[Arm(parameters={
"x1": 5.0,
"x2": 5.0
})])).run()
trial.mark_completed()
with self.assertLogs(logger="ax.service.utils.best_point",
level="WARN") as lg:
get_best_raw_objective_point(exp, opt_conf)
self.assertTrue(
any("No status quo provided" in warning
for warning in lg.output),
msg=lg.output,
)
exp.status_quo = Arm(parameters={"x1": 0, "x2": 0}, name="status_quo")
sq_trial = exp.new_trial(generator_run=GeneratorRun(
arms=[exp.status_quo])).run()
sq_trial.mark_completed()
with self.assertRaisesRegex(ValueError, "No points satisfied"):
get_best_raw_objective_point(exp, opt_conf)
def test_create_experiment(self) -> None:
"""Test basic experiment creation."""
ax_client = AxClient(
GenerationStrategy(
steps=[GenerationStep(model=Models.SOBOL, num_trials=30)]))
with self.assertRaisesRegex(ValueError,
"Experiment not set on Ax client"):
ax_client.experiment
ax_client.create_experiment(
name="test_experiment",
parameters=[
{
"name": "x",
"type": "range",
"bounds": [0.001, 0.1],
"value_type": "float",
"log_scale": True,
},
{
"name": "y",
"type": "choice",
"values": [1, 2, 3],
"value_type": "int",
"is_ordered": True,
},
{
"name": "x3",
"type": "fixed",
"value": 2,
"value_type": "int"
},
{
"name": "x4",
"type": "range",
"bounds": [1.0, 3.0],
"value_type": "int",
},
{
"name": "x5",
"type": "choice",
"values": ["one", "two", "three"],
"value_type": "str",
},
{
"name": "x6",
"type": "range",
"bounds": [1.0, 3.0],
"value_type": "int",
},
],
objective_name="test_objective",
minimize=True,
outcome_constraints=["some_metric >= 3", "some_metric <= 4.0"],
parameter_constraints=["x4 <= x6"],
)
assert ax_client._experiment is not None
self.assertEqual(ax_client._experiment, ax_client.experiment)
self.assertEqual(
ax_client._experiment.search_space.parameters["x"],
RangeParameter(
name="x",
parameter_type=ParameterType.FLOAT,
lower=0.001,
upper=0.1,
log_scale=True,
),
)
self.assertEqual(
ax_client._experiment.search_space.parameters["y"],
ChoiceParameter(
name="y",
parameter_type=ParameterType.INT,
values=[1, 2, 3],
is_ordered=True,
),
)
self.assertEqual(
ax_client._experiment.search_space.parameters["x3"],
FixedParameter(name="x3",
parameter_type=ParameterType.INT,
value=2),
)
self.assertEqual(
ax_client._experiment.search_space.parameters["x4"],
RangeParameter(name="x4",
parameter_type=ParameterType.INT,
lower=1.0,
upper=3.0),
)
self.assertEqual(
ax_client._experiment.search_space.parameters["x5"],
ChoiceParameter(
name="x5",
parameter_type=ParameterType.STRING,
values=["one", "two", "three"],
),
)
self.assertEqual(
ax_client._experiment.optimization_config.outcome_constraints[0],
OutcomeConstraint(
metric=Metric(name="some_metric"),
op=ComparisonOp.GEQ,
bound=3.0,
relative=False,
),
)
self.assertEqual(
ax_client._experiment.optimization_config.outcome_constraints[1],
OutcomeConstraint(
metric=Metric(name="some_metric"),
op=ComparisonOp.LEQ,
bound=4.0,
relative=False,
),
)
self.assertTrue(
ax_client._experiment.optimization_config.objective.minimize)
def get_branin_outcome_constraint() -> OutcomeConstraint:
return OutcomeConstraint(metric=get_branin_metric(), op=ComparisonOp.LEQ, bound=0)
def get_outcome_constraint() -> OutcomeConstraint:
return OutcomeConstraint(metric=Metric(name="m2"), op=ComparisonOp.GEQ, bound=-0.25)
def metric_from_sqa(
self, metric_sqa: SQAMetric
) -> Union[Metric, Objective, OutcomeConstraint]:
"""Convert SQLAlchemy Metric to Ax Metric, Objective, or OutcomeConstraint."""
metric = self.metric_from_sqa_util(metric_sqa)
if metric_sqa.intent == MetricIntent.TRACKING:
return metric
elif metric_sqa.intent == MetricIntent.OBJECTIVE:
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Objective because minimize is None."
)
if metric_sqa.scalarized_objective_weight is not None:
raise SQADecodeError( # pragma: no cover
"The metric corresponding to regular objective does not \
have weight attribute")
return Objective(metric=metric, minimize=metric_sqa.minimize)
elif (metric_sqa.intent == MetricIntent.MULTI_OBJECTIVE
): # metric_sqa is a parent whose children are individual
# metrics in MultiObjective
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to MultiObjective \
because minimize is None.")
metrics_sqa_children = metric_sqa.scalarized_objective_children_metrics
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to MultiObjective \
because the parent metric has no children metrics.")
# Extracting metric and weight for each child
metrics = [
self.metric_from_sqa_util(child)
for child in metrics_sqa_children
]
return MultiObjective(
metrics=list(metrics),
# pyre-fixme[6]: Expected `bool` for 2nd param but got `Optional[bool]`.
minimize=metric_sqa.minimize,
)
elif (metric_sqa.intent == MetricIntent.SCALARIZED_OBJECTIVE
): # metric_sqa is a parent whose children are individual
# metrics in Scalarized Objective
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized Objective \
because minimize is None.")
metrics_sqa_children = metric_sqa.scalarized_objective_children_metrics
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized Objective \
because the parent metric has no children metrics.")
# Extracting metric and weight for each child
metrics, weights = zip(*[(
self.metric_from_sqa_util(child),
child.scalarized_objective_weight,
) for child in metrics_sqa_children])
return ScalarizedObjective(
metrics=list(metrics),
weights=list(weights),
# pyre-fixme[6]: Expected `bool` for 3nd param but got `Optional[bool]`.
minimize=metric_sqa.minimize,
)
elif metric_sqa.intent == MetricIntent.OUTCOME_CONSTRAINT:
if (metric_sqa.bound is None or metric_sqa.op is None
or metric_sqa.relative is None):
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to OutcomeConstraint because "
"bound, op, or relative is None.")
return OutcomeConstraint(
metric=metric,
# pyre-fixme[6]: Expected `float` for 2nd param but got
# `Optional[float]`.
bound=metric_sqa.bound,
op=metric_sqa.op,
relative=metric_sqa.relative,
)
elif metric_sqa.intent == MetricIntent.SCALARIZED_OUTCOME_CONSTRAINT:
if (metric_sqa.bound is None or metric_sqa.op is None
or metric_sqa.relative is None):
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized OutcomeConstraint because "
"bound, op, or relative is None.")
metrics_sqa_children = (
metric_sqa.scalarized_outcome_constraint_children_metrics)
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized OutcomeConstraint \
because the parent metric has no children metrics.")
# Extracting metric and weight for each child
metrics, weights = zip(*[(
self.metric_from_sqa_util(child),
child.scalarized_outcome_constraint_weight,
) for child in metrics_sqa_children])
return ScalarizedOutcomeConstraint(
metrics=list(metrics),
weights=list(weights),
# pyre-fixme[6]: Expected `float` for 2nd param but got
# `Optional[float]`.
bound=metric_sqa.bound,
op=metric_sqa.op,
relative=metric_sqa.relative,
)
elif metric_sqa.intent == MetricIntent.OBJECTIVE_THRESHOLD:
if metric_sqa.bound is None or metric_sqa.relative is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to ObjectiveThreshold because "
"bound, op, or relative is None.")
return ObjectiveThreshold(
metric=metric,
# pyre-fixme[6]: Expected `float` for 2nd param but got
# `Optional[float]`.
bound=metric_sqa.bound,
relative=metric_sqa.relative,
op=metric_sqa.op,
)
else:
raise SQADecodeError(
f"Cannot decode SQAMetric because {metric_sqa.intent} "
f"is an invalid intent.")
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 testConstraintValidation(self):
# 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 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 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 metric_from_sqa(
self, metric_sqa: SQAMetric
) -> Union[Metric, Objective, OutcomeConstraint]:
"""Convert SQLAlchemy Metric to Ax Metric, Objective, or OutcomeConstraint."""
metric = self.metric_from_sqa_util(metric_sqa)
if metric_sqa.intent == MetricIntent.TRACKING:
return metric
elif metric_sqa.intent == MetricIntent.OBJECTIVE:
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Objective because minimize is None."
)
if metric_sqa.scalarized_objective_weight is not None:
raise SQADecodeError( # pragma: no cover
"The metric corresponding to regular objective does not \
have weight attribute")
return Objective(metric=metric, minimize=metric_sqa.minimize)
elif (metric_sqa.intent == MetricIntent.MULTI_OBJECTIVE
): # metric_sqa is a parent whose children are individual
# metrics in MultiObjective
try:
metrics_sqa_children = metric_sqa.scalarized_objective_children_metrics
except DetachedInstanceError:
metrics_sqa_children = _get_scalarized_objective_children_metrics(
metric_id=metric_sqa.id, decoder=self)
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to MultiObjective \
because the parent metric has no children metrics.")
# Extracting metric and weight for each child
objectives = [
Objective(
metric=self.metric_from_sqa_util(metric_sqa),
minimize=metric_sqa.minimize,
) for metric_sqa in metrics_sqa_children
]
multi_objective = MultiObjective(objectives=objectives)
multi_objective.db_id = metric_sqa.id
return multi_objective
elif (metric_sqa.intent == MetricIntent.SCALARIZED_OBJECTIVE
): # metric_sqa is a parent whose children are individual
# metrics in Scalarized Objective
if metric_sqa.minimize is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized Objective \
because minimize is None.")
try:
metrics_sqa_children = metric_sqa.scalarized_objective_children_metrics
except DetachedInstanceError:
metrics_sqa_children = _get_scalarized_objective_children_metrics(
metric_id=metric_sqa.id, decoder=self)
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized Objective \
because the parent metric has no children metrics.")
# Extracting metric and weight for each child
metrics, weights = zip(*[(
self.metric_from_sqa_util(child),
child.scalarized_objective_weight,
) for child in metrics_sqa_children])
scalarized_objective = ScalarizedObjective(
metrics=list(metrics),
weights=list(weights),
minimize=not_none(metric_sqa.minimize),
)
scalarized_objective.db_id = metric_sqa.id
return scalarized_objective
elif metric_sqa.intent == MetricIntent.OUTCOME_CONSTRAINT:
if (metric_sqa.bound is None or metric_sqa.op is None
or metric_sqa.relative is None):
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to OutcomeConstraint because "
"bound, op, or relative is None.")
return OutcomeConstraint(
metric=metric,
bound=metric_sqa.bound,
op=metric_sqa.op,
relative=metric_sqa.relative,
)
elif metric_sqa.intent == MetricIntent.SCALARIZED_OUTCOME_CONSTRAINT:
if (metric_sqa.bound is None or metric_sqa.op is None
or metric_sqa.relative is None):
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized OutcomeConstraint because "
"bound, op, or relative is None.")
try:
metrics_sqa_children = (
metric_sqa.scalarized_outcome_constraint_children_metrics)
except DetachedInstanceError:
metrics_sqa_children = (
_get_scalarized_outcome_constraint_children_metrics(
metric_id=metric_sqa.id, decoder=self))
if metrics_sqa_children is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to Scalarized OutcomeConstraint \
because the parent metric has no children metrics.")
# Extracting metric and weight for each child
metrics, weights = zip(*[(
self.metric_from_sqa_util(child),
child.scalarized_outcome_constraint_weight,
) for child in metrics_sqa_children])
scalarized_outcome_constraint = ScalarizedOutcomeConstraint(
metrics=list(metrics),
weights=list(weights),
bound=not_none(metric_sqa.bound),
op=not_none(metric_sqa.op),
relative=not_none(metric_sqa.relative),
)
scalarized_outcome_constraint.db_id = metric_sqa.id
return scalarized_outcome_constraint
elif metric_sqa.intent == MetricIntent.OBJECTIVE_THRESHOLD:
if metric_sqa.bound is None or metric_sqa.relative is None:
raise SQADecodeError( # pragma: no cover
"Cannot decode SQAMetric to ObjectiveThreshold because "
"bound, op, or relative is None.")
ot = ObjectiveThreshold(
metric=metric,
bound=metric_sqa.bound,
relative=metric_sqa.relative,
op=metric_sqa.op,
)
# ObjectiveThreshold constructor clones the passed-in metric, which means
# the db id gets lost and so we need to reset it
ot.metric._db_id = metric.db_id
return ot
else:
raise SQADecodeError(
f"Cannot decode SQAMetric because {metric_sqa.intent} "
f"is an invalid intent.")
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)