def setUp(self) -> None:
self.experiment = SimpleExperiment(
name="test_branin",
search_space=get_branin_search_space(),
evaluation_function=sum_evaluation_function,
objective_name="sum",
)
self.arms = [
Arm(parameters={
"x1": 0.75,
"x2": 1
}),
Arm(parameters={
"x1": 2,
"x2": 7
}),
Arm(parameters={
"x1": 10,
"x2": 8
}),
Arm(parameters={
"x1": -2,
"x2": 10
}),
]
def _init_experiment_from_sqa(self, experiment_sqa: SQAExperiment) -> Experiment:
"""First step of conversion within experiment_from_sqa."""
opt_config, tracking_metrics = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=experiment_sqa.metrics
)
search_space = self.search_space_from_sqa(
parameters_sqa=experiment_sqa.parameters,
parameter_constraints_sqa=experiment_sqa.parameter_constraints,
)
if search_space is None:
raise SQADecodeError( # pragma: no cover
"Experiment SearchSpace cannot be None."
)
status_quo = (
Arm(
parameters=experiment_sqa.status_quo_parameters,
name=experiment_sqa.status_quo_name,
)
if experiment_sqa.status_quo_parameters is not None
else None
)
if len(experiment_sqa.runners) == 0:
runner = None
elif len(experiment_sqa.runners) == 1:
runner = self.runner_from_sqa(experiment_sqa.runners[0])
else:
raise ValueError( # pragma: no cover
"Multiple runners on experiment "
"only supported for MultiTypeExperiment."
)
subclass = (experiment_sqa.properties or {}).get("subclass")
if subclass == "SimpleExperiment":
if opt_config is None:
raise SQADecodeError( # pragma: no cover
"SimpleExperiment must have an optimization config."
)
experiment = SimpleExperiment(
name=experiment_sqa.name,
search_space=search_space,
objective_name=opt_config.objective.metric.name,
minimize=opt_config.objective.minimize,
outcome_constraints=opt_config.outcome_constraints,
status_quo=status_quo,
)
experiment.description = experiment_sqa.description
experiment.is_test = experiment_sqa.is_test
else:
experiment = Experiment(
name=experiment_sqa.name,
description=experiment_sqa.description,
search_space=search_space,
optimization_config=opt_config,
tracking_metrics=tracking_metrics,
runner=runner,
status_quo=status_quo,
is_test=experiment_sqa.is_test,
)
return experiment
def testDeprecation(self) -> None:
with patch.object(warnings, "warn") as mock_warn:
SimpleExperiment(
name="test_branin",
search_space=get_branin_search_space(),
objective_name="sum",
)
mock_warn.assert_called_once()
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 = SimpleExperiment(
name="test",
search_space=hartmann_search_space,
evaluation_function=hartmann_evaluation_function,
objective_name="hartmann6",
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,
)
],
)
# 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))
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))
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))
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))
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 experiment_from_sqa(self, experiment_sqa: SQAExperiment) -> Experiment:
"""Convert SQLAlchemy Experiment to Ax Experiment."""
opt_config, tracking_metrics = self.opt_config_and_tracking_metrics_from_sqa(
metrics_sqa=experiment_sqa.metrics)
search_space = self.search_space_from_sqa(
parameters_sqa=experiment_sqa.parameters,
parameter_constraints_sqa=experiment_sqa.parameter_constraints,
)
if search_space is None:
raise SQADecodeError( # pragma: no cover
"Experiment SearchSpace cannot be None.")
runner = (self.runner_from_sqa(experiment_sqa.runner)
if experiment_sqa.runner else None)
status_quo = (Arm(
parameters=experiment_sqa.status_quo_parameters,
name=experiment_sqa.status_quo_name,
) if experiment_sqa.status_quo_parameters is not None else None)
if (experiment_sqa.properties is not None
and experiment_sqa.properties.get("subclass")
== "SimpleExperiment"):
if opt_config is None:
raise SQADecodeError( # pragma: no cover
"SimpleExperiment must have an optimization config.")
experiment = SimpleExperiment(
name=experiment_sqa.name,
search_space=search_space,
objective_name=opt_config.objective.metric.name,
minimize=opt_config.objective.minimize,
outcome_constraints=opt_config.outcome_constraints,
status_quo=status_quo,
)
experiment.description = experiment_sqa.description
experiment.is_test = experiment_sqa.is_test
else:
experiment = Experiment(
name=experiment_sqa.name,
description=experiment_sqa.description,
search_space=search_space,
optimization_config=opt_config,
tracking_metrics=tracking_metrics,
runner=runner,
status_quo=status_quo,
is_test=experiment_sqa.is_test,
)
trials = [
self.trial_from_sqa(trial_sqa=trial, experiment=experiment)
for trial in experiment_sqa.trials
]
data_by_trial = defaultdict(dict)
for data_sqa in experiment_sqa.data:
trial_index = data_sqa.trial_index
timestamp = data_sqa.time_created
data_by_trial[trial_index][timestamp] = self.data_from_sqa(
data_sqa=data_sqa)
data_by_trial = {
trial_index: OrderedDict(sorted(data_by_timestamp.items()))
for trial_index, data_by_timestamp in data_by_trial.items()
}
experiment._trials = {trial.index: trial for trial in trials}
for trial in trials:
for arm in trial.arms:
experiment._arms_by_signature[arm.signature] = arm
if experiment.status_quo is not None:
sq_sig = experiment.status_quo.signature
experiment._arms_by_signature[sq_sig] = experiment.status_quo
experiment._time_created = experiment_sqa.time_created
experiment._experiment_type = self.get_enum_name(
value=experiment_sqa.experiment_type,
enum=self.config.experiment_type_enum)
experiment._data_by_trial = dict(data_by_trial)
return experiment
