def test_pareto_frontier(self, _):
exp = get_branin_experiment_with_multi_objective(
has_optimization_config=True, with_batch=True)
for trial in exp.trials.values():
trial.mark_running(no_runner_required=True).mark_completed()
metrics_dict = exp.optimization_config.metrics
objective_thresholds = [
ObjectiveThreshold(
metric=metrics_dict["branin_a"],
bound=0.0,
relative=False,
op=ComparisonOp.GEQ,
),
ObjectiveThreshold(
metric=metrics_dict["branin_b"],
bound=0.0,
relative=False,
op=ComparisonOp.GEQ,
),
]
exp.optimization_config = exp.optimization_config.clone_with_args(
objective_thresholds=objective_thresholds)
exp.attach_data(
get_branin_data_multi_objective(trial_indices=exp.trials.keys()))
modelbridge = MultiObjectiveTorchModelBridge(
search_space=exp.search_space,
model=MultiObjectiveBotorchModel(),
optimization_config=exp.optimization_config,
transforms=[t1, t2],
experiment=exp,
data=exp.fetch_data(),
objective_thresholds=objective_thresholds,
)
with patch(
PARETO_FRONTIER_EVALUATOR_PATH,
wraps=pareto_frontier_evaluator) as wrapped_frontier_evaluator:
modelbridge.model.frontier_evaluator = wrapped_frontier_evaluator
observed_frontier = observed_pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds)
wrapped_frontier_evaluator.assert_called_once()
self.assertIsNone(wrapped_frontier_evaluator.call_args.kwargs["X"])
self.assertEqual(1, len(observed_frontier))
self.assertEqual(observed_frontier[0].arm_name, "0_0")
with self.assertRaises(ValueError):
predicted_pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=[],
)
predicted_frontier = predicted_pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=None,
)
self.assertEqual(predicted_frontier[0].arm_name, "0_0")
observation_features = [
ObservationFeatures(parameters={
"x1": 0.0,
"x2": 1.0
}),
ObservationFeatures(parameters={
"x1": 1.0,
"x2": 0.0
}),
]
observation_data = [
ObservationData(
metric_names=["branin_b", "branin_a"],
means=np.array([1.0, 2.0]),
covariance=np.array([[1.0, 2.0], [3.0, 4.0]]),
),
ObservationData(
metric_names=["branin_a", "branin_b"],
means=np.array([3.0, 4.0]),
covariance=np.array([[1.0, 2.0], [3.0, 4.0]]),
),
]
predicted_frontier = predicted_pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=observation_features,
)
self.assertTrue(len(predicted_frontier) <= 2)
self.assertIsNone(predicted_frontier[0].arm_name, None)
with patch(
PARETO_FRONTIER_EVALUATOR_PATH,
wraps=pareto_frontier_evaluator) as wrapped_frontier_evaluator:
observed_frontier = pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=observation_features,
observation_data=observation_data,
)
wrapped_frontier_evaluator.assert_called_once()
self.assertTrue(
torch.equal(
wrapped_frontier_evaluator.call_args.kwargs["X"],
torch.tensor([[1.0, 4.0], [4.0, 1.0]]),
))
with patch(
PARETO_FRONTIER_EVALUATOR_PATH,
wraps=pareto_frontier_evaluator) as wrapped_frontier_evaluator:
observed_frontier = pareto_frontier(
modelbridge=modelbridge,
objective_thresholds=objective_thresholds,
observation_features=observation_features,
observation_data=observation_data,
use_model_predictions=False,
)
wrapped_frontier_evaluator.assert_called_once()
self.assertIsNone(wrapped_frontier_evaluator.call_args.kwargs["X"])
self.assertTrue(
torch.equal(
wrapped_frontier_evaluator.call_args.kwargs["Y"],
torch.tensor([[9.0, 4.0], [16.0, 25.0]]),
))
def get_observed_pareto_frontiers(
experiment: Experiment,
data: Optional[Data] = None,
rel: bool = True,
) -> List[ParetoFrontierResults]:
"""
Find all Pareto points from an experiment.
Uses only values as observed in the data; no modeling is involved. Makes no
assumption about the search space or types of parameters. If "data" is provided will
use that, otherwise will use all data attached to the experiment.
Uses all arms present in data; does not filter according to experiment
search space.
Assumes experiment has a multiobjective optimization config from which the
objectives and outcome constraints will be extracted.
Will generate a ParetoFrontierResults for every pair of metrics in the experiment's
multiobjective optimization config.
"""
if data is None:
data = experiment.fetch_data()
if experiment.optimization_config is None:
raise ValueError("Experiment must have an optimization config")
mb = get_tensor_converter_model(experiment=experiment, data=data)
pareto_observations = observed_pareto_frontier(modelbridge=mb)
# Convert to ParetoFrontierResults
metric_names = [
metric.name for metric in
experiment.optimization_config.objective.metrics # pyre-ignore
]
pfr_means = {name: [] for name in metric_names}
pfr_sems = {name: [] for name in metric_names}
for obs in pareto_observations:
for i, name in enumerate(obs.data.metric_names):
pfr_means[name].append(obs.data.means[i])
pfr_sems[name].append(np.sqrt(obs.data.covariance[i, i]))
# Relativize as needed
if rel and experiment.status_quo is not None:
# Get status quo values
sq_df = data.df[data.df["arm_name"] ==
experiment.status_quo.name # pyre-ignore
]
sq_df = sq_df.to_dict(orient="list") # pyre-ignore
sq_means = {}
sq_sems = {}
for i, metric in enumerate(sq_df["metric_name"]):
sq_means[metric] = sq_df["mean"][i]
sq_sems[metric] = sq_df["sem"][i]
# Relativize
for name in metric_names:
if np.isnan(sq_sems[name]) or np.isnan(pfr_sems[name]).any():
# Just relativize means
pfr_means[name] = [(mu / sq_means[name] - 1) * 100
for mu in pfr_means[name]]
else:
# Use delta method
pfr_means[name], pfr_sems[name] = relativize(
means_t=pfr_means[name],
sems_t=pfr_sems[name],
mean_c=sq_means[name],
sem_c=sq_sems[name],
as_percent=True,
)
absolute_metrics = []
else:
absolute_metrics = metric_names
objective_thresholds = {}
if experiment.optimization_config.objective_thresholds is not None: # pyre-ignore
for objth in experiment.optimization_config.objective_thresholds:
is_rel = objth.metric.name not in absolute_metrics
if objth.relative != is_rel:
raise ValueError(
f"Objective threshold for {objth.metric.name} has "
f"rel={objth.relative} but was specified here as rel={is_rel}"
)
objective_thresholds[objth.metric.name] = objth.bound
# Construct ParetoFrontResults for each pair
pfr_list = []
param_dicts = [obs.features.parameters for obs in pareto_observations]
arm_names = [obs.arm_name for obs in pareto_observations]
for metric_a, metric_b in combinations(metric_names, 2):
pfr_list.append(
ParetoFrontierResults(
param_dicts=param_dicts,
means=pfr_means,
sems=pfr_sems,
primary_metric=metric_a,
secondary_metric=metric_b,
absolute_metrics=absolute_metrics,
objective_thresholds=objective_thresholds,
arm_names=arm_names,
))
return pfr_list
