def __init__(
self,
experiment: Experiment,
search_space: SearchSpace,
data: Data,
model: TorchModel,
transforms: List[Type[Transform]],
transform_configs: Optional[Dict[str, TConfig]] = None,
torch_dtype: Optional[torch.dtype] = None, # noqa T484
torch_device: Optional[torch.device] = None,
status_quo_name: Optional[str] = None,
status_quo_features: Optional[ObservationFeatures] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
fit_out_of_design: bool = False,
objective_thresholds: Optional[TRefPoint] = None,
default_model_gen_options: Optional[TConfig] = None,
) -> None:
if (isinstance(experiment, MultiTypeExperiment)
and objective_thresholds is not None):
raise NotImplementedError(
"Objective threshold dependent multi-objective optimization algos "
"like EHVI are not yet supported for MultiTypeExperiments. "
"Remove the objective threshold arg and use a compatible algorithm "
"like ParEGO.")
self._objective_metric_names = None
# Optimization_config
mooc = optimization_config or checked_cast_optional(
MultiObjectiveOptimizationConfig, experiment.optimization_config)
# Extract objective_thresholds from optimization_config, or inject it.
if not mooc:
raise ValueError(
("experiment must have an existing optimization_config "
"of type MultiObjectiveOptimizationConfig "
"or `optimization_config` must be passed as an argument."))
if not isinstance(mooc, MultiObjectiveOptimizationConfig):
mooc = not_none(
MultiObjectiveOptimizationConfig.from_opt_conf(mooc))
if objective_thresholds:
mooc = mooc.clone_with_args(
objective_thresholds=objective_thresholds)
optimization_config = mooc
super().__init__(
experiment=experiment,
search_space=search_space,
data=data,
model=model,
transforms=transforms,
transform_configs=transform_configs,
torch_dtype=torch_dtype,
torch_device=torch_device,
status_quo_name=status_quo_name,
status_quo_features=status_quo_features,
optimization_config=optimization_config,
fit_out_of_design=fit_out_of_design,
default_model_gen_options=default_model_gen_options,
)
def _pareto_frontier(
self,
objective_thresholds: Optional[TRefPoint] = None,
observation_features: Optional[List[ObservationFeatures]] = None,
observation_data: Optional[List[ObservationData]] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
) -> List[ObservationData]:
# TODO(jej): This method should be refactored to move tensor
# conversions into a separate utility, and eventually should be
# moved into base.py.
# The reason this method is currently implemented in array.py is to
# allow the broadest possible set of models to call frontier and
# hypervolume evaluation functions given the current API.
X = (self.transform_observation_features(observation_features)
if observation_features else None)
X = self._array_to_tensor(X) if X is not None else None
Y, Yvar = (None, None)
if observation_data:
Y, Yvar = self.transform_observation_data(observation_data)
if Y is not None and Yvar is not None:
Y, Yvar = (self._array_to_tensor(Y), self._array_to_tensor(Yvar))
# Optimization_config
mooc = optimization_config or checked_cast_optional(
MultiObjectiveOptimizationConfig, self._optimization_config)
if not mooc:
raise ValueError(
("experiment must have an existing optimization_config "
"of type MultiObjectiveOptimizationConfig "
"or `optimization_config` must be passed as an argument."))
if not isinstance(mooc, MultiObjectiveOptimizationConfig):
mooc = not_none(
MultiObjectiveOptimizationConfig.from_opt_conf(mooc))
if objective_thresholds:
mooc = mooc.clone_with_args(
objective_thresholds=objective_thresholds)
optimization_config = mooc
# Transform OptimizationConfig.
optimization_config = self.transform_optimization_config(
optimization_config=optimization_config,
fixed_features=ObservationFeatures(parameters={}),
)
# Extract weights, constraints, and objective_thresholds
objective_weights = extract_objective_weights(
objective=optimization_config.objective, outcomes=self.outcomes)
outcome_constraints = extract_outcome_constraints(
outcome_constraints=optimization_config.outcome_constraints,
outcomes=self.outcomes,
)
objective_thresholds_arr = extract_objective_thresholds(
objective_thresholds=optimization_config.objective_thresholds,
outcomes=self.outcomes,
)
# Transform to tensors.
obj_w, oc_c, _, _ = validate_and_apply_final_transform(
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=None,
pending_observations=None,
final_transform=self._array_to_tensor,
)
obj_t = self._array_to_tensor(objective_thresholds_arr)
frontier_evaluator = self._get_frontier_evaluator()
# pyre-ignore[28]: Unexpected keyword `model` to anonymous call
f, cov = frontier_evaluator(
model=self.model,
X=X,
Y=Y,
Yvar=Yvar,
objective_thresholds=obj_t,
objective_weights=obj_w,
outcome_constraints=oc_c,
)
f, cov = f.detach().cpu().clone().numpy(), cov.detach().cpu().clone(
).numpy()
frontier_observation_data = array_to_observation_data(
f=f, cov=cov, outcomes=not_none(self.outcomes))
# Untransform observations
for t in reversed(self.transforms.values()): # noqa T484
frontier_observation_data = t.untransform_observation_data(
frontier_observation_data, [])
return frontier_observation_data
def get_pareto_frontier_and_transformed_configs(
modelbridge: modelbridge_module.array.ArrayModelBridge,
observation_features: List[ObservationFeatures],
observation_data: Optional[List[ObservationData]] = None,
objective_thresholds: Optional[TRefPoint] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
arm_names: Optional[List[Optional[str]]] = None,
use_model_predictions: bool = True,
) -> Tuple[List[Observation], Tensor, Tensor, Optional[Tensor]]:
"""Helper that applies transforms and calls frontier_evaluator.
Returns transformed configs in addition to the Pareto observations.
Args:
modelbridge: Modelbridge used to predict metrics outcomes.
observation_features: observation features to predict, if provided and
use_model_predictions is True.
observation_data: data for computing the Pareto front, unless features
are provided and model_predictions is True.
objective_thresholds: metric values bounding the region of interest in
the objective outcome space.
optimization_config: Optimization config.
arm_names: Arm names for each observation.
use_model_predictions: If True, will use model predictions at
observation_features to compute Pareto front, if provided. If False,
will use observation_data directly to compute Pareto front, regardless
of whether observation_features are provided.
Returns:
frontier_observations: Observations of points on the pareto frontier.
f: n x m tensor representation of the Pareto frontier values where n is the
length of frontier_observations and m is the number of metrics.
obj_w: m tensor of objective weights.
obj_t: m tensor of objective thresholds corresponding to Y, or None if no
objective thresholds used.
"""
array_to_tensor = partial(_array_to_tensor, modelbridge=modelbridge)
X = (modelbridge.transform_observation_features(observation_features)
if use_model_predictions else None)
X = array_to_tensor(X) if X is not None else None
Y, Yvar = (None, None)
if observation_data is not None:
Y, Yvar = modelbridge.transform_observation_data(observation_data)
Y, Yvar = (array_to_tensor(Y), array_to_tensor(Yvar))
if arm_names is None:
arm_names = [None] * len(observation_features)
# Optimization_config
mooc = optimization_config or checked_cast_optional(
MultiObjectiveOptimizationConfig, modelbridge._optimization_config)
if not mooc:
raise ValueError(
("Experiment must have an existing optimization_config "
"of type `MultiObjectiveOptimizationConfig` "
"or `optimization_config` must be passed as an argument."))
if not isinstance(mooc, MultiObjectiveOptimizationConfig):
mooc = not_none(MultiObjectiveOptimizationConfig.from_opt_conf(mooc))
if objective_thresholds:
mooc = mooc.clone_with_args(objective_thresholds=objective_thresholds)
optimization_config = mooc
# Transform OptimizationConfig.
optimization_config = modelbridge.transform_optimization_config(
optimization_config=optimization_config,
fixed_features=ObservationFeatures(parameters={}),
)
# Extract weights, constraints, and objective_thresholds
objective_weights = extract_objective_weights(
objective=optimization_config.objective, outcomes=modelbridge.outcomes)
outcome_constraints = extract_outcome_constraints(
outcome_constraints=optimization_config.outcome_constraints,
outcomes=modelbridge.outcomes,
)
obj_t = extract_objective_thresholds(
objective_thresholds=optimization_config.objective_thresholds,
objective=optimization_config.objective,
outcomes=modelbridge.outcomes,
)
obj_t = array_to_tensor(obj_t)
# Transform to tensors.
obj_w, oc_c, _, _, _ = validate_and_apply_final_transform(
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=None,
pending_observations=None,
final_transform=array_to_tensor,
)
frontier_evaluator = get_default_frontier_evaluator()
# pyre-ignore[28]: Unexpected keyword `modelbridge` to anonymous call
f, cov, indx = frontier_evaluator(
model=modelbridge.model,
X=X,
Y=Y,
Yvar=Yvar,
objective_thresholds=obj_t,
objective_weights=obj_w,
outcome_constraints=oc_c,
)
f, cov = f.detach().cpu().clone(), cov.detach().cpu().clone()
indx = indx.tolist()
frontier_observation_data = array_to_observation_data(
f=f.numpy(), cov=cov.numpy(), outcomes=not_none(modelbridge.outcomes))
# Untransform observations
for t in reversed(modelbridge.transforms.values()): # noqa T484
frontier_observation_data = t.untransform_observation_data(
frontier_observation_data, [])
# Construct observations
frontier_observations = []
for i, obsd in enumerate(frontier_observation_data):
frontier_observations.append(
Observation(
features=observation_features[indx[i]],
data=obsd,
arm_name=arm_names[indx[i]],
))
return frontier_observations, f, obj_w, obj_t
def pareto_frontier(
modelbridge: modelbridge_module.array.ArrayModelBridge,
objective_thresholds: Optional[TRefPoint] = None,
observation_features: Optional[List[ObservationFeatures]] = None,
observation_data: Optional[List[ObservationData]] = None,
optimization_config: Optional[MultiObjectiveOptimizationConfig] = None,
) -> List[ObservationData]:
"""Helper that applies transforms and calls frontier_evaluator."""
array_to_tensor = partial(_array_to_tensor, modelbridge=modelbridge)
X = (modelbridge.transform_observation_features(observation_features)
if observation_features else None)
X = array_to_tensor(X) if X is not None else None
Y, Yvar = (None, None)
if observation_data:
Y, Yvar = modelbridge.transform_observation_data(observation_data)
if Y is not None and Yvar is not None:
Y, Yvar = (array_to_tensor(Y), array_to_tensor(Yvar))
# Optimization_config
mooc = optimization_config or checked_cast_optional(
MultiObjectiveOptimizationConfig, modelbridge._optimization_config)
if not mooc:
raise ValueError(
("Experiment must have an existing optimization_config "
"of type `MultiObjectiveOptimizationConfig` "
"or `optimization_config` must be passed as an argument."))
if not isinstance(mooc, MultiObjectiveOptimizationConfig):
mooc = not_none(MultiObjectiveOptimizationConfig.from_opt_conf(mooc))
if objective_thresholds:
mooc = mooc.clone_with_args(objective_thresholds=objective_thresholds)
optimization_config = mooc
# Transform OptimizationConfig.
optimization_config = modelbridge.transform_optimization_config(
optimization_config=optimization_config,
fixed_features=ObservationFeatures(parameters={}),
)
# Extract weights, constraints, and objective_thresholds
objective_weights = extract_objective_weights(
objective=optimization_config.objective, outcomes=modelbridge.outcomes)
outcome_constraints = extract_outcome_constraints(
outcome_constraints=optimization_config.outcome_constraints,
outcomes=modelbridge.outcomes,
)
objective_thresholds_arr = extract_objective_thresholds(
objective_thresholds=optimization_config.objective_thresholds,
outcomes=modelbridge.outcomes,
)
# Transform to tensors.
obj_w, oc_c, _, _ = validate_and_apply_final_transform(
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=None,
pending_observations=None,
final_transform=array_to_tensor,
)
obj_t = array_to_tensor(objective_thresholds_arr)
frontier_evaluator = get_default_frontier_evaluator()
# pyre-ignore[28]: Unexpected keyword `modelbridge` to anonymous call
f, cov = frontier_evaluator(
model=modelbridge.model,
X=X,
Y=Y,
Yvar=Yvar,
objective_thresholds=obj_t,
objective_weights=obj_w,
outcome_constraints=oc_c,
)
f, cov = f.detach().cpu().clone().numpy(), cov.detach().cpu().clone(
).numpy()
frontier_observation_data = array_to_observation_data(
f=f, cov=cov, outcomes=not_none(modelbridge.outcomes))
# Untransform observations
for t in reversed(modelbridge.transforms.values()): # noqa T484
frontier_observation_data = t.untransform_observation_data(
frontier_observation_data, [])
return frontier_observation_data