def testExtractSearchSpaceDigest(self):
search_space_digest = extract_search_space_digest(
self.search_space, ["x", "y", "z"])
self.assertEqual(search_space_digest.bounds, [(0.0, 1.0), (1.0, 2.0),
(0.0, 5.0)])
self.assertEqual(search_space_digest.fidelity_features, [1])
self.assertEqual(search_space_digest.task_features, [])
self.assertEqual(search_space_digest.target_fidelities, {1: 2.0})
search_space_digest = extract_search_space_digest(
self.search_space, ["x", "z"])
self.assertEqual(search_space_digest.target_fidelities, {})
# Test that task param is treated as task feature
search_space = SearchSpace(self.parameters)
search_space._parameters["x"] = ChoiceParameter(
"x",
ParameterType.INT,
values=[1, 4, 5],
is_task=True,
)
search_space_digest = extract_search_space_digest(
search_space, ["x", "y", "z"])
self.assertEqual(search_space_digest.task_features, [0])
# Test validation
search_space._parameters["x"] = RangeParameter("x",
ParameterType.FLOAT,
lower=1.0,
upper=4.0,
log_scale=True)
with self.assertRaises(ValueError):
extract_search_space_digest(search_space, ["x", "y", "z"])
def _fit(
self,
model: Any,
search_space: SearchSpace,
observation_features: List[ObservationFeatures],
observation_data: List[ObservationData],
) -> None:
# Convert observations to arrays
self.parameters = list(search_space.parameters.keys())
all_metric_names: Set[str] = set()
for od in observation_data:
all_metric_names.update(od.metric_names)
self.outcomes = sorted(all_metric_names) # Deterministic order
# Convert observations to arrays
Xs_array, Ys_array, Yvars_array, candidate_metadata = _convert_observations(
observation_data=observation_data,
observation_features=observation_features,
outcomes=self.outcomes,
parameters=self.parameters,
)
# Get all relevant information on the parameters
search_space_digest = extract_search_space_digest(
search_space=search_space, param_names=self.parameters
)
# Fit
self._model_fit(
model=model,
Xs=Xs_array,
Ys=Ys_array,
Yvars=Yvars_array,
search_space_digest=search_space_digest,
metric_names=self.outcomes,
candidate_metadata=candidate_metadata,
)
def _cross_validate(
self,
search_space: SearchSpace,
obs_feats: List[ObservationFeatures],
obs_data: List[ObservationData],
cv_test_points: List[ObservationFeatures],
) -> List[ObservationData]:
"""Make predictions at cv_test_points using only the data in obs_feats
and obs_data.
"""
Xs_train, Ys_train, Yvars_train, candidate_metadata = _convert_observations(
observation_data=obs_data,
observation_features=obs_feats,
outcomes=self.outcomes,
parameters=self.parameters,
)
search_space_digest = extract_search_space_digest(
search_space=search_space, param_names=self.parameters
)
X_test = np.array(
[[obsf.parameters[p] for p in self.parameters] for obsf in cv_test_points]
)
# Use the model to do the cross validation
f_test, cov_test = self._model_cross_validate(
Xs_train=Xs_train,
Ys_train=Ys_train,
Yvars_train=Yvars_train,
X_test=X_test,
search_space_digest=search_space_digest,
metric_names=self.outcomes,
)
# Convert array back to ObservationData
return array_to_observation_data(f=f_test, cov=cov_test, outcomes=self.outcomes)
def _update(
self,
search_space: SearchSpace,
observation_features: List[ObservationFeatures],
observation_data: List[ObservationData],
) -> None:
"""Apply terminal transform for update data, and pass along to model."""
Xs_array, Ys_array, Yvars_array, candidate_metadata = _convert_observations(
observation_data=observation_data,
observation_features=observation_features,
outcomes=self.outcomes,
parameters=self.parameters,
)
search_space_digest = extract_search_space_digest(
search_space=search_space, param_names=self.parameters
)
# Update in-design status for these new points.
self._model_update(
Xs=Xs_array,
Ys=Ys_array,
Yvars=Yvars_array,
search_space_digest=search_space_digest,
metric_names=self.outcomes,
candidate_metadata=candidate_metadata,
)
def _gen(
self,
n: int,
search_space: SearchSpace,
pending_observations: Dict[str, List[ObservationFeatures]],
fixed_features: ObservationFeatures,
optimization_config: Optional[OptimizationConfig],
model_gen_options: Optional[TConfig],
) -> Tuple[List[ObservationFeatures], List[float],
Optional[ObservationFeatures], TGenMetadata, ]:
"""Generate new candidates according to a search_space."""
# Extract parameter values
search_space_digest = extract_search_space_digest(
search_space, self.parameters)
# Get fixed features
fixed_features_dict = get_fixed_features(fixed_features,
self.parameters)
# Extract param constraints
linear_constraints = extract_parameter_constraints(
search_space.parameter_constraints, self.parameters)
# Generate the candidates
X, w = self.model.gen(
n=n,
bounds=search_space_digest.bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features_dict,
model_gen_options=model_gen_options,
rounding_func=transform_callback(self.parameters, self.transforms),
)
observation_features = parse_observation_features(X, self.parameters)
return observation_features, w.tolist(), None, {}
def _get_transformed_model_gen_args(
self,
search_space: SearchSpace,
pending_observations: Dict[str, List[ObservationFeatures]],
fixed_features: ObservationFeatures,
model_gen_options: Optional[TConfig] = None,
optimization_config: Optional[OptimizationConfig] = None,
) -> ArrayModelGenArgs:
# Validation
if not self.parameters: # pragma: no cover
raise ValueError(FIT_MODEL_ERROR.format(action="_gen"))
# Extract search space info
search_space_digest = extract_search_space_digest(
search_space=search_space, param_names=self.parameters
)
if optimization_config is None:
raise ValueError(
"ArrayModelBridge requires an OptimizationConfig to be specified"
)
if self.outcomes is None or len(self.outcomes) == 0: # pragma: no cover
raise ValueError("No outcomes found during model fit--data are missing.")
validate_optimization_config(optimization_config, self.outcomes)
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,
)
extra_model_gen_kwargs = self._get_extra_model_gen_kwargs(
optimization_config=optimization_config
)
linear_constraints = extract_parameter_constraints(
search_space.parameter_constraints, self.parameters
)
fixed_features_dict = get_fixed_features(fixed_features, self.parameters)
pending_array = pending_observations_as_array(
pending_observations, self.outcomes, self.parameters
)
return ArrayModelGenArgs(
search_space_digest=search_space_digest,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features_dict,
pending_observations=pending_array,
rounding_func=transform_callback(self.parameters, self.transforms),
extra_model_gen_kwargs=extra_model_gen_kwargs,
)
def _gen(
self,
n: int,
search_space: SearchSpace,
pending_observations: Dict[str, List[ObservationFeatures]],
fixed_features: ObservationFeatures,
model_gen_options: Optional[TConfig] = None,
optimization_config: Optional[OptimizationConfig] = None,
) -> Tuple[
List[ObservationFeatures],
List[float],
Optional[ObservationFeatures],
TGenMetadata,
]:
"""Generate new candidates according to search_space and
optimization_config.
The outcome constraints should be transformed to no longer be relative.
"""
# Validation
if not self.parameters: # pragma: no cover
raise ValueError(FIT_MODEL_ERROR.format(action="_gen"))
# Extract search space info
search_space_digest = extract_search_space_digest(
search_space=search_space, param_names=self.parameters
)
if optimization_config is None:
raise ValueError(
"ArrayModelBridge requires an OptimizationConfig to be specified"
)
if self.outcomes is None or len(self.outcomes) == 0: # pragma: no cover
raise ValueError("No outcomes found during model fit--data are missing.")
validate_optimization_config(optimization_config, self.outcomes)
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,
)
extra_model_gen_kwargs = self._get_extra_model_gen_kwargs(
optimization_config=optimization_config
)
linear_constraints = extract_parameter_constraints(
search_space.parameter_constraints, self.parameters
)
fixed_features_dict = get_fixed_features(fixed_features, self.parameters)
pending_array = pending_observations_as_array(
pending_observations, self.outcomes, self.parameters
)
# Generate the candidates
X, w, gen_metadata, candidate_metadata = self._model_gen(
n=n,
bounds=search_space_digest.bounds,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features_dict,
pending_observations=pending_array,
model_gen_options=model_gen_options,
rounding_func=transform_callback(self.parameters, self.transforms),
target_fidelities=search_space_digest.target_fidelities,
**extra_model_gen_kwargs,
)
# Transform array to observations
observation_features = parse_observation_features(
X=X, param_names=self.parameters, candidate_metadata=candidate_metadata
)
xbest = self._model_best_point(
bounds=search_space_digest.bounds,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
linear_constraints=linear_constraints,
fixed_features=fixed_features_dict,
model_gen_options=model_gen_options,
target_fidelities=search_space_digest.target_fidelities,
)
best_obsf = (
None
if xbest is None
else ObservationFeatures(
parameters={p: float(xbest[i]) for i, p in enumerate(self.parameters)}
)
)
return observation_features, w.tolist(), best_obsf, gen_metadata