def fit(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
bounds: List[Tuple[float, float]],
task_features: List[int],
feature_names: List[str],
metric_names: List[str],
fidelity_features: List[int],
target_fidelities: Optional[Dict[int, float]] = None,
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
) -> None:
# Ensure that parts of data all have equal lengths.
validate_data_format(Xs=Xs,
Ys=Ys,
Yvars=Yvars,
metric_names=metric_names)
# Choose `Surrogate` and undelying `Model` based on properties of data.
if not self._surrogate:
self._autoset_surrogate(
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
task_features=task_features,
fidelity_features=fidelity_features,
metric_names=metric_names,
)
if isinstance(self.surrogate, ListSurrogate):
training_data = construct_training_data_list(Xs=Xs,
Ys=Ys,
Yvars=Yvars)
else:
training_data = construct_single_training_data(Xs=Xs,
Ys=Ys,
Yvars=Yvars)
# Fit the model.
if self.surrogate_fit_options.get(
Keys.REFIT_ON_UPDATE,
True) and not self.surrogate_fit_options.get(
Keys.WARM_START_REFITTING, True):
self.surrogate_fit_options[Keys.STATE_DICT] = None
self.surrogate.fit(
# pyre-ignore[6]: Base `Surrogate` expects only single `TrainingData`,
# but `ListSurrogate` expects a list of them, to `training_data` here is
# a union of the two.
training_data=training_data,
bounds=bounds,
task_features=task_features,
feature_names=feature_names,
fidelity_features=fidelity_features,
target_fidelities=target_fidelities,
metric_names=metric_names,
candidate_metadata=candidate_metadata,
state_dict=self.surrogate_fit_options.get(Keys.STATE_DICT, None),
refit=self.surrogate_fit_options.get(Keys.REFIT_ON_UPDATE, True),
)
def fit(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
bounds: List[Tuple[float, float]],
task_features: List[int],
feature_names: List[str],
metric_names: List[str],
fidelity_features: List[int],
target_fidelities: Optional[Dict[int, float]] = None,
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
state_dict: Optional[Dict[str, Tensor]] = None,
refit: bool = True,
) -> None:
# Ensure that parts of data all have equal lengths.
validate_data_format(Xs=Xs,
Ys=Ys,
Yvars=Yvars,
metric_names=metric_names)
# Choose `Surrogate` and undelying `Model` based on properties of data.
if not self._surrogate:
self._autoset_surrogate(
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
task_features=task_features,
fidelity_features=fidelity_features,
metric_names=metric_names,
)
self.surrogate.fit(
# pyre-ignore[6]: Base `Surrogate` expects only single `TrainingData`,
# but `ListSurrogate` expects a list of them, so `training_data` here is
# a union of the two.
training_data=self._mk_training_data(Xs=Xs, Ys=Ys, Yvars=Yvars),
bounds=bounds,
task_features=task_features,
feature_names=feature_names,
fidelity_features=fidelity_features,
target_fidelities=target_fidelities,
metric_names=metric_names,
candidate_metadata=candidate_metadata,
state_dict=state_dict,
refit=refit,
)
def fit(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
search_space_digest: SearchSpaceDigest,
metric_names: List[str],
target_fidelities: Optional[Dict[int, float]] = None,
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
state_dict: Optional[Dict[str, Tensor]] = None,
refit: bool = True,
) -> None:
# Ensure that parts of data all have equal lengths.
validate_data_format(Xs=Xs,
Ys=Ys,
Yvars=Yvars,
metric_names=metric_names)
# store search space info for later use (e.g. during generation)
self._search_space_digest = search_space_digest
# Choose `Surrogate` and undelying `Model` based on properties of data.
if not self._surrogate:
self._autoset_surrogate(
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
search_space_digest=search_space_digest,
metric_names=metric_names,
)
self.surrogate.fit(
# pyre-ignore[6]: Base `Surrogate` expects only single `TrainingData`,
# but `ListSurrogate` expects a list of them, so `training_data` here is
# a union of the two.
training_data=self._mk_training_data(Xs=Xs, Ys=Ys, Yvars=Yvars),
search_space_digest=search_space_digest,
metric_names=metric_names,
candidate_metadata=candidate_metadata,
state_dict=state_dict,
refit=refit,
)
def fit(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
bounds: List[Tuple[float, float]],
task_features: List[int],
feature_names: List[str],
metric_names: List[str],
fidelity_features: List[int],
target_fidelities: Optional[Dict[int, float]] = None,
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
) -> None:
# Ensure that parts of data all have equal lengths.
validate_data_format(Xs=Xs,
Ys=Ys,
Yvars=Yvars,
metric_names=metric_names)
# Choose `Surrogate` and undelying `Model` based on properties of data.
if not self._surrogate:
model_class = choose_model_class(
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
task_features=task_features,
fidelity_features=fidelity_features,
)
mll_class = choose_mll_class(
model_class=model_class,
state_dict=self.surrogate_fit_options.get(
Keys.STATE_DICT, None),
refit=self.surrogate_fit_options.get(Keys.REFIT_ON_UPDATE,
True),
)
self._surrogate = Surrogate(botorch_model_class=model_class,
mll_class=mll_class)
# Construct `TrainingData` based on properties of data and type of `Model`.
training_data = construct_training_data(
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
model_class=self.surrogate.botorch_model_class)
# Fit the model.
if self.surrogate_fit_options.get(
Keys.REFIT_ON_UPDATE,
True) and not self.surrogate_fit_options.get(
Keys.WARM_START_REFITTING, True):
self.surrogate_fit_options[Keys.STATE_DICT] = None
self.surrogate.fit(
training_data=training_data,
bounds=bounds,
task_features=task_features,
feature_names=feature_names,
fidelity_features=fidelity_features,
target_fidelities=target_fidelities,
metric_names=metric_names,
candidate_metadata=candidate_metadata,
state_dict=self.surrogate_fit_options.get(Keys.STATE_DICT, None),
refit=self.surrogate_fit_options.get(Keys.REFIT_ON_UPDATE, True),
)
