def fit(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
search_space_digest: SearchSpaceDigest,
metric_names: List[str],
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
) -> None:
self.dtype = Xs[0].dtype
self.device = Xs[0].device
self.Xs = Xs
self.Ys = Ys
self.Yvars = Yvars
self.task_features = normalize_indices(
search_space_digest.task_features, d=Xs[0].size(-1))
self.fidelity_features = normalize_indices(
search_space_digest.fidelity_features, d=Xs[0].size(-1))
self.metric_names = metric_names
self.model = self.model_constructor( # pyre-ignore [28]
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
task_features=self.task_features,
fidelity_features=self.fidelity_features,
metric_names=self.metric_names,
use_input_warping=self.use_input_warping,
use_loocv_pseudo_likelihood=self.use_loocv_pseudo_likelihood,
**self._kwargs,
)
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],
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
) -> None:
self.dtype = Xs[0].dtype
self.device = Xs[0].device
self.Xs = Xs
self.Ys = Ys
self.Yvars = Yvars
# ensure indices are non-negative
self.task_features = normalize_indices(task_features, d=Xs[0].size(-1))
self.fidelity_features = normalize_indices(fidelity_features, d=Xs[0].size(-1))
self.metric_names = metric_names
self.model = self.model_constructor( # pyre-ignore [28]
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
task_features=self.task_features,
fidelity_features=self.fidelity_features,
metric_names=self.metric_names,
**self._kwargs,
)
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],
fidelity_features: List[int],
) -> None:
self.dtype = Xs[0].dtype
self.device = Xs[0].device
self.Xs = Xs
self.Ys = Ys
self.Yvars = Yvars
# ensure indices are non-negative
self.task_features = normalize_indices(task_features, d=Xs[0].size(-1))
self.fidelity_features = normalize_indices(fidelity_features,
d=Xs[0].size(-1))
kwargs = self._kwargs
if len(self.fidelity_features) == 0:
# only pass linear_truncated arg if there are fidelities
self._kwargs = {
k: v
for k, v in kwargs.items() if k != "linear_truncated"
}
self.model = self.model_constructor( # pyre-ignore [28]
Xs=Xs,
Ys=Ys,
Yvars=Yvars,
task_features=self.task_features,
fidelity_features=self.fidelity_features,
**self._kwargs,
)
def testNormalizeIndices(self):
indices = [0, 2]
nlzd_indices = normalize_indices(indices, 3)
self.assertEqual(nlzd_indices, indices)
nlzd_indices = normalize_indices(indices, 4)
self.assertEqual(nlzd_indices, indices)
indices = [0, -1]
nlzd_indices = normalize_indices(indices, 3)
self.assertEqual(nlzd_indices, [0, 2])
with self.assertRaises(ValueError):
nlzd_indices = normalize_indices([3], 3)
with self.assertRaises(ValueError):
nlzd_indices = normalize_indices([-4], 3)
