#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from typing import Dict, List, Optional, Tuple
import torch
from ax.exceptions.model import ModelError
from ax.models.model_utils import filter_constraints_and_fixed_features, get_observed
from botorch.models import ModelListGP, SingleTaskGP
from botorch.models.model import Model
from torch import Tensor
NOISELESS_MODELS = {SingleTaskGP} # pyre-ignore [16]
[docs]def is_noiseless(model: Model) -> bool:
"""Check if a given (single-task) botorch model is noiseless"""
if isinstance(model, ModelListGP): # pyre-ignore: [16]
raise ModelError(
"Checking for noisless models only applies to sub-models of ModelListGP"
)
return model.__class__ in NOISELESS_MODELS
def _filter_X_observed(
Xs: List[Tensor],
objective_weights: Tensor,
bounds: List[Tuple[float, float]],
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
) -> Optional[Tensor]:
r"""Filter input points to those appearing in objective or constraints.
Args:
Xs: The input tensors of a model.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
bounds: A list of (lower, upper) tuples for each column of X.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b. (Not used by single task models)
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b. (Not used by single task models)
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
Returns:
Tensor: All points that are feasible and appear in the objective or
the constraints. None if there are no such points.
"""
# Get points observed for all objective and constraint outcomes
X_obs = get_observed(
Xs=Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
# Filter to those that satisfy constraints.
X_obs = filter_constraints_and_fixed_features(
X=X_obs,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
if len(X_obs) > 0:
return torch.as_tensor(X_obs) # please the linter
def _get_X_pending_and_observed(
Xs: List[Tensor],
objective_weights: Tensor,
bounds: List[Tuple[float, float]],
pending_observations: Optional[List[Tensor]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
) -> Tuple[Optional[Tensor], Optional[Tensor]]:
r"""Get pending and observed points.
Args:
Xs: The input tensors of a model.
pending_observations: A list of m (k_i x d) feature tensors X
for m outcomes and k_i pending observations for outcome i.
(Only used if n > 1).
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
bounds: A list of (lower, upper) tuples for each column of X.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b. (Not used by single task models)
linear_constraints: A tuple of (A, b). For k linear constraints on
d-dimensional x, A is (k x d) and b is (k x 1) such that
A x <= b. (Not used by single task models)
fixed_features: A map {feature_index: value} for features that
should be fixed to a particular value during generation.
Returns:
Tensor: Pending points that are feasible and appear in the objective or
the constraints. None if there are no such points.
Tensor: Observed points that are feasible and appear in the objective or
the constraints. None if there are no such points.
"""
if pending_observations is None:
X_pending = None
else:
X_pending = _filter_X_observed(
Xs=pending_observations,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
X_observed = _filter_X_observed(
Xs=Xs,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
bounds=bounds,
linear_constraints=linear_constraints,
fixed_features=fixed_features,
)
return X_pending, X_observed
