#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional, Tuple
import botorch.utils.sampling as botorch_sampling
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}
# Distributions
SIMPLEX = "simplex"
HYPERSPHERE = "hypersphere"
[docs]def is_noiseless(model: Model) -> bool:
"""Check if a given (single-task) botorch model is noiseless"""
if isinstance(model, ModelListGP):
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
[docs]def normalize_indices(indices: List[int], d: int) -> List[int]:
r"""Normalize a list of indices to ensure that they are positive.
Args:
indices: A list of indices (may contain negative indices for indexing
"from the back").
d: The dimension of the tensor to index.
Returns:
A normalized list of indices such that each index is between `0` and `d-1`.
"""
normalized_indices = []
for i in indices:
if i < 0:
i = i + d
if i < 0 or i > d - 1:
raise ValueError(f"Index {i} out of bounds for tensor or length {d}.")
normalized_indices.append(i)
return normalized_indices
[docs]def subset_model(
model: Model,
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
) -> Tuple[Model, Tensor, Optional[Tuple[Tensor, Tensor]]]:
"""Subset a botorch model to the outputs used in the optimization.
Args:
model: A BoTorch Model. If the model does not implement the
`subset_outputs` method, this function is a null-op and returns the
input arguments.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
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)
Returns:
A three-tuple of model, objective_weights, and outcome_constraints, all
subset to only those outputs that appear in either the objective weights
or the outcome constraints.
"""
nonzero = objective_weights != 0
if outcome_constraints is not None:
A, _ = outcome_constraints
nonzero = nonzero | torch.any(A != 0, dim=0)
idcs = torch.arange(nonzero.size(0))[nonzero].tolist()
if len(idcs) == model.num_outputs:
# if we use all model outputs, just return the inputs
return model, objective_weights, outcome_constraints
elif len(idcs) > model.num_outputs:
raise RuntimeError(
"Model size inconsistency. Tryting to subset a model with "
f"{model.num_outputs} outputs to {len(idcs)} outputs"
)
try:
model = model.subset_output(idcs=idcs)
objective_weights = objective_weights[nonzero]
if outcome_constraints is not None:
A, b = outcome_constraints
outcome_constraints = A[:, nonzero], b
except NotImplementedError:
pass
return model, objective_weights, outcome_constraints
def _to_inequality_constraints(
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None
) -> Optional[List[Tuple[Tensor, Tensor, float]]]:
if linear_constraints is not None:
A, b = linear_constraints
inequality_constraints = []
k, d = A.shape
for i in range(k):
indicies = A[i, :].nonzero().squeeze()
coefficients = -A[i, indicies]
rhs = -b[i, 0]
inequality_constraints.append((indicies, coefficients, rhs))
else:
inequality_constraints = None
return inequality_constraints
[docs]def sample_simplex(dim: int) -> Tensor:
"""Sample uniformly from a dim-simplex."""
return botorch_sampling.sample_simplex(dim, dtype=torch.double).squeeze()
[docs]def sample_hypersphere_positive_quadrant(dim: int) -> Tensor:
"""Sample uniformly from the positive quadrant of a dim-sphere."""
return torch.abs(
botorch_sampling.sample_hypersphere(dim, dtype=torch.double).squeeze()
)
