#!/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 Any, Callable, Dict, List, Optional, Tuple, Type, cast
import numpy as np
import torch
from ax.core.types import TConfig
from ax.exceptions.model import ModelError
from ax.models.model_utils import filter_constraints_and_fixed_features, get_observed
from ax.models.random.sobol import SobolGenerator
from ax.utils.common.constants import Keys
from ax.utils.common.logger import get_logger
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.analytic import PosteriorMean
from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
from botorch.acquisition.monte_carlo import qSimpleRegret
from botorch.acquisition.objective import (
AcquisitionObjective,
ConstrainedMCObjective,
LinearMCObjective,
MCAcquisitionObjective,
ScalarizedObjective,
)
from botorch.acquisition.utils import get_infeasible_cost
from botorch.exceptions.errors import UnsupportedError
from botorch.models import ModelListGP, SingleTaskGP
from botorch.models.model import Model
from botorch.sampling.samplers import IIDNormalSampler, SobolQMCNormalSampler
from botorch.utils.constraints import get_outcome_constraint_transforms
from botorch.utils.objective import get_objective_weights_transform
from botorch.utils.sampling import sample_hypersphere, sample_simplex
from torch import Tensor
logger = get_logger(__name__)
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
def _generate_sobol_points(
n_sobol: int,
bounds: List[Tuple[float, float]],
device: torch.device,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
) -> Tensor:
linear_constraints_array = None
if linear_constraints is not None:
linear_constraints_array = (
linear_constraints[0].detach().numpy(),
linear_constraints[1].detach().numpy(),
)
array_rounding_func = None
if rounding_func is not None:
array_rounding_func = tensor_callable_to_array_callable(
tensor_func=rounding_func, device=device
)
sobol = SobolGenerator(deduplicate=False, seed=np.random.randint(10000))
array_X, _ = sobol.gen(
n=n_sobol,
bounds=bounds,
linear_constraints=linear_constraints_array,
fixed_features=fixed_features,
rounding_func=array_rounding_func,
model_gen_options=model_gen_options,
)
return torch.from_numpy(array_X).to(device)
[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,
Ys: Optional[List[Tensor]] = None,
) -> Tuple[Model, Tensor, Optional[Tuple[Tensor, Tensor]], Optional[List[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)
Ys: The corresponding list of m (k_i x 1) outcome tensors Y, for
each outcome.
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, Ys
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
Ys = [Ys[i] for i in idcs] if Ys is not None else None
except NotImplementedError:
pass
return model, objective_weights, outcome_constraints, Ys
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 tensor_callable_to_array_callable(
tensor_func: Callable[[Tensor], Tensor], device: torch.device
) -> Callable[[np.ndarray], np.ndarray]:
""""transfer a tensor callable to an array callable"""
# TODO: move this reuseable function and its equivalent reverse functions
# to some utils files
def array_func(x: np.ndarray) -> np.ndarray:
return tensor_func(torch.from_numpy(x).to(device)).detach().numpy()
return array_func
[docs]def get_botorch_objective(
model: Model,
objective_weights: Tensor,
use_scalarized_objective: bool = True,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
) -> AcquisitionObjective:
"""Constructs a BoTorch `AcquisitionObjective` object.
Args:
model: A BoTorch Model
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
use_scalarized_objective: A boolean parameter that defaults to True,
specifying whether ScalarizedObjective should be used.
NOTE: when using outcome_constraints, use_scalarized_objective
will be ignored.
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)
X_observed: Observed points that are feasible and appear in the
objective or the constraints. None if there are no such points.
Returns:
A BoTorch `AcquisitionObjective` object. It will be one of:
`ScalarizedObjective`, `LinearMCOObjective`, `ConstrainedMCObjective`.
"""
if X_observed is None:
raise UnsupportedError(
"X_observed is required to construct a BoTorch Objective."
)
if outcome_constraints:
if use_scalarized_objective:
logger.warning(
"Currently cannot use ScalarizedObjective when there are outcome "
"constraints. Ignoring (default) kwarg `use_scalarized_objective`"
"= True. Creating ConstrainedMCObjective."
)
obj_tf = get_objective_weights_transform(objective_weights)
con_tfs = get_outcome_constraint_transforms(outcome_constraints)
inf_cost = get_infeasible_cost(X=X_observed, model=model, objective=obj_tf)
return ConstrainedMCObjective(
objective=obj_tf, constraints=con_tfs or [], infeasible_cost=inf_cost
)
if use_scalarized_objective:
return ScalarizedObjective(weights=objective_weights)
return LinearMCObjective(weights=objective_weights)
[docs]def get_out_of_sample_best_point_acqf(
model: Model,
Xs: List[Tensor],
X_observed: Tensor,
objective_weights: Tensor,
mc_samples: int = 512,
fixed_features: Optional[Dict[int, float]] = None,
fidelity_features: Optional[List[int]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
seed_inner: Optional[int] = None,
qmc: bool = True,
**kwargs: Any,
) -> Tuple[AcquisitionFunction, Optional[List[int]]]:
"""Picks an appropriate acquisition function to find the best
out-of-sample (predicted by the given surrogate model) point
and instantiates it.
NOTE: Typically the appropriate function is the posterior mean,
but can differ to account for fidelities etc.
"""
model = model
# subset model only to the outcomes we need for the optimization
if kwargs.get(Keys.SUBSET_MODEL, True):
model, objective_weights, outcome_constraints, _ = subset_model(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
)
fixed_features = fixed_features or {}
target_fidelities = target_fidelities or {}
if fidelity_features:
# we need to optimize at the target fidelities
if any(f in fidelity_features for f in fixed_features):
raise RuntimeError("Fixed features cannot also be fidelity features.")
elif set(fidelity_features) != set(target_fidelities):
raise RuntimeError(
"Must provide a target fidelity for every fidelity feature."
)
# make sure to not modify fixed_features in-place
fixed_features = {**fixed_features, **target_fidelities}
elif target_fidelities:
raise RuntimeError(
"Must specify fidelity_features in fit() when using target fidelities."
)
acqf_class, acqf_options = pick_best_out_of_sample_point_acqf_class(
outcome_constraints=outcome_constraints,
mc_samples=mc_samples,
qmc=qmc,
seed_inner=seed_inner,
)
objective = get_botorch_objective(
model=model,
objective_weights=objective_weights,
outcome_constraints=outcome_constraints,
X_observed=X_observed,
)
if not isinstance(objective, (ScalarizedObjective, MCAcquisitionObjective)):
raise UnsupportedError(
f"Unknown objective type: {objective.__class__}" # pragma: nocover
)
else:
# pyre-ignore[28]: All acq. functions here expect `objective` kwarg.
acqf = acqf_class(model=model, objective=objective, **acqf_options)
if fixed_features:
acqf = FixedFeatureAcquisitionFunction(
acq_function=acqf,
d=X_observed.size(-1),
columns=list(fixed_features.keys()),
values=list(fixed_features.values()),
)
non_fixed_idcs = [i for i in range(Xs[0].size(-1)) if i not in fixed_features]
else:
non_fixed_idcs = None
return acqf, non_fixed_idcs
[docs]def pick_best_out_of_sample_point_acqf_class(
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
mc_samples: int = 512,
qmc: bool = True,
seed_inner: Optional[int] = None,
) -> Tuple[Type[AcquisitionFunction], Dict[str, Any]]:
if outcome_constraints is None:
acqf_class = PosteriorMean
acqf_options = {}
else:
acqf_class = qSimpleRegret
sampler_class = SobolQMCNormalSampler if qmc else IIDNormalSampler
acqf_options = {
Keys.SAMPLER.value: sampler_class(num_samples=mc_samples, seed=seed_inner)
}
return cast(Type[AcquisitionFunction], acqf_class), acqf_options
[docs]def predict_from_model(model: Model, X: Tensor) -> Tuple[Tensor, Tensor]:
r"""Predicts outcomes given a model and input tensor.
Args:
model: A botorch Model.
X: A `n x d` tensor of input parameters.
Returns:
Tensor: The predicted posterior mean as an `n x o`-dim tensor.
Tensor: The predicted posterior covariance as a `n x o x o`-dim tensor.
"""
with torch.no_grad():
posterior = model.posterior(X)
mean = posterior.mean.cpu().detach()
# TODO: Allow Posterior to (optionally) return the full covariance matrix
variance = posterior.variance.cpu().detach().clamp_min(0) # pyre-ignore
cov = torch.diag_embed(variance)
return mean, cov
# TODO(jej): Possibly refactor to use "objective_directions".
[docs]def randomize_objective_weights(
objective_weights: Tensor, **acquisition_function_kwargs: Any
) -> Tensor:
"""Generate a random weighting based on acquisition function settings.
Args:
objective_weights: Base weights to multiply by random values..
**acquisition_function_kwargs: Kwargs containing weight generation algorithm
options.
Returns:
A normalized list of indices such that each index is between `0` and `d-1`.
"""
# Set distribution and sample weights.
distribution = acquisition_function_kwargs.get(
"random_scalarization_distribution", SIMPLEX
)
dtype = objective_weights.dtype
device = objective_weights.device
if distribution == SIMPLEX:
random_weights = sample_simplex(
len(objective_weights), dtype=dtype, device=device
).squeeze()
elif distribution == HYPERSPHERE:
random_weights = torch.abs(
sample_hypersphere(
len(objective_weights), dtype=dtype, device=device
).squeeze()
)
objective_weights = torch.mul(objective_weights, random_weights)
return objective_weights
