#!/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.
"""
References
.. [Daulton2020qehvi]
S. Daulton, M. Balandat, and E. Bakshy. Differentiable Expected Hypervolume
Improvement for Parallel Multi-Objective Bayesian Optimization. Advances in Neural
Information Processing Systems 33, 2020.
.. [Daulton2021nehvi]
S. Daulton, M. Balandat, and E. Bakshy. Parallel Bayesian Optimization of
Multiple Noisy Objectives. ArXiv, 2021.
"""
from typing import Any, Callable, Dict, List, Optional, Tuple
import torch
from ax.models.torch.utils import ( # noqa F40
_to_inequality_constraints,
get_outcome_constraint_transforms,
predict_from_model,
)
from ax.models.torch_base import TorchModel
from ax.utils.common.constants import Keys
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.multi_objective.objective import WeightedMCMultiOutputObjective
from botorch.acquisition.multi_objective.utils import get_default_partitioning_alpha
from botorch.acquisition.utils import (
get_acquisition_function,
)
from botorch.models.model import Model
from botorch.optim.optimize import optimize_acqf_list
from botorch.utils.multi_objective.pareto import is_non_dominated
from torch import Tensor
DEFAULT_EHVI_MC_SAMPLES = 128
# Callable that takes tensors of observations and model parameters,
# then returns means of observations that make up a pareto frontier,
# along with their covariances and their index in the input observations.
TFrontierEvaluator = Callable[
[
TorchModel,
Tensor,
Optional[Tensor],
Optional[Tensor],
Optional[Tensor],
Optional[Tensor],
Optional[Tuple[Tensor, Tensor]],
],
Tuple[Tensor, Tensor, Tensor],
]
[docs]def get_default_frontier_evaluator() -> TFrontierEvaluator:
return pareto_frontier_evaluator
[docs]def get_weighted_mc_objective_and_objective_thresholds(
objective_weights: Tensor, objective_thresholds: Tensor
) -> Tuple[WeightedMCMultiOutputObjective, Tensor]:
r"""Construct weighted objective and apply the weights to objective thresholds.
Args:
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
objective_thresholds: A tensor containing thresholds forming a reference point
from which to calculate pareto frontier hypervolume. Points that do not
dominate the objective_thresholds contribute nothing to hypervolume.
Returns:
A two-element tuple with the objective and objective thresholds:
- The objective
- The objective thresholds
"""
# pyre-ignore [16]
nonzero_idcs = objective_weights.nonzero(as_tuple=False).view(-1)
objective_weights = objective_weights[nonzero_idcs]
objective_thresholds = objective_thresholds[nonzero_idcs]
objective = WeightedMCMultiOutputObjective(
weights=objective_weights, outcomes=nonzero_idcs.tolist()
)
objective_thresholds = torch.mul(objective_thresholds, objective_weights)
return objective, objective_thresholds
[docs]def get_NEHVI(
model: Model,
objective_weights: Tensor,
objective_thresholds: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
X_pending: Optional[Tensor] = None,
**kwargs: Any,
) -> AcquisitionFunction:
r"""Instantiates a qNoisyExpectedHyperVolumeImprovement acquisition function.
Args:
model: The underlying model which the acqusition function uses
to estimate acquisition values of candidates.
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)
X_observed: A tensor containing points observed for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
X_pending: A tensor containing points whose evaluation is pending (i.e.
that have been submitted for evaluation) present for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
mc_samples: The number of MC samples to use (default: 512).
qmc: If True, use qMC instead of MC (default: True).
prune_baseline: If True, prune the baseline points for NEI (default: True).
chebyshev_scalarization: Use augmented Chebyshev scalarization.
Returns:
qNoisyExpectedHyperVolumeImprovement: The instantiated acquisition function.
"""
if X_observed is None:
raise ValueError("There are no feasible observed points.")
# construct Objective module
(
objective,
objective_thresholds,
) = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=objective_weights, objective_thresholds=objective_thresholds
)
# For EHVI acquisition functions we pass the constraint transform directly.
if outcome_constraints is None:
cons_tfs = None
else:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
num_objectives = objective_thresholds.shape[0]
return get_acquisition_function(
acquisition_function_name="qNEHVI",
model=model,
objective=objective, # pyre-ignore [6]
X_observed=X_observed,
X_pending=X_pending,
constraints=cons_tfs,
prune_baseline=kwargs.get("prune_baseline", True),
mc_samples=kwargs.get("mc_samples", DEFAULT_EHVI_MC_SAMPLES),
alpha=kwargs.get(
"alpha", get_default_partitioning_alpha(num_objectives=num_objectives)
),
qmc=kwargs.get("qmc", True),
seed=torch.randint(1, 10000, (1,)).item(),
ref_point=objective_thresholds.tolist(),
marginalize_dim=kwargs.get("marginalize_dim"),
match_right_most_batch_dim=kwargs.get("match_right_most_batch_dim", False),
)
[docs]def get_EHVI(
model: Model,
objective_weights: Tensor,
objective_thresholds: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
X_observed: Optional[Tensor] = None,
X_pending: Optional[Tensor] = None,
**kwargs: Any,
) -> AcquisitionFunction:
r"""Instantiates a qExpectedHyperVolumeImprovement acquisition function.
Args:
model: The underlying model which the acqusition function uses
to estimate acquisition values of candidates.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
objective_thresholds: A tensor containing thresholds forming a reference point
from which to calculate pareto frontier hypervolume. Points that do not
dominate the objective_thresholds contribute nothing to hypervolume.
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: A tensor containing points observed for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
X_pending: A tensor containing points whose evaluation is pending (i.e.
that have been submitted for evaluation) present for all objective
outcomes and outcomes that appear in the outcome constraints (if
there are any).
mc_samples: The number of MC samples to use (default: 512).
qmc: If True, use qMC instead of MC (default: True).
Returns:
qExpectedHypervolumeImprovement: The instantiated acquisition function.
"""
if X_observed is None:
raise ValueError("There are no feasible observed points.")
# construct Objective module
(
objective,
objective_thresholds,
) = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=objective_weights, objective_thresholds=objective_thresholds
)
with torch.no_grad():
Y = model.posterior(X_observed).mean
# For EHVI acquisition functions we pass the constraint transform directly.
if outcome_constraints is None:
cons_tfs = None
else:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
num_objectives = objective_thresholds.shape[0]
return get_acquisition_function(
acquisition_function_name="qEHVI",
model=model,
# TODO (jej): Fix pyre error below by restructuring class hierarchy.
# pyre-fixme[6]: Expected `botorch.acquisition.objective.
# MCAcquisitionObjective` for 3rd parameter `objective` to call
# `get_acquisition_function` but got `IdentityMCMultiOutputObjective`.
objective=objective,
X_observed=X_observed,
X_pending=X_pending,
constraints=cons_tfs,
mc_samples=kwargs.get("mc_samples", DEFAULT_EHVI_MC_SAMPLES),
qmc=kwargs.get("qmc", True),
alpha=kwargs.get(
"alpha", get_default_partitioning_alpha(num_objectives=num_objectives)
),
seed=torch.randint(1, 10000, (1,)).item(),
ref_point=objective_thresholds.tolist(),
Y=Y,
)
# TODO (jej): rewrite optimize_acqf wrappers to avoid duplicate code.
[docs]def scipy_optimizer_list(
acq_function_list: List[AcquisitionFunction],
bounds: Tensor,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
**kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""Sequential optimizer using scipy's minimize module on a numpy-adaptor.
The ith acquisition in the sequence uses the ith given acquisition_function.
Args:
acq_function_list: A list of botorch AcquisitionFunctions,
optimized sequentially.
bounds: A `2 x d`-dim tensor, where `bounds[0]` (`bounds[1]`) are the
lower (upper) bounds of the feasible hyperrectangle.
n: The number of candidates to generate.
inequality constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an inequality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) >= rhs`
fixed_features: A map {feature_index: value} for features that should
be fixed to a particular value during generation.
rounding_func: A function that rounds an optimization result
appropriately (i.e., according to `round-trip` transformations).
Returns:
2-element tuple containing
- A `n x d`-dim tensor of generated candidates.
- A `n`-dim tensor of conditional acquisition
values, where `i`-th element is the expected acquisition value
conditional on having observed candidates `0,1,...,i-1`.
"""
num_restarts: int = kwargs.pop(Keys.NUM_RESTARTS, 20)
raw_samples: int = kwargs.pop(Keys.RAW_SAMPLES, 50 * num_restarts)
# use SLSQP by default for small problems since it yields faster wall times
if "method" not in kwargs:
kwargs["method"] = "SLSQP"
X, expected_acquisition_value = optimize_acqf_list(
acq_function_list=acq_function_list,
bounds=bounds,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=kwargs,
inequality_constraints=inequality_constraints,
fixed_features=fixed_features,
post_processing_func=rounding_func,
)
return X, expected_acquisition_value
[docs]def pareto_frontier_evaluator(
model: TorchModel,
objective_weights: Tensor,
objective_thresholds: Optional[Tensor] = None,
X: Optional[Tensor] = None,
Y: Optional[Tensor] = None,
Yvar: Optional[Tensor] = None,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
) -> Tuple[Tensor, Tensor, Tensor]:
"""Return outcomes predicted to lie on a pareto frontier.
Given a model and a points to evaluate use the model to predict which points
lie on the pareto frontier.
Args:
model: Model used to predict outcomes.
objective_weights: A `m` tensor of values indicating the weight to put
on different outcomes. For pareto frontiers only the sign matters.
objective_thresholds: A tensor containing thresholds forming a reference point
from which to calculate pareto frontier hypervolume. Points that do not
dominate the objective_thresholds contribute nothing to hypervolume.
X: A `n x d` tensor of features to evaluate.
Y: A `n x m` tensor of outcomes to use instead of predictions.
Yvar: A `n x m x m` tensor of input covariances (NaN if unobserved).
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.
Returns:
3-element tuple containing
- A `j x m` tensor of outcome on the pareto frontier. j is the number
of frontier points.
- A `j x m x m` tensor of predictive covariances.
cov[j, m1, m2] is Cov[m1@j, m2@j].
- A `j` tensor of the index of each frontier point in the input Y.
"""
if X is not None:
Y, Yvar = model.predict(X)
elif Y is None or Yvar is None:
raise ValueError(
"Requires `X` to predict or both `Y` and `Yvar` to select a subset of "
"points on the pareto frontier."
)
# Apply objective_weights to outcomes and objective_thresholds.
# If objective_thresholds is not None use a dummy tensor of zeros.
(
obj,
weighted_objective_thresholds,
) = get_weighted_mc_objective_and_objective_thresholds(
objective_weights=objective_weights,
objective_thresholds=(
objective_thresholds
if objective_thresholds is not None
else torch.zeros(objective_weights.shape)
),
)
Y_obj = obj(Y)
indx_frontier = torch.arange(Y.shape[0], dtype=torch.long, device=Y.device)
# Filter Y, Yvar, Y_obj to items that dominate all objective thresholds
if objective_thresholds is not None:
objective_thresholds_mask = (Y_obj >= weighted_objective_thresholds).all(dim=1)
Y = Y[objective_thresholds_mask]
Yvar = Yvar[objective_thresholds_mask]
Y_obj = Y_obj[objective_thresholds_mask]
indx_frontier = indx_frontier[objective_thresholds_mask]
# Get feasible points that do not violate outcome_constraints
if outcome_constraints is not None:
cons_tfs = get_outcome_constraint_transforms(outcome_constraints)
# pyre-ignore [16]
feas = torch.stack([c(Y) <= 0 for c in cons_tfs], dim=-1).all(dim=-1)
Y = Y[feas]
Yvar = Yvar[feas]
Y_obj = Y_obj[feas]
indx_frontier = indx_frontier[feas]
if Y.shape[0] == 0:
# if there are no feasible points that are better than the reference point
# return empty tensors
return Y, Yvar, indx_frontier
# calculate pareto front with only objective outcomes:
frontier_mask = is_non_dominated(Y_obj)
# Apply masks
Y_frontier = Y[frontier_mask]
Yvar_frontier = Yvar[frontier_mask]
indx_frontier = indx_frontier[frontier_mask]
return Y_frontier, Yvar_frontier, indx_frontier
