#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
r"""
References
.. [Regis]
R. G. Regis, C. A. Shoemaker. Combining radial basis function
surrogates and dynamic coordinate search in high-dimensional
expensive black-box optimization, Engineering Optimization, 2013.
"""
from __future__ import annotations
import warnings
from math import ceil
from typing import Callable, Optional, Union
import torch
from botorch import settings
from botorch.acquisition import analytic, monte_carlo, multi_objective
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
from botorch.acquisition.knowledge_gradient import (
_get_value_function,
qKnowledgeGradient,
)
from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import (
_get_hv_value_function,
qHypervolumeKnowledgeGradient,
qMultiFidelityHypervolumeKnowledgeGradient,
)
from botorch.exceptions.errors import BotorchTensorDimensionError, UnsupportedError
from botorch.exceptions.warnings import (
BadInitialCandidatesWarning,
BotorchWarning,
SamplingWarning,
)
from botorch.models.model import Model
from botorch.optim.utils import fix_features, get_X_baseline
from botorch.utils.multi_objective.pareto import is_non_dominated
from botorch.utils.sampling import (
batched_multinomial,
draw_sobol_samples,
get_polytope_samples,
manual_seed,
)
from botorch.utils.transforms import normalize, standardize, unnormalize
from torch import Tensor
from torch.distributions import Normal
from torch.quasirandom import SobolEngine
TGenInitialConditions = Callable[
[
# reasoning behind this annotation: contravariance
qKnowledgeGradient,
Tensor,
int,
int,
int,
Optional[dict[int, float]],
Optional[dict[str, Union[bool, float, int]]],
Optional[list[tuple[Tensor, Tensor, float]]],
Optional[list[tuple[Tensor, Tensor, float]]],
],
Optional[Tensor],
]
[docs]
def sample_q_batches_from_polytope(
n: int,
q: int,
bounds: Tensor,
n_burnin: int,
n_thinning: int,
seed: int,
inequality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
) -> Tensor:
r"""Samples `n` q-baches from a polytope of dimension `d`.
Args:
n: Number of q-batches to sample.
q: Number of samples per q-batch
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
n_burnin: The number of burn-in samples for the Markov chain sampler.
n_thinning: The amount of thinning. The sampler will return every
`n_thinning` sample (after burn-in).
seed: The random seed.
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`.
equality_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`.
Returns:
A `n x q x d`-dim tensor of samples.
"""
# check if inter-point constraints are present
inter_point = any(
len(indices.shape) > 1
for constraints in (inequality_constraints or [], equality_constraints or [])
for indices, _, _ in constraints
)
if inter_point:
samples = get_polytope_samples(
n=n,
bounds=torch.hstack([bounds for _ in range(q)]),
inequality_constraints=transform_constraints(
constraints=inequality_constraints, q=q, d=bounds.shape[1]
),
equality_constraints=transform_constraints(
constraints=equality_constraints, q=q, d=bounds.shape[1]
),
seed=seed,
n_burnin=n_burnin,
n_thinning=n_thinning * q,
)
else:
samples = get_polytope_samples(
n=n * q,
bounds=bounds,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
seed=seed,
n_burnin=n_burnin,
n_thinning=n_thinning,
)
return samples.view(n, q, -1).cpu()
[docs]
def gen_batch_initial_conditions(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
fixed_features: Optional[dict[int, float]] = None,
options: Optional[dict[str, Union[bool, float, int]]] = None,
inequality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
generator: Optional[Callable[[int, int, Optional[int]], Tensor]] = None,
fixed_X_fantasies: Optional[Tensor] = None,
) -> Tensor:
r"""Generate a batch of initial conditions for random-restart optimziation.
TODO: Support t-batches of initial conditions.
Args:
acq_function: The acquisition function to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`.
q: The number of candidates to consider.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic. Note: if `sample_around_best` is True (the default is False),
then `2 * raw_samples` samples are used.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
options: Options for initial condition generation. For valid options see
`initialize_q_batch` and `initialize_q_batch_nonneg`. If `options`
contains a `nonnegative=True` entry, then `acq_function` is
assumed to be non-negative (useful when using custom acquisition
functions). In addition, an "init_batch_limit" option can be passed
to specify the batch limit for the initialization. This is useful
for avoiding memory limits when computing the batch posterior over
raw samples.
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`.
equality 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`.
generator: Callable for generating samples that are then further
processed. It receives `n`, `q` and `seed` as arguments
and returns a tensor of shape `n x q x d`.
fixed_X_fantasies: A fixed set of fantasy points to concatenate to
the `q` candidates being initialized along the `-2` dimension. The
shape should be `num_pseudo_points x d`. E.g., this should be
`num_fantasies x d` for KG and `num_fantasies*num_pareto x d`
for HVKG.
Returns:
A `num_restarts x q x d` tensor of initial conditions.
Example:
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0.], [1.]])
>>> Xinit = gen_batch_initial_conditions(
>>> qEI, bounds, q=3, num_restarts=25, raw_samples=500
>>> )
"""
if bounds.isinf().any():
raise NotImplementedError(
"Currently only finite values in `bounds` are supported "
"for generating initial conditions for optimization."
)
options = options or {}
sample_around_best = options.get("sample_around_best", False)
if sample_around_best and equality_constraints:
raise UnsupportedError(
"Option 'sample_around_best' is not supported when equality"
"constraints are present."
)
if sample_around_best and generator:
raise UnsupportedError(
"Option 'sample_around_best' is not supported when custom "
"generator is be used."
)
seed: Optional[int] = options.get("seed")
batch_limit: Optional[int] = options.get(
"init_batch_limit", options.get("batch_limit")
)
factor, max_factor = 1, 5
init_kwargs = {}
device = bounds.device
bounds_cpu = bounds.cpu()
if "eta" in options:
init_kwargs["eta"] = options.get("eta")
if options.get("nonnegative") or is_nonnegative(acq_function):
init_func = initialize_q_batch_nonneg
if "alpha" in options:
init_kwargs["alpha"] = options.get("alpha")
else:
init_func = initialize_q_batch
q = 1 if q is None else q
# the dimension the samples are drawn from
effective_dim = bounds.shape[-1] * q
if effective_dim > SobolEngine.MAXDIM and settings.debug.on():
warnings.warn(
f"Sample dimension q*d={effective_dim} exceeding Sobol max dimension "
f"({SobolEngine.MAXDIM}). Using iid samples instead.",
SamplingWarning,
stacklevel=3,
)
while factor < max_factor:
with warnings.catch_warnings(record=True) as ws:
n = raw_samples * factor
if generator is not None:
X_rnd = generator(n, q, seed)
# check if no constraints are provided
elif not (inequality_constraints or equality_constraints):
if effective_dim <= SobolEngine.MAXDIM:
X_rnd = draw_sobol_samples(bounds=bounds_cpu, n=n, q=q, seed=seed)
else:
with manual_seed(seed):
# load on cpu
X_rnd_nlzd = torch.rand(
n, q, bounds_cpu.shape[-1], dtype=bounds.dtype
)
X_rnd = bounds_cpu[0] + (bounds_cpu[1] - bounds_cpu[0]) * X_rnd_nlzd
else:
X_rnd = sample_q_batches_from_polytope(
n=n,
q=q,
bounds=bounds,
n_burnin=options.get("n_burnin", 10000),
n_thinning=options.get("n_thinning", 32),
seed=seed,
equality_constraints=equality_constraints,
inequality_constraints=inequality_constraints,
)
# sample points around best
if sample_around_best:
X_best_rnd = sample_points_around_best(
acq_function=acq_function,
n_discrete_points=n * q,
sigma=options.get("sample_around_best_sigma", 1e-3),
bounds=bounds,
subset_sigma=options.get("sample_around_best_subset_sigma", 1e-1),
prob_perturb=options.get("sample_around_best_prob_perturb"),
)
if X_best_rnd is not None:
X_rnd = torch.cat(
[
X_rnd,
X_best_rnd.view(n, q, bounds.shape[-1]).cpu(),
],
dim=0,
)
X_rnd = fix_features(X_rnd, fixed_features=fixed_features)
if fixed_X_fantasies is not None:
if (d_f := fixed_X_fantasies.shape[-1]) != (d_r := X_rnd.shape[-1]):
raise BotorchTensorDimensionError(
"`fixed_X_fantasies` and `bounds` must both have the same "
f"trailing dimension `d`, but have {d_f} and {d_r}, "
"respectively."
)
X_rnd = torch.cat(
[
X_rnd,
fixed_X_fantasies.cpu()
.unsqueeze(0)
.expand(X_rnd.shape[0], *fixed_X_fantasies.shape),
],
dim=-2,
)
with torch.no_grad():
if batch_limit is None:
batch_limit = X_rnd.shape[0]
Y_rnd_list = []
start_idx = 0
while start_idx < X_rnd.shape[0]:
end_idx = min(start_idx + batch_limit, X_rnd.shape[0])
Y_rnd_curr = acq_function(
X_rnd[start_idx:end_idx].to(device=device)
).cpu()
Y_rnd_list.append(Y_rnd_curr)
start_idx += batch_limit
Y_rnd = torch.cat(Y_rnd_list)
batch_initial_conditions = init_func(
X=X_rnd, Y=Y_rnd, n=num_restarts, **init_kwargs
).to(device=device)
if not any(issubclass(w.category, BadInitialCandidatesWarning) for w in ws):
return batch_initial_conditions
if factor < max_factor:
factor += 1
if seed is not None:
seed += 1 # make sure to sample different X_rnd
warnings.warn(
"Unable to find non-zero acquisition function values - initial conditions "
"are being selected randomly.",
BadInitialCandidatesWarning,
)
return batch_initial_conditions
[docs]
def gen_one_shot_kg_initial_conditions(
acq_function: qKnowledgeGradient,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
fixed_features: Optional[dict[int, float]] = None,
options: Optional[dict[str, Union[bool, float, int]]] = None,
inequality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
) -> Optional[Tensor]:
r"""Generate a batch of smart initializations for qKnowledgeGradient.
This function generates initial conditions for optimizing one-shot KG using
the maximizer of the posterior objective. Intutively, the maximizer of the
fantasized posterior will often be close to a maximizer of the current
posterior. This function uses that fact to generate the initial conditions
for the fantasy points. Specifically, a fraction of `1 - frac_random` (see
options) is generated by sampling from the set of maximizers of the
posterior objective (obtained via random restart optimization) according to
a softmax transformation of their respective values. This means that this
initialization strategy internally solves an acquisition function
maximization problem. The remaining `frac_random` fantasy points as well as
all `q` candidate points are chosen according to the standard initialization
strategy in `gen_batch_initial_conditions`.
Args:
acq_function: The qHypervolumeKnowledgeGradient instance to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of
task features.
q: The number of candidates to consider.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
options: Options for initial condition generation. These contain all
settings for the standard heuristic initialization from
`gen_batch_initial_conditions`. In addition, they contain
`frac_random` (the fraction of fully random fantasy points),
`num_inner_restarts` and `raw_inner_samples` (the number of random
restarts and raw samples for solving the posterior objective
maximization problem, respectively) and `eta` (temperature parameter
for sampling heuristic from posterior objective maximizers).
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`.
equality 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`.
Returns:
A `num_restarts x q' x d` tensor that can be used as initial conditions
for `optimize_acqf()`. Here `q' = q + num_fantasies` is the total number
of points (candidate points plus fantasy points).
Example:
>>> qHVKG = qHypervolumeKnowledgeGradient(model, ref_point=num_fantasies=64)
>>> bounds = torch.tensor([[0., 0.], [1., 1.]])
>>> Xinit = gen_one_shot_hvkg_initial_conditions(
>>> qHVKG, bounds, q=3, num_restarts=10, raw_samples=512,
>>> options={"frac_random": 0.25},
>>> )
"""
options = options or {}
frac_random: float = options.get("frac_random", 0.1)
if not 0 < frac_random < 1:
raise ValueError(
f"frac_random must take on values in (0,1). Value: {frac_random}"
)
q_aug = acq_function.get_augmented_q_batch_size(q=q)
# TODO: Avoid unnecessary computation by not generating all candidates
ics = gen_batch_initial_conditions(
acq_function=acq_function,
bounds=bounds,
q=q_aug,
num_restarts=num_restarts,
raw_samples=raw_samples,
fixed_features=fixed_features,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
)
# compute maximizer of the value function
value_function = _get_value_function(
model=acq_function.model,
objective=acq_function.objective,
posterior_transform=acq_function.posterior_transform,
sampler=acq_function.inner_sampler,
project=getattr(acq_function, "project", None),
)
from botorch.optim.optimize import optimize_acqf
fantasy_cands, fantasy_vals = optimize_acqf(
acq_function=value_function,
bounds=bounds,
q=1,
num_restarts=options.get("num_inner_restarts", 20),
raw_samples=options.get("raw_inner_samples", 1024),
fixed_features=fixed_features,
return_best_only=False,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
)
# sampling from the optimizers
n_value = int((1 - frac_random) * (q_aug - q)) # number of non-random ICs
eta = options.get("eta", 2.0)
weights = torch.exp(eta * standardize(fantasy_vals))
idx = torch.multinomial(weights, num_restarts * n_value, replacement=True)
# set the respective initial conditions to the sampled optimizers
ics[..., -n_value:, :] = fantasy_cands[idx, 0].view(num_restarts, n_value, -1)
return ics
[docs]
def gen_one_shot_hvkg_initial_conditions(
acq_function: qHypervolumeKnowledgeGradient,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
fixed_features: Optional[dict[int, float]] = None,
options: Optional[dict[str, Union[bool, float, int]]] = None,
inequality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[list[tuple[Tensor, Tensor, float]]] = None,
) -> Optional[Tensor]:
r"""Generate a batch of smart initializations for qHypervolumeKnowledgeGradient.
This function generates initial conditions for optimizing one-shot HVKG using
the hypervolume maximizing set (of fixed size) under the posterior mean.
Intutively, the hypervolume maximizing set of the fantasized posterior mean
will often be close to a hypervolume maximizing set under the current posterior
mean. This function uses that fact to generate the initial conditions
for the fantasy points. Specifically, a fraction of `1 - frac_random` (see
options) of the restarts are generated by learning the hypervolume maximizing sets
under the current posterior mean, where each hypervolume maximizing set is
obtained from maximizing the hypervolume from a different starting point. Given
a hypervolume maximizing set, the `q` candidate points are selected using to the
standard initialization strategy in `gen_batch_initial_conditions`, with the fixed
hypervolume maximizing set. The remaining `frac_random` restarts fantasy points
as well as all `q` candidate points are chosen according to the standard
initialization strategy in `gen_batch_initial_conditions`.
Args:
acq_function: The qKnowledgeGradient instance to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of
task features.
q: The number of candidates to consider.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
options: Options for initial condition generation. These contain all
settings for the standard heuristic initialization from
`gen_batch_initial_conditions`. In addition, they contain
`frac_random` (the fraction of fully random fantasy points),
`num_inner_restarts` and `raw_inner_samples` (the number of random
restarts and raw samples for solving the posterior objective
maximization problem, respectively) and `eta` (temperature parameter
for sampling heuristic from posterior objective maximizers).
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`.
equality 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`.
Returns:
A `num_restarts x q' x d` tensor that can be used as initial conditions
for `optimize_acqf()`. Here `q' = q + num_fantasies` is the total number
of points (candidate points plus fantasy points).
Example:
>>> qHVKG = qHypervolumeKnowledgeGradient(model, ref_point)
>>> bounds = torch.tensor([[0., 0.], [1., 1.]])
>>> Xinit = gen_one_shot_hvkg_initial_conditions(
>>> qHVKG, bounds, q=3, num_restarts=10, raw_samples=512,
>>> options={"frac_random": 0.25},
>>> )
"""
from botorch.optim.optimize import optimize_acqf
options = options or {}
frac_random: float = options.get("frac_random", 0.1)
if not 0 < frac_random < 1:
raise ValueError(
f"frac_random must take on values in (0,1). Value: {frac_random}"
)
value_function = _get_hv_value_function(
model=acq_function.model,
ref_point=acq_function.ref_point,
objective=acq_function.objective,
sampler=acq_function.inner_sampler,
use_posterior_mean=acq_function.use_posterior_mean,
)
is_mf_hvkg = isinstance(acq_function, qMultiFidelityHypervolumeKnowledgeGradient)
if is_mf_hvkg:
dim = bounds.shape[-1]
fidelity_dims, fidelity_targets = zip(*acq_function.target_fidelities.items())
value_function = FixedFeatureAcquisitionFunction(
acq_function=value_function,
d=dim,
columns=fidelity_dims,
values=fidelity_targets,
)
non_fidelity_dims = list(set(range(dim)) - set(fidelity_dims))
num_optim_restarts = int(round(num_restarts * (1 - frac_random)))
fantasy_cands, fantasy_vals = optimize_acqf(
acq_function=value_function,
bounds=bounds[:, non_fidelity_dims] if is_mf_hvkg else bounds,
q=acq_function.num_pareto,
num_restarts=options.get("num_inner_restarts", 20),
raw_samples=options.get("raw_inner_samples", 1024),
fixed_features=fixed_features,
return_best_only=False,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
sequential=False,
)
# sampling from the optimizers
eta = options.get("eta", 2.0)
if num_optim_restarts > 0:
probs = torch.nn.functional.softmax(eta * standardize(fantasy_vals), dim=0)
idx = torch.multinomial(
probs,
num_optim_restarts * acq_function.num_fantasies,
replacement=True,
)
optim_ics = fantasy_cands[idx]
if is_mf_hvkg:
# add fixed features
optim_ics = value_function._construct_X_full(optim_ics)
optim_ics = optim_ics.reshape(
num_optim_restarts, acq_function.num_pseudo_points, bounds.shape[-1]
)
# get random initial conditions
num_random_restarts = num_restarts - num_optim_restarts
if num_random_restarts > 0:
q_aug = acq_function.get_augmented_q_batch_size(q=q)
base_ics = gen_batch_initial_conditions(
acq_function=acq_function,
bounds=bounds,
q=q_aug,
num_restarts=num_restarts,
raw_samples=raw_samples,
fixed_features=fixed_features,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
)
if num_optim_restarts > 0:
probs = torch.full(
(num_restarts,),
1.0 / num_restarts,
dtype=optim_ics.dtype,
device=optim_ics.device,
)
optim_idxr = probs.multinomial(
num_samples=num_optim_restarts, replacement=False
)
base_ics[optim_idxr, q:] = optim_ics
else:
# optim_ics is num_restarts x num_pseudo_points x d
# add padding so that base_ics is num_restarts x q+num_pseudo_points x d
q_padding = torch.zeros(
optim_ics.shape[0],
q,
optim_ics.shape[-1],
dtype=optim_ics.dtype,
device=optim_ics.device,
)
base_ics = torch.cat([q_padding, optim_ics], dim=-2)
if num_optim_restarts > 0:
all_ics = []
if num_random_restarts > 0:
optim_idcs = optim_idxr.view(-1).tolist()
else:
optim_idcs = list(range(num_restarts))
for i in list(range(num_restarts)):
if i in optim_idcs:
# optimize the q points,
# given fixed, optimized fantasy designs
ics = gen_batch_initial_conditions(
acq_function=acq_function,
bounds=bounds,
q=q,
num_restarts=1,
raw_samples=raw_samples,
fixed_features=fixed_features,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_X_fantasies=base_ics[i, q:],
)
else:
# ics are all randomly sampled
ics = base_ics[i : i + 1]
all_ics.append(ics)
return torch.cat(all_ics, dim=0)
return base_ics
[docs]
def gen_value_function_initial_conditions(
acq_function: AcquisitionFunction,
bounds: Tensor,
num_restarts: int,
raw_samples: int,
current_model: Model,
fixed_features: Optional[dict[int, float]] = None,
options: Optional[dict[str, Union[bool, float, int]]] = None,
) -> Tensor:
r"""Generate a batch of smart initializations for optimizing
the value function of qKnowledgeGradient.
This function generates initial conditions for optimizing the inner problem of
KG, i.e. its value function, using the maximizer of the posterior objective.
Intutively, the maximizer of the fantasized posterior will often be close to a
maximizer of the current posterior. This function uses that fact to generate the
initital conditions for the fantasy points. Specifically, a fraction of `1 -
frac_random` (see options) of raw samples is generated by sampling from the set of
maximizers of the posterior objective (obtained via random restart optimization)
according to a softmax transformation of their respective values. This means that
this initialization strategy internally solves an acquisition function
maximization problem. The remaining raw samples are generated using
`draw_sobol_samples`. All raw samples are then evaluated, and the initial
conditions are selected according to the standard initialization strategy in
'initialize_q_batch' individually for each inner problem.
Args:
acq_function: The value function instance to be optimized.
bounds: A `2 x d` tensor of lower and upper bounds for each column of
task features.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of raw samples to consider in the initialization
heuristic.
current_model: The model of the KG acquisition function that was used to
generate the fantasy model of the value function.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
options: Options for initial condition generation. These contain all
settings for the standard heuristic initialization from
`gen_batch_initial_conditions`. In addition, they contain
`frac_random` (the fraction of fully random fantasy points),
`num_inner_restarts` and `raw_inner_samples` (the number of random
restarts and raw samples for solving the posterior objective
maximization problem, respectively) and `eta` (temperature parameter
for sampling heuristic from posterior objective maximizers).
Returns:
A `num_restarts x batch_shape x q x d` tensor that can be used as initial
conditions for `optimize_acqf()`. Here `batch_shape` is the batch shape
of value function model.
Example:
>>> fant_X = torch.rand(5, 1, 2)
>>> fantasy_model = model.fantasize(fant_X, SobolQMCNormalSampler(16))
>>> value_function = PosteriorMean(fantasy_model)
>>> bounds = torch.tensor([[0., 0.], [1., 1.]])
>>> Xinit = gen_value_function_initial_conditions(
>>> value_function, bounds, num_restarts=10, raw_samples=512,
>>> options={"frac_random": 0.25},
>>> )
"""
options = options or {}
seed: Optional[int] = options.get("seed")
frac_random: float = options.get("frac_random", 0.6)
if not 0 < frac_random < 1:
raise ValueError(
f"frac_random must take on values in (0,1). Value: {frac_random}"
)
# compute maximizer of the current value function
value_function = _get_value_function(
model=current_model,
objective=getattr(acq_function, "objective", None),
posterior_transform=acq_function.posterior_transform,
sampler=getattr(acq_function, "sampler", None),
project=getattr(acq_function, "project", None),
)
from botorch.optim.optimize import optimize_acqf
fantasy_cands, fantasy_vals = optimize_acqf(
acq_function=value_function,
bounds=bounds,
q=1,
num_restarts=options.get("num_inner_restarts", 20),
raw_samples=options.get("raw_inner_samples", 1024),
fixed_features=fixed_features,
return_best_only=False,
options={
k: v
for k, v in options.items()
if k
not in ("frac_random", "num_inner_restarts", "raw_inner_samples", "eta")
},
)
batch_shape = acq_function.model.batch_shape
# sampling from the optimizers
n_value = int((1 - frac_random) * raw_samples) # number of non-random ICs
if n_value > 0:
eta = options.get("eta", 2.0)
weights = torch.exp(eta * standardize(fantasy_vals))
idx = batched_multinomial(
weights=weights.expand(*batch_shape, -1),
num_samples=n_value,
replacement=True,
).permute(-1, *range(len(batch_shape)))
resampled = fantasy_cands[idx]
else:
resampled = torch.empty(
0,
*batch_shape,
1,
bounds.shape[-1],
dtype=fantasy_cands.dtype,
device=fantasy_cands.device,
)
# add qMC samples
randomized = draw_sobol_samples(
bounds=bounds, n=raw_samples - n_value, q=1, batch_shape=batch_shape, seed=seed
).to(resampled)
# full set of raw samples
X_rnd = torch.cat([resampled, randomized], dim=0)
X_rnd = fix_features(X_rnd, fixed_features=fixed_features)
# evaluate the raw samples
with torch.no_grad():
Y_rnd = acq_function(X_rnd)
# select the restart points using the heuristic
return initialize_q_batch(
X=X_rnd, Y=Y_rnd, n=num_restarts, eta=options.get("eta", 2.0)
)
[docs]
def initialize_q_batch(X: Tensor, Y: Tensor, n: int, eta: float = 1.0) -> Tensor:
r"""Heuristic for selecting initial conditions for candidate generation.
This heuristic selects points from `X` (without replacement) with probability
proportional to `exp(eta * Z)`, where `Z = (Y - mean(Y)) / std(Y)` and `eta`
is a temperature parameter.
When using an acquisiton function that is non-negative and possibly zero
over large areas of the feature space (e.g. qEI), you should use
`initialize_q_batch_nonneg` instead.
Args:
X: A `b x batch_shape x q x d` tensor of `b` - `batch_shape` samples of
`q`-batches from a d`-dim feature space. Typically, these are generated
using qMC sampling.
Y: A tensor of `b x batch_shape` outcomes associated with the samples.
Typically, this is the value of the batch acquisition function to be
maximized.
n: The number of initial condition to be generated. Must be less than `b`.
eta: Temperature parameter for weighting samples.
Returns:
A `n x batch_shape x q x d` tensor of `n` - `batch_shape` `q`-batch initial
conditions, where each batch of `n x q x d` samples is selected independently.
Example:
>>> # To get `n=10` starting points of q-batch size `q=3`
>>> # for model with `d=6`:
>>> qUCB = qUpperConfidenceBound(model, beta=0.1)
>>> Xrnd = torch.rand(500, 3, 6)
>>> Xinit = initialize_q_batch(Xrnd, qUCB(Xrnd), 10)
"""
n_samples = X.shape[0]
batch_shape = X.shape[1:-2] or torch.Size()
if n > n_samples:
raise RuntimeError(
f"n ({n}) cannot be larger than the number of "
f"provided samples ({n_samples})"
)
elif n == n_samples:
return X
Ystd = Y.std(dim=0)
if torch.any(Ystd == 0):
warnings.warn(
"All acquisition values for raw samples points are the same for "
"at least one batch. Choosing initial conditions at random.",
BadInitialCandidatesWarning,
)
return X[torch.randperm(n=n_samples, device=X.device)][:n]
max_val, max_idx = torch.max(Y, dim=0)
Z = (Y - Y.mean(dim=0)) / Ystd
etaZ = eta * Z
weights = torch.exp(etaZ)
while torch.isinf(weights).any():
etaZ *= 0.5
weights = torch.exp(etaZ)
if batch_shape == torch.Size():
idcs = torch.multinomial(weights, n)
else:
idcs = batched_multinomial(
weights=weights.permute(*range(1, len(batch_shape) + 1), 0), num_samples=n
).permute(-1, *range(len(batch_shape)))
# make sure we get the maximum
if max_idx not in idcs:
idcs[-1] = max_idx
if batch_shape == torch.Size():
return X[idcs]
else:
return X.gather(
dim=0, index=idcs.view(*idcs.shape, 1, 1).expand(n, *X.shape[1:])
)
[docs]
def initialize_q_batch_nonneg(
X: Tensor, Y: Tensor, n: int, eta: float = 1.0, alpha: float = 1e-4
) -> Tensor:
r"""Heuristic for selecting initial conditions for non-neg. acquisition functions.
This function is similar to `initialize_q_batch`, but designed specifically
for acquisition functions that are non-negative and possibly zero over
large areas of the feature space (e.g. qEI). All samples for which
`Y < alpha * max(Y)` will be ignored (assuming that `Y` contains at least
one positive value).
Args:
X: A `b x q x d` tensor of `b` samples of `q`-batches from a `d`-dim.
feature space. Typically, these are generated using qMC.
Y: A tensor of `b` outcomes associated with the samples. Typically, this
is the value of the batch acquisition function to be maximized.
n: The number of initial condition to be generated. Must be less than `b`.
eta: Temperature parameter for weighting samples.
alpha: The threshold (as a fraction of the maximum observed value) under
which to ignore samples. All input samples for which
`Y < alpha * max(Y)` will be ignored.
Returns:
A `n x q x d` tensor of `n` `q`-batch initial conditions.
Example:
>>> # To get `n=10` starting points of q-batch size `q=3`
>>> # for model with `d=6`:
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> Xrnd = torch.rand(500, 3, 6)
>>> Xinit = initialize_q_batch(Xrnd, qEI(Xrnd), 10)
"""
n_samples = X.shape[0]
if n > n_samples:
raise RuntimeError("n cannot be larger than the number of provided samples")
elif n == n_samples:
return X
max_val, max_idx = torch.max(Y, dim=0)
if torch.any(max_val <= 0):
warnings.warn(
"All acquisition values for raw sampled points are nonpositive, so "
"initial conditions are being selected randomly.",
BadInitialCandidatesWarning,
)
return X[torch.randperm(n=n_samples, device=X.device)][:n]
# make sure there are at least `n` points with positive acquisition values
pos = Y > 0
num_pos = pos.sum().item()
if num_pos < n:
# select all positive points and then fill remaining quota with randomly
# selected points
remaining_indices = (~pos).nonzero(as_tuple=False).view(-1)
rand_indices = torch.randperm(remaining_indices.shape[0], device=Y.device)
sampled_remaining_indices = remaining_indices[rand_indices[: n - num_pos]]
pos[sampled_remaining_indices] = 1
return X[pos]
# select points within alpha of max_val, iteratively decreasing alpha by a
# factor of 10 as necessary
alpha_pos = Y >= alpha * max_val
while alpha_pos.sum() < n:
alpha = 0.1 * alpha
alpha_pos = Y >= alpha * max_val
alpha_pos_idcs = torch.arange(len(Y), device=Y.device)[alpha_pos]
weights = torch.exp(eta * (Y[alpha_pos] / max_val - 1))
idcs = alpha_pos_idcs[torch.multinomial(weights, n)]
if max_idx not in idcs:
idcs[-1] = max_idx
return X[idcs]
[docs]
def sample_points_around_best(
acq_function: AcquisitionFunction,
n_discrete_points: int,
sigma: float,
bounds: Tensor,
best_pct: float = 5.0,
subset_sigma: float = 1e-1,
prob_perturb: Optional[float] = None,
) -> Optional[Tensor]:
r"""Find best points and sample nearby points.
Args:
acq_function: The acquisition function.
n_discrete_points: The number of points to sample.
sigma: The standard deviation of the additive gaussian noise for
perturbing the best points.
bounds: A `2 x d`-dim tensor containing the bounds.
best_pct: The percentage of best points to perturb.
subset_sigma: The standard deviation of the additive gaussian
noise for perturbing a subset of dimensions of the best points.
prob_perturb: The probability of perturbing each dimension.
Returns:
An optional `n_discrete_points x d`-dim tensor containing the
sampled points. This is None if no baseline points are found.
"""
X = get_X_baseline(acq_function=acq_function)
if X is None:
return
with torch.no_grad():
try:
posterior = acq_function.model.posterior(X)
except AttributeError:
warnings.warn(
"Failed to sample around previous best points.",
BotorchWarning,
)
return
mean = posterior.mean
while mean.ndim > 2:
# take average over batch dims
mean = mean.mean(dim=0)
try:
f_pred = acq_function.objective(mean)
# Some acquisition functions do not have an objective
# and for some acquisition functions the objective is None
except (AttributeError, TypeError):
f_pred = mean
if hasattr(acq_function, "maximize"):
# make sure that the optimiztaion direction is set properly
if not acq_function.maximize:
f_pred = -f_pred
try:
# handle constraints for EHVI-based acquisition functions
constraints = acq_function.constraints
if constraints is not None:
neg_violation = -torch.stack(
[c(mean).clamp_min(0.0) for c in constraints], dim=-1
).sum(dim=-1)
feas = neg_violation == 0
if feas.any():
f_pred[~feas] = float("-inf")
else:
# set objective equal to negative violation
f_pred = neg_violation
except AttributeError:
pass
if f_pred.ndim == mean.ndim and f_pred.shape[-1] > 1:
# multi-objective
# find pareto set
is_pareto = is_non_dominated(f_pred)
best_X = X[is_pareto]
else:
if f_pred.shape[-1] == 1:
f_pred = f_pred.squeeze(-1)
n_best = max(1, round(X.shape[0] * best_pct / 100))
# the view() is to ensure that best_idcs is not a scalar tensor
best_idcs = torch.topk(f_pred, n_best).indices.view(-1)
best_X = X[best_idcs]
use_perturbed_sampling = best_X.shape[-1] >= 20 or prob_perturb is not None
n_trunc_normal_points = (
n_discrete_points // 2 if use_perturbed_sampling else n_discrete_points
)
perturbed_X = sample_truncated_normal_perturbations(
X=best_X,
n_discrete_points=n_trunc_normal_points,
sigma=sigma,
bounds=bounds,
)
if use_perturbed_sampling:
perturbed_subset_dims_X = sample_perturbed_subset_dims(
X=best_X,
bounds=bounds,
# ensure that we return n_discrete_points
n_discrete_points=n_discrete_points - n_trunc_normal_points,
sigma=sigma,
prob_perturb=prob_perturb,
)
perturbed_X = torch.cat([perturbed_X, perturbed_subset_dims_X], dim=0)
# shuffle points
perm = torch.randperm(perturbed_X.shape[0], device=X.device)
perturbed_X = perturbed_X[perm]
return perturbed_X
[docs]
def sample_truncated_normal_perturbations(
X: Tensor,
n_discrete_points: int,
sigma: float,
bounds: Tensor,
qmc: bool = True,
) -> Tensor:
r"""Sample points around `X`.
Sample perturbed points around `X` such that the added perturbations
are sampled from N(0, sigma^2 I) and truncated to be within [0,1]^d.
Args:
X: A `n x d`-dim tensor starting points.
n_discrete_points: The number of points to sample.
sigma: The standard deviation of the additive gaussian noise for
perturbing the points.
bounds: A `2 x d`-dim tensor containing the bounds.
qmc: A boolean indicating whether to use qmc.
Returns:
A `n_discrete_points x d`-dim tensor containing the sampled points.
"""
X = normalize(X, bounds=bounds)
d = X.shape[1]
# sample points from N(X_center, sigma^2 I), truncated to be within
# [0, 1]^d.
if X.shape[0] > 1:
rand_indices = torch.randint(X.shape[0], (n_discrete_points,), device=X.device)
X = X[rand_indices]
if qmc:
std_bounds = torch.zeros(2, d, dtype=X.dtype, device=X.device)
std_bounds[1] = 1
u = draw_sobol_samples(bounds=std_bounds, n=n_discrete_points, q=1).squeeze(1)
else:
u = torch.rand((n_discrete_points, d), dtype=X.dtype, device=X.device)
# compute bounds to sample from
a = -X
b = 1 - X
# compute z-score of bounds
alpha = a / sigma
beta = b / sigma
normal = Normal(0, 1)
cdf_alpha = normal.cdf(alpha)
# use inverse transform
perturbation = normal.icdf(cdf_alpha + u * (normal.cdf(beta) - cdf_alpha)) * sigma
# add perturbation and clip points that are still outside
perturbed_X = (X + perturbation).clamp(0.0, 1.0)
return unnormalize(perturbed_X, bounds=bounds)
[docs]
def sample_perturbed_subset_dims(
X: Tensor,
bounds: Tensor,
n_discrete_points: int,
sigma: float = 1e-1,
qmc: bool = True,
prob_perturb: Optional[float] = None,
) -> Tensor:
r"""Sample around `X` by perturbing a subset of the dimensions.
By default, dimensions are perturbed with probability equal to
`min(20 / d, 1)`. As shown in [Regis]_, perturbing a small number
of dimensions can be beneificial. The perturbations are sampled
from N(0, sigma^2 I) and truncated to be within [0,1]^d.
Args:
X: A `n x d`-dim tensor starting points. `X`
must be normalized to be within `[0, 1]^d`.
bounds: The bounds to sample perturbed values from
n_discrete_points: The number of points to sample.
sigma: The standard deviation of the additive gaussian noise for
perturbing the points.
qmc: A boolean indicating whether to use qmc.
prob_perturb: The probability of perturbing each dimension. If omitted,
defaults to `min(20 / d, 1)`.
Returns:
A `n_discrete_points x d`-dim tensor containing the sampled points.
"""
if bounds.ndim != 2:
raise BotorchTensorDimensionError("bounds must be a `2 x d`-dim tensor.")
elif X.ndim != 2:
raise BotorchTensorDimensionError("X must be a `n x d`-dim tensor.")
d = bounds.shape[-1]
if prob_perturb is None:
# Only perturb a subset of the features
prob_perturb = min(20.0 / d, 1.0)
if X.shape[0] == 1:
X_cand = X.repeat(n_discrete_points, 1)
else:
rand_indices = torch.randint(X.shape[0], (n_discrete_points,), device=X.device)
X_cand = X[rand_indices]
pert = sample_truncated_normal_perturbations(
X=X_cand,
n_discrete_points=n_discrete_points,
sigma=sigma,
bounds=bounds,
qmc=qmc,
)
# find cases where we are not perturbing any dimensions
mask = (
torch.rand(
n_discrete_points,
d,
dtype=bounds.dtype,
device=bounds.device,
)
<= prob_perturb
)
ind = (~mask).all(dim=-1).nonzero()
# perturb `n_perturb` of the dimensions
n_perturb = ceil(d * prob_perturb)
perturb_mask = torch.zeros(d, dtype=mask.dtype, device=mask.device)
perturb_mask[:n_perturb].fill_(1)
# TODO: use batched `torch.randperm` when available:
# https://github.com/pytorch/pytorch/issues/42502
for idx in ind:
mask[idx] = perturb_mask[torch.randperm(d, device=bounds.device)]
# Create candidate points
X_cand[mask] = pert[mask]
return X_cand
[docs]
def is_nonnegative(acq_function: AcquisitionFunction) -> bool:
r"""Determine whether a given acquisition function is non-negative.
Args:
acq_function: The `AcquisitionFunction` instance.
Returns:
True if `acq_function` is non-negative, False if not, or if the behavior
is unknown (for custom acquisition functions).
Example:
>>> qEI = qExpectedImprovement(model, best_f=0.1)
>>> is_nonnegative(qEI) # returns True
"""
return isinstance(
acq_function,
(
analytic.ExpectedImprovement,
analytic.ConstrainedExpectedImprovement,
analytic.ProbabilityOfImprovement,
analytic.NoisyExpectedImprovement,
monte_carlo.qExpectedImprovement,
monte_carlo.qNoisyExpectedImprovement,
monte_carlo.qProbabilityOfImprovement,
multi_objective.analytic.ExpectedHypervolumeImprovement,
multi_objective.monte_carlo.qExpectedHypervolumeImprovement,
multi_objective.monte_carlo.qNoisyExpectedHypervolumeImprovement,
),
)
