#!/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"""
Methods for optimizing acquisition functions.
"""
from __future__ import annotations
import dataclasses
import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from botorch.acquisition.acquisition import (
AcquisitionFunction,
OneShotAcquisitionFunction,
)
from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import (
qHypervolumeKnowledgeGradient,
)
from botorch.exceptions import InputDataError, UnsupportedError
from botorch.exceptions.warnings import OptimizationWarning
from botorch.generation.gen import gen_candidates_scipy, TGenCandidates
from botorch.logging import logger
from botorch.optim.initializers import (
gen_batch_initial_conditions,
gen_one_shot_hvkg_initial_conditions,
gen_one_shot_kg_initial_conditions,
TGenInitialConditions,
)
from botorch.optim.stopping import ExpMAStoppingCriterion
from botorch.optim.utils import _filter_kwargs
from torch import Tensor
INIT_OPTION_KEYS = {
# set of options for initialization that we should
# not pass to scipy.optimize.minimize to avoid
# warnings
"alpha",
"batch_limit",
"eta",
"init_batch_limit",
"nonnegative",
"n_burnin",
"sample_around_best",
"sample_around_best_sigma",
"sample_around_best_prob_perturb",
"seed",
"thinning",
}
def _raise_deprecation_warning_if_kwargs(fn_name: str, kwargs: Dict[str, Any]) -> None:
"""
Raise a warning if kwargs are provided.
Some functions used to support **kwargs. The applicable parameters have now been
refactored to be named arguments, so no warning will be raised for users passing
the expected arguments. However, if a user had been passing an inapplicable
keyword argument, this will now raise a warning whereas in the past it did
nothing.
"""
if len(kwargs) > 0:
warnings.warn(
f"`{fn_name}` does not support arguments {list(kwargs.keys())}. In "
"the future, this will become an error.",
DeprecationWarning,
stacklevel=2,
)
def _optimize_acqf_all_features_fixed(
*,
bounds: Tensor,
fixed_features: Dict[int, float],
q: int,
acq_function: AcquisitionFunction,
) -> Tuple[Tensor, Tensor]:
"""
Helper function for `optimize_acqf` for the trivial case where
all features are fixed.
"""
X = torch.tensor(
[fixed_features[i] for i in range(bounds.shape[-1])],
device=bounds.device,
dtype=bounds.dtype,
)
X = X.expand(q, *X.shape)
with torch.no_grad():
acq_value = acq_function(X)
return X, acq_value
def _validate_sequential_inputs(opt_inputs: OptimizeAcqfInputs) -> None:
# validate that linear constraints across the q-dim and
# self.sequential are not present together
if opt_inputs.inequality_constraints is not None:
for constraint in opt_inputs.inequality_constraints:
if len(constraint[0].shape) > 1:
raise UnsupportedError(
"Linear inequality constraints across the q-dimension are not "
"supported for sequential optimization."
)
if opt_inputs.equality_constraints is not None:
for constraint in opt_inputs.equality_constraints:
if len(constraint[0].shape) > 1:
raise UnsupportedError(
"Linear equality constraints across the q-dimension are not "
"supported for sequential optimization."
)
# TODO: Validate constraints if provided:
# https://github.com/pytorch/botorch/pull/1231
if opt_inputs.batch_initial_conditions is not None:
raise UnsupportedError(
"`batch_initial_conditions` is not supported for sequential "
"optimization. Either avoid specifying "
"`batch_initial_conditions` to use the custom initializer or "
"use the `ic_generator` kwarg to generate initial conditions "
"for the case of nonlinear inequality constraints."
)
if not opt_inputs.return_best_only:
raise NotImplementedError(
"`return_best_only=False` only supported for joint optimization."
)
if isinstance(opt_inputs.acq_function, OneShotAcquisitionFunction):
raise NotImplementedError(
"sequential optimization currently not supported for one-shot "
"acquisition functions. Must have `sequential=False`."
)
def _optimize_acqf_sequential_q(
opt_inputs: OptimizeAcqfInputs,
) -> Tuple[Tensor, Tensor]:
"""
Helper function for `optimize_acqf` when sequential=True and q > 1.
For each of `q` times, generate a single candidate greedily, then add it to
the list of pending points.
"""
_validate_sequential_inputs(opt_inputs)
# When using sequential optimization, we allocate the total timeout
# evenly across the individual acquisition optimizations.
timeout_sec = (
opt_inputs.timeout_sec / opt_inputs.q
if opt_inputs.timeout_sec is not None
else None
)
candidate_list, acq_value_list = [], []
base_X_pending = opt_inputs.acq_function.X_pending
new_inputs = dataclasses.replace(
opt_inputs,
q=1,
batch_initial_conditions=None,
return_best_only=True,
sequential=False,
timeout_sec=timeout_sec,
)
for i in range(opt_inputs.q):
candidate, acq_value = _optimize_acqf_batch(new_inputs)
candidate_list.append(candidate)
acq_value_list.append(acq_value)
candidates = torch.cat(candidate_list, dim=-2)
new_inputs.acq_function.set_X_pending(
torch.cat([base_X_pending, candidates], dim=-2)
if base_X_pending is not None
else candidates
)
logger.info(f"Generated sequential candidate {i+1} of {opt_inputs.q}")
opt_inputs.acq_function.set_X_pending(base_X_pending)
return candidates, torch.stack(acq_value_list)
def _optimize_acqf_batch(opt_inputs: OptimizeAcqfInputs) -> Tuple[Tensor, Tensor]:
options = opt_inputs.options or {}
initial_conditions_provided = opt_inputs.batch_initial_conditions is not None
if initial_conditions_provided:
batch_initial_conditions = opt_inputs.batch_initial_conditions
else:
# pyre-ignore[28]: Unexpected keyword argument `acq_function` to anonymous call.
batch_initial_conditions = opt_inputs.get_ic_generator()(
acq_function=opt_inputs.acq_function,
bounds=opt_inputs.bounds,
q=opt_inputs.q,
num_restarts=opt_inputs.num_restarts,
raw_samples=opt_inputs.raw_samples,
fixed_features=opt_inputs.fixed_features,
options=options,
inequality_constraints=opt_inputs.inequality_constraints,
equality_constraints=opt_inputs.equality_constraints,
**opt_inputs.ic_gen_kwargs,
)
batch_limit: int = options.get(
"batch_limit",
(
opt_inputs.num_restarts
if not opt_inputs.nonlinear_inequality_constraints
else 1
),
)
def _optimize_batch_candidates() -> Tuple[Tensor, Tensor, List[Warning]]:
batch_candidates_list: List[Tensor] = []
batch_acq_values_list: List[Tensor] = []
batched_ics = batch_initial_conditions.split(batch_limit)
opt_warnings = []
timeout_sec = (
opt_inputs.timeout_sec / len(batched_ics)
if opt_inputs.timeout_sec is not None
else None
)
bounds = opt_inputs.bounds
gen_kwargs: Dict[str, Any] = {
"lower_bounds": None if bounds[0].isinf().all() else bounds[0],
"upper_bounds": None if bounds[1].isinf().all() else bounds[1],
"options": {k: v for k, v in options.items() if k not in INIT_OPTION_KEYS},
"fixed_features": opt_inputs.fixed_features,
"timeout_sec": timeout_sec,
}
# only add parameter constraints to gen_kwargs if they are specified
# to avoid unnecessary warnings in _filter_kwargs
for constraint_name in [
"inequality_constraints",
"equality_constraints",
"nonlinear_inequality_constraints",
]:
if (constraint := getattr(opt_inputs, constraint_name)) is not None:
gen_kwargs[constraint_name] = constraint
filtered_gen_kwargs = _filter_kwargs(opt_inputs.gen_candidates, **gen_kwargs)
for i, batched_ics_ in enumerate(batched_ics):
# optimize using random restart optimization
with warnings.catch_warnings(record=True) as ws:
warnings.simplefilter("always", category=OptimizationWarning)
(
batch_candidates_curr,
batch_acq_values_curr,
) = opt_inputs.gen_candidates(
batched_ics_, opt_inputs.acq_function, **filtered_gen_kwargs
)
opt_warnings += ws
batch_candidates_list.append(batch_candidates_curr)
batch_acq_values_list.append(batch_acq_values_curr)
logger.info(f"Generated candidate batch {i+1} of {len(batched_ics)}.")
batch_candidates = torch.cat(batch_candidates_list)
has_scalars = batch_acq_values_list[0].ndim == 0
if has_scalars:
batch_acq_values = torch.stack(batch_acq_values_list)
else:
batch_acq_values = torch.cat(batch_acq_values_list).flatten()
return batch_candidates, batch_acq_values, opt_warnings
batch_candidates, batch_acq_values, ws = _optimize_batch_candidates()
optimization_warning_raised = any(
(issubclass(w.category, OptimizationWarning) for w in ws)
)
if optimization_warning_raised and opt_inputs.retry_on_optimization_warning:
first_warn_msg = (
"Optimization failed in `gen_candidates_scipy` with the following "
f"warning(s):\n{[w.message for w in ws]}\nBecause you specified "
"`batch_initial_conditions`, optimization will not be retried with "
"new initial conditions and will proceed with the current solution."
" Suggested remediation: Try again with different "
"`batch_initial_conditions`, or don't provide `batch_initial_conditions.`"
if initial_conditions_provided
else "Optimization failed in `gen_candidates_scipy` with the following "
f"warning(s):\n{[w.message for w in ws]}\nTrying again with a new "
"set of initial conditions."
)
warnings.warn(first_warn_msg, RuntimeWarning, stacklevel=2)
if not initial_conditions_provided:
batch_initial_conditions = opt_inputs.get_ic_generator()(
acq_function=opt_inputs.acq_function,
bounds=opt_inputs.bounds,
q=opt_inputs.q,
num_restarts=opt_inputs.num_restarts,
raw_samples=opt_inputs.raw_samples,
fixed_features=opt_inputs.fixed_features,
options=options,
inequality_constraints=opt_inputs.inequality_constraints,
equality_constraints=opt_inputs.equality_constraints,
**opt_inputs.ic_gen_kwargs,
)
batch_candidates, batch_acq_values, ws = _optimize_batch_candidates()
optimization_warning_raised = any(
(issubclass(w.category, OptimizationWarning) for w in ws)
)
if optimization_warning_raised:
warnings.warn(
"Optimization failed on the second try, after generating a "
"new set of initial conditions.",
RuntimeWarning,
stacklevel=2,
)
if opt_inputs.post_processing_func is not None:
batch_candidates = opt_inputs.post_processing_func(batch_candidates)
with torch.no_grad():
acq_values_list = [
opt_inputs.acq_function(cand)
for cand in batch_candidates.split(batch_limit, dim=0)
]
batch_acq_values = torch.cat(acq_values_list, dim=0)
if opt_inputs.return_best_only:
best = torch.argmax(batch_acq_values.view(-1), dim=0)
batch_candidates = batch_candidates[best]
batch_acq_values = batch_acq_values[best]
if not opt_inputs.full_tree:
batch_candidates = opt_inputs.acq_function.extract_candidates(
X_full=batch_candidates
)
return batch_candidates, batch_acq_values
[docs]
def optimize_acqf(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
nonlinear_inequality_constraints: Optional[List[Tuple[Callable, bool]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
batch_initial_conditions: Optional[Tensor] = None,
return_best_only: bool = True,
gen_candidates: Optional[TGenCandidates] = None,
sequential: bool = False,
*,
ic_generator: Optional[TGenInitialConditions] = None,
timeout_sec: Optional[float] = None,
return_full_tree: bool = False,
retry_on_optimization_warning: bool = True,
**ic_gen_kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""Generate a set of candidates via multi-start optimization.
Args:
acq_function: An AcquisitionFunction.
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`
(if inequality_constraints is provided, these bounds can be -inf and
+inf, respectively).
q: The number of candidates.
num_restarts: The number of starting points for multistart acquisition
function optimization.
raw_samples: The number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
options: Options for candidate generation.
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`. `indices` and
`coefficients` should be torch tensors. See the docstring of
`make_scipy_linear_constraints` for an example. When q=1, or when
applying the same constraint to each candidate in the batch
(intra-point constraint), `indices` should be a 1-d tensor.
For inter-point constraints, in which the constraint is applied to the
whole batch of candidates, `indices` must be a 2-d tensor, where
in each row `indices[i] =(k_i, l_i)` the first index `k_i` corresponds
to the `k_i`-th element of the `q`-batch and the second index `l_i`
corresponds to the `l_i`-th feature of that element.
equality_constraints: A list of tuples (indices, coefficients, rhs),
with each tuple encoding an equality constraint of the form
`\sum_i (X[indices[i]] * coefficients[i]) = rhs`. See the docstring of
`make_scipy_linear_constraints` for an example.
nonlinear_inequality_constraints: A list of tuples representing the nonlinear
inequality constraints. The first element in the tuple is a callable
representing a constraint of the form `callable(x) >= 0`. In case of an
intra-point constraint, `callable()`takes in an one-dimensional tensor of
shape `d` and returns a scalar. In case of an inter-point constraint,
`callable()` takes a two dimensional tensor of shape `q x d` and again
returns a scalar. The second element is a boolean, indicating if it is an
intra-point or inter-point constraint (`True` for intra-point. `False` for
inter-point). For more information on intra-point vs inter-point
constraints, see the docstring of the `inequality_constraints` argument to
`optimize_acqf()`. The constraints will later be passed to the scipy
solver. You need to pass in `batch_initial_conditions` in this case.
Using non-linear inequality constraints also requires that `batch_limit`
is set to 1, which will be done automatically if not specified in
`options`.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
batch_initial_conditions: A tensor to specify the initial conditions. Set
this if you do not want to use default initialization strategy.
return_best_only: If False, outputs the solutions corresponding to all
random restart initializations of the optimization.
gen_candidates: A callable for generating candidates (and their associated
acquisition values) given a tensor of initial conditions and an
acquisition function. Other common inputs include lower and upper bounds
and a dictionary of options, but refer to the documentation of specific
generation functions (e.g gen_candidates_scipy and gen_candidates_torch)
for method-specific inputs. Default: `gen_candidates_scipy`
sequential: If False, uses joint optimization, otherwise uses sequential
optimization.
ic_generator: Function for generating initial conditions. Not needed when
`batch_initial_conditions` are provided. Defaults to
`gen_one_shot_kg_initial_conditions` for `qKnowledgeGradient` acquisition
functions and `gen_batch_initial_conditions` otherwise. Must be specified
for nonlinear inequality constraints.
timeout_sec: Max amount of time optimization can run for.
return_full_tree:
retry_on_optimization_warning: Whether to retry candidate generation with a new
set of initial conditions when it fails with an `OptimizationWarning`.
ic_gen_kwargs: Additional keyword arguments passed to function specified by
`ic_generator`
Returns:
A two-element tuple containing
- A tensor of generated candidates. The shape is
-- `q x d` if `return_best_only` is True (default)
-- `num_restarts x q x d` if `return_best_only` is False
- a tensor of associated acquisition values. If `sequential=False`,
this is a `(num_restarts)`-dim tensor of joint acquisition values
(with explicit restart dimension if `return_best_only=False`). If
`sequential=True`, this is a `q`-dim tensor of expected acquisition
values conditional on having observed candidates `0,1,...,i-1`.
Example:
>>> # generate `q=2` candidates jointly using 20 random restarts
>>> # and 512 raw samples
>>> candidates, acq_value = optimize_acqf(qEI, bounds, 2, 20, 512)
>>> generate `q=3` candidates sequentially using 15 random restarts
>>> # and 256 raw samples
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0.], [1.]])
>>> candidates, acq_value_list = optimize_acqf(
>>> qEI, bounds, 3, 15, 256, sequential=True
>>> )
"""
# using a default of None simplifies unit testing
if gen_candidates is None:
gen_candidates = gen_candidates_scipy
opt_acqf_inputs = OptimizeAcqfInputs(
acq_function=acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
nonlinear_inequality_constraints=nonlinear_inequality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
return_best_only=return_best_only,
gen_candidates=gen_candidates,
sequential=sequential,
ic_generator=ic_generator,
timeout_sec=timeout_sec,
return_full_tree=return_full_tree,
retry_on_optimization_warning=retry_on_optimization_warning,
ic_gen_kwargs=ic_gen_kwargs,
)
return _optimize_acqf(opt_acqf_inputs)
def _optimize_acqf(opt_inputs: OptimizeAcqfInputs) -> Tuple[Tensor, Tensor]:
# Handle the trivial case when all features are fixed
if (
opt_inputs.fixed_features is not None
and len(opt_inputs.fixed_features) == opt_inputs.bounds.shape[-1]
):
return _optimize_acqf_all_features_fixed(
bounds=opt_inputs.bounds,
fixed_features=opt_inputs.fixed_features,
q=opt_inputs.q,
acq_function=opt_inputs.acq_function,
)
# Perform sequential optimization via successive conditioning on pending points
if opt_inputs.sequential and opt_inputs.q > 1:
return _optimize_acqf_sequential_q(opt_inputs=opt_inputs)
# Batch optimization (including the case q=1)
return _optimize_acqf_batch(opt_inputs=opt_inputs)
[docs]
def optimize_acqf_cyclic(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
batch_initial_conditions: Optional[Tensor] = None,
cyclic_options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
*,
ic_generator: Optional[TGenInitialConditions] = None,
timeout_sec: Optional[float] = None,
return_full_tree: bool = False,
retry_on_optimization_warning: bool = True,
**ic_gen_kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""Generate a set of `q` candidates via cyclic optimization.
Args:
acq_function: An AcquisitionFunction
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`
(if inequality_constraints is provided, these bounds can be -inf and
+inf, respectively).
q: The number of candidates.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
options: Options for candidate generation.
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`
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
batch_initial_conditions: A tensor to specify the initial conditions.
If no initial conditions are provided, the default initialization will
be used.
cyclic_options: Options for stopping criterion for outer cyclic optimization.
ic_generator: Function for generating initial conditions. Not needed when
`batch_initial_conditions` are provided. Defaults to
`gen_one_shot_kg_initial_conditions` for `qKnowledgeGradient` acquisition
functions and `gen_batch_initial_conditions` otherwise. Must be specified
for nonlinear inequality constraints.
timeout_sec: Max amount of time optimization can run for.
return_full_tree:
retry_on_optimization_warning: Whether to retry candidate generation with a new
set of initial conditions when it fails with an `OptimizationWarning`.
ic_gen_kwargs: Additional keyword arguments passed to function specified by
`ic_generator`
Returns:
A two-element tuple containing
- a `q x d`-dim tensor of generated candidates.
- a `q`-dim tensor of expected acquisition values, where the value at
index `i` is the acquisition value conditional on having observed
all candidates except candidate `i`.
Example:
>>> # generate `q=3` candidates cyclically using 15 random restarts
>>> # 256 raw samples, and 4 cycles
>>>
>>> qEI = qExpectedImprovement(model, best_f=0.2)
>>> bounds = torch.tensor([[0.], [1.]])
>>> candidates, acq_value_list = optimize_acqf_cyclic(
>>> qEI, bounds, 3, 15, 256, cyclic_options={"maxiter": 4}
>>> )
"""
opt_inputs = OptimizeAcqfInputs(
acq_function=acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
nonlinear_inequality_constraints=None,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
return_best_only=True,
gen_candidates=gen_candidates_scipy,
sequential=True,
ic_generator=ic_generator,
timeout_sec=timeout_sec,
return_full_tree=return_full_tree,
retry_on_optimization_warning=retry_on_optimization_warning,
ic_gen_kwargs=ic_gen_kwargs,
)
# for the first cycle, optimize the q candidates sequentially
candidates, acq_vals = _optimize_acqf(opt_inputs)
q = opt_inputs.q
opt_inputs = dataclasses.replace(opt_inputs, q=1)
acq_function = opt_inputs.acq_function
if q > 1:
cyclic_options = cyclic_options or {}
stopping_criterion = ExpMAStoppingCriterion(**cyclic_options)
stop = stopping_criterion.evaluate(fvals=acq_vals)
base_X_pending = acq_function.X_pending
idxr = torch.ones(q, dtype=torch.bool, device=opt_inputs.bounds.device)
while not stop:
for i in range(q):
# optimize only candidate i
idxr[i] = 0
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates[idxr]], dim=-2)
if base_X_pending is not None
else candidates[idxr]
)
opt_inputs = dataclasses.replace(
opt_inputs,
batch_initial_conditions=candidates[i].unsqueeze(0),
sequential=False,
)
candidate_i, acq_val_i = _optimize_acqf(opt_inputs)
candidates[i] = candidate_i
acq_vals[i] = acq_val_i
idxr[i] = 1
stop = stopping_criterion.evaluate(fvals=acq_vals)
acq_function.set_X_pending(base_X_pending)
return candidates, acq_vals
[docs]
def optimize_acqf_list(
acq_function_list: List[AcquisitionFunction],
bounds: Tensor,
num_restarts: int,
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
nonlinear_inequality_constraints: Optional[List[Tuple[Callable, bool]]] = None,
fixed_features: Optional[Dict[int, float]] = None,
fixed_features_list: Optional[List[Dict[int, float]]] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
ic_generator: Optional[TGenInitialConditions] = None,
ic_gen_kwargs: Optional[Dict] = None,
) -> Tuple[Tensor, Tensor]:
r"""Generate a list of candidates from a list of acquisition functions.
The acquisition functions are optimized in sequence, with previous candidates
set as `X_pending`. This is also known as sequential greedy optimization.
Args:
acq_function_list: A list of acquisition functions.
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`
(if inequality_constraints is provided, these bounds can be -inf and
+inf, respectively).
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
options: Options for candidate generation.
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`
nonlinear_inequality_constraints: A list of tuples representing the nonlinear
inequality constraints. The first element in the tuple is a callable
representing a constraint of the form `callable(x) >= 0`. In case of an
intra-point constraint, `callable()`takes in an one-dimensional tensor of
shape `d` and returns a scalar. In case of an inter-point constraint,
`callable()` takes a two dimensional tensor of shape `q x d` and again
returns a scalar. The second element is a boolean, indicating if it is an
intra-point or inter-point constraint (`True` for intra-point. `False` for
inter-point). For more information on intra-point vs inter-point
constraints, see the docstring of the `inequality_constraints` argument to
`optimize_acqf()`. The constraints will later be passed to the scipy
solver. You need to pass in `batch_initial_conditions` in this case.
Using non-linear inequality constraints also requires that `batch_limit`
is set to 1, which will be done automatically if not specified in
`options`.
fixed_features: A map `{feature_index: value}` for features that
should be fixed to a particular value during generation.
fixed_features_list: A list of maps `{feature_index: value}`. The i-th
item represents the fixed_feature for the i-th optimization. If
`fixed_features_list` is provided, `optimize_acqf_mixed` is invoked.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
ic_generator: Function for generating initial conditions. Not needed when
`batch_initial_conditions` are provided. Defaults to
`gen_one_shot_kg_initial_conditions` for `qKnowledgeGradient` acquisition
functions and `gen_batch_initial_conditions` otherwise. Must be specified
for nonlinear inequality constraints.
ic_gen_kwargs: Additional keyword arguments passed to function specified by
`ic_generator`
Returns:
A two-element tuple containing
- a `q x d`-dim tensor of generated candidates.
- a `q`-dim tensor of expected acquisition values, where the value at
index `i` is the acquisition value conditional on having observed
all candidates except candidate `i`.
"""
if fixed_features and fixed_features_list:
raise ValueError(
"Èither `fixed_feature` or `fixed_features_list` can be provided, not both."
)
if not acq_function_list:
raise ValueError("acq_function_list must be non-empty.")
candidate_list, acq_value_list = [], []
candidates = torch.tensor([], device=bounds.device, dtype=bounds.dtype)
base_X_pending = acq_function_list[0].X_pending
for acq_function in acq_function_list:
if candidate_list:
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates], dim=-2)
if base_X_pending is not None
else candidates
)
if fixed_features_list:
candidate, acq_value = optimize_acqf_mixed(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options or {},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
nonlinear_inequality_constraints=nonlinear_inequality_constraints,
fixed_features_list=fixed_features_list,
post_processing_func=post_processing_func,
ic_generator=ic_generator,
ic_gen_kwargs=ic_gen_kwargs,
)
else:
ic_gen_kwargs = ic_gen_kwargs or {}
candidate, acq_value = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options or {},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
nonlinear_inequality_constraints=nonlinear_inequality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
return_best_only=True,
sequential=False,
ic_generator=ic_generator,
**ic_gen_kwargs,
)
candidate_list.append(candidate)
acq_value_list.append(acq_value)
candidates = torch.cat(candidate_list, dim=-2)
return candidates, torch.stack(acq_value_list)
[docs]
def optimize_acqf_mixed(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
fixed_features_list: List[Dict[int, float]],
raw_samples: Optional[int] = None,
options: Optional[Dict[str, Union[bool, float, int, str]]] = None,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
nonlinear_inequality_constraints: Optional[List[Tuple[Callable, bool]]] = None,
post_processing_func: Optional[Callable[[Tensor], Tensor]] = None,
batch_initial_conditions: Optional[Tensor] = None,
ic_generator: Optional[TGenInitialConditions] = None,
ic_gen_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""Optimize over a list of fixed_features and returns the best solution.
This is useful for optimizing over mixed continuous and discrete domains.
For q > 1 this function always performs sequential greedy optimization (with
proper conditioning on generated candidates).
Args:
acq_function: An AcquisitionFunction
bounds: A `2 x d` tensor of lower and upper bounds for each column of `X`
(if inequality_constraints is provided, these bounds can be -inf and
+inf, respectively).
q: The number of candidates.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
fixed_features_list: A list of maps `{feature_index: value}`. The i-th
item represents the fixed_feature for the i-th optimization.
options: Options for candidate generation.
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`
nonlinear_inequality_constraints: A list of tuples representing the nonlinear
inequality constraints. The first element in the tuple is a callable
representing a constraint of the form `callable(x) >= 0`. In case of an
intra-point constraint, `callable()`takes in an one-dimensional tensor of
shape `d` and returns a scalar. In case of an inter-point constraint,
`callable()` takes a two dimensional tensor of shape `q x d` and again
returns a scalar. The second element is a boolean, indicating if it is an
intra-point or inter-point constraint (`True` for intra-point. `False` for
inter-point). For more information on intra-point vs inter-point
constraints, see the docstring of the `inequality_constraints` argument to
`optimize_acqf()`. The constraints will later be passed to the scipy
solver. You need to pass in `batch_initial_conditions` in this case.
Using non-linear inequality constraints also requires that `batch_limit`
is set to 1, which will be done automatically if not specified in
`options`.
post_processing_func: A function that post-processes an optimization
result appropriately (i.e., according to `round-trip`
transformations).
batch_initial_conditions: A tensor to specify the initial conditions. Set
this if you do not want to use default initialization strategy.
ic_generator: Function for generating initial conditions. Not needed when
`batch_initial_conditions` are provided. Defaults to
`gen_one_shot_kg_initial_conditions` for `qKnowledgeGradient` acquisition
functions and `gen_batch_initial_conditions` otherwise. Must be specified
for nonlinear inequality constraints.
ic_gen_kwargs: Additional keyword arguments passed to function specified by
`ic_generator`
kwargs: kwargs do nothing. This is provided so that the same arguments can
be passed to different acquisition functions without raising an error.
Returns:
A two-element tuple containing
- a `q x d`-dim tensor of generated candidates.
- an associated acquisition value.
"""
if not fixed_features_list:
raise ValueError("fixed_features_list must be non-empty.")
if isinstance(acq_function, OneShotAcquisitionFunction):
if not hasattr(acq_function, "evaluate") and q > 1:
raise ValueError(
"`OneShotAcquisitionFunction`s that do not implement `evaluate` "
"are currently not supported when `q > 1`. This is needed to "
"compute the joint acquisition value."
)
_raise_deprecation_warning_if_kwargs("optimize_acqf_mixed", kwargs)
ic_gen_kwargs = ic_gen_kwargs or {}
if q == 1:
ff_candidate_list, ff_acq_value_list = [], []
for fixed_features in fixed_features_list:
candidate, acq_value = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options or {},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
nonlinear_inequality_constraints=nonlinear_inequality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
ic_generator=ic_generator,
return_best_only=True,
**ic_gen_kwargs,
)
ff_candidate_list.append(candidate)
ff_acq_value_list.append(acq_value)
ff_acq_values = torch.stack(ff_acq_value_list)
best = torch.argmax(ff_acq_values)
return ff_candidate_list[best], ff_acq_values[best]
# For batch optimization with q > 1 we do not want to enumerate all n_combos^n
# possible combinations of discrete choices. Instead, we use sequential greedy
# optimization.
base_X_pending = acq_function.X_pending
candidates = torch.tensor([], device=bounds.device, dtype=bounds.dtype)
for _ in range(q):
candidate, acq_value = optimize_acqf_mixed(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
fixed_features_list=fixed_features_list,
options=options or {},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
nonlinear_inequality_constraints=nonlinear_inequality_constraints,
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
ic_generator=ic_generator,
ic_gen_kwargs=ic_gen_kwargs,
)
candidates = torch.cat([candidates, candidate], dim=-2)
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates], dim=-2)
if base_X_pending is not None
else candidates
)
acq_function.set_X_pending(base_X_pending)
# compute joint acquisition value
if isinstance(acq_function, OneShotAcquisitionFunction):
acq_value = acq_function.evaluate(X=candidates, bounds=bounds)
else:
acq_value = acq_function(candidates)
return candidates, acq_value
[docs]
def optimize_acqf_discrete(
acq_function: AcquisitionFunction,
q: int,
choices: Tensor,
max_batch_size: int = 2048,
unique: bool = True,
**kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""Optimize over a discrete set of points using batch evaluation.
For `q > 1` this function generates candidates by means of sequential
conditioning (rather than joint optimization), since for all but the
smalles number of choices the set `choices^q` of discrete points to
evaluate quickly explodes.
Args:
acq_function: An AcquisitionFunction.
q: The number of candidates.
choices: A `num_choices x d` tensor of possible choices.
max_batch_size: The maximum number of choices to evaluate in batch.
A large limit can cause excessive memory usage if the model has
a large training set.
unique: If True return unique choices, o/w choices may be repeated
(only relevant if `q > 1`).
kwargs: kwargs do nothing. This is provided so that the same arguments can
be passed to different acquisition functions without raising an error.
Returns:
A two-element tuple containing
- a `q x d`-dim tensor of generated candidates.
- an associated acquisition value.
"""
if isinstance(acq_function, OneShotAcquisitionFunction):
raise UnsupportedError(
"Discrete optimization is not supported for"
"one-shot acquisition functions."
)
if choices.numel() == 0:
raise InputDataError("`choices` must be non-emtpy.")
_raise_deprecation_warning_if_kwargs("optimize_acqf_discrete", kwargs)
choices_batched = choices.unsqueeze(-2)
if q > 1:
candidate_list, acq_value_list = [], []
base_X_pending = acq_function.X_pending
for _ in range(q):
with torch.no_grad():
acq_values = _split_batch_eval_acqf(
acq_function=acq_function,
X=choices_batched,
max_batch_size=max_batch_size,
)
best_idx = torch.argmax(acq_values)
candidate_list.append(choices_batched[best_idx])
acq_value_list.append(acq_values[best_idx])
# set pending points
candidates = torch.cat(candidate_list, dim=-2)
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates], dim=-2)
if base_X_pending is not None
else candidates
)
# need to remove choice from choice set if enforcing uniqueness
if unique:
choices_batched = torch.cat(
[choices_batched[:best_idx], choices_batched[best_idx + 1 :]]
)
# Reset acq_func to previous X_pending state
acq_function.set_X_pending(base_X_pending)
return candidates, torch.stack(acq_value_list)
with torch.no_grad():
acq_values = _split_batch_eval_acqf(
acq_function=acq_function, X=choices_batched, max_batch_size=max_batch_size
)
best_idx = torch.argmax(acq_values)
return choices_batched[best_idx], acq_values[best_idx]
def _split_batch_eval_acqf(
acq_function: AcquisitionFunction, X: Tensor, max_batch_size: int
) -> Tensor:
return torch.cat([acq_function(X_) for X_ in X.split(max_batch_size)])
def _generate_neighbors(
x: Tensor,
discrete_choices: List[Tensor],
X_avoid: Tensor,
inequality_constraints: List[Tuple[Tensor, Tensor, float]],
):
# generate all 1D perturbations
npts = sum([len(c) for c in discrete_choices])
X_loc = x.repeat(npts, 1)
j = 0
for i, c in enumerate(discrete_choices):
X_loc[j : j + len(c), i] = c
j += len(c)
# remove invalid and infeasible points (also remove x)
X_loc = _filter_invalid(X=X_loc, X_avoid=torch.cat((X_avoid, x)))
X_loc = _filter_infeasible(X=X_loc, inequality_constraints=inequality_constraints)
return X_loc
def _filter_infeasible(
X: Tensor, inequality_constraints: List[Tuple[Tensor, Tensor, float]]
):
"""Remove all points from `X` that don't satisfy the constraints."""
is_feasible = torch.ones(X.shape[0], dtype=torch.bool, device=X.device)
for inds, weights, bound in inequality_constraints:
is_feasible &= (X[..., inds] * weights).sum(dim=-1) >= bound
return X[is_feasible]
def _filter_invalid(X: Tensor, X_avoid: Tensor):
"""Remove all occurences of `X_avoid` from `X`."""
return X[~(X == X_avoid.unsqueeze(-2)).all(dim=-1).any(dim=-2)]
def _gen_batch_initial_conditions_local_search(
discrete_choices: List[Tensor],
raw_samples: int,
X_avoid: Tensor,
inequality_constraints: List[Tuple[Tensor, Tensor, float]],
min_points: int,
max_tries: int = 100,
):
"""Generate initial conditions for local search."""
device = discrete_choices[0].device
dtype = discrete_choices[0].dtype
dim = len(discrete_choices)
X = torch.zeros(0, dim, device=device, dtype=dtype)
for _ in range(max_tries):
X_new = torch.zeros(raw_samples, dim, device=device, dtype=dtype)
for i, c in enumerate(discrete_choices):
X_new[:, i] = c[
torch.randint(low=0, high=len(c), size=(raw_samples,), device=c.device)
]
X = torch.unique(torch.cat((X, X_new)), dim=0)
X = _filter_invalid(X=X, X_avoid=X_avoid)
X = _filter_infeasible(X=X, inequality_constraints=inequality_constraints)
if len(X) >= min_points:
return X
raise RuntimeError(f"Failed to generate at least {min_points} initial conditions")
[docs]
def optimize_acqf_discrete_local_search(
acq_function: AcquisitionFunction,
discrete_choices: List[Tensor],
q: int,
num_restarts: int = 20,
raw_samples: int = 4096,
inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None,
X_avoid: Optional[Tensor] = None,
batch_initial_conditions: Optional[Tensor] = None,
max_batch_size: int = 2048,
unique: bool = True,
**kwargs: Any,
) -> Tuple[Tensor, Tensor]:
r"""Optimize acquisition function over a lattice.
This is useful when d is large and enumeration of the search space
isn't possible. For q > 1 this function always performs sequential
greedy optimization (with proper conditioning on generated candidates).
NOTE: While this method supports arbitrary lattices, it has only been
thoroughly tested for {0, 1}^d. Consider it to be in alpha stage for
the more general case.
Args:
acq_function: An AcquisitionFunction
discrete_choices: A list of possible discrete choices for each dimension.
Each element in the list is expected to be a torch tensor.
q: The number of candidates.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization. This is required
if `batch_initial_conditions` is not specified.
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`
X_avoid: An `n x d` tensor of candidates that we aren't allowed to pick.
batch_initial_conditions: A tensor of size `n x 1 x d` to specify the
initial conditions. Set this if you do not want to use default
initialization strategy.
max_batch_size: The maximum number of choices to evaluate in batch.
A large limit can cause excessive memory usage if the model has
a large training set.
unique: If True return unique choices, o/w choices may be repeated
(only relevant if `q > 1`).
kwargs: kwargs do nothing. This is provided so that the same arguments can
be passed to different acquisition functions without raising an error.
Returns:
A two-element tuple containing
- a `q x d`-dim tensor of generated candidates.
- an associated acquisition value.
"""
_raise_deprecation_warning_if_kwargs("optimize_acqf_discrete_local_search", kwargs)
candidate_list = []
base_X_pending = acq_function.X_pending if q > 1 else None
base_X_avoid = X_avoid
device = discrete_choices[0].device
dtype = discrete_choices[0].dtype
dim = len(discrete_choices)
if X_avoid is None:
X_avoid = torch.zeros(0, dim, device=device, dtype=dtype)
inequality_constraints = inequality_constraints or []
for i in range(q):
# generate some starting points
if i == 0 and batch_initial_conditions is not None:
X0 = _filter_invalid(X=batch_initial_conditions.squeeze(1), X_avoid=X_avoid)
X0 = _filter_infeasible(
X=X0, inequality_constraints=inequality_constraints
).unsqueeze(1)
else:
X_init = _gen_batch_initial_conditions_local_search(
discrete_choices=discrete_choices,
raw_samples=raw_samples,
X_avoid=X_avoid,
inequality_constraints=inequality_constraints,
min_points=num_restarts,
)
# pick the best starting points
with torch.no_grad():
acqvals_init = _split_batch_eval_acqf(
acq_function=acq_function,
X=X_init.unsqueeze(1),
max_batch_size=max_batch_size,
).unsqueeze(-1)
X0 = X_init[acqvals_init.topk(k=num_restarts, largest=True, dim=0).indices]
# optimize from the best starting points
best_xs = torch.zeros(len(X0), dim, device=device, dtype=dtype)
best_acqvals = torch.zeros(len(X0), 1, device=device, dtype=dtype)
for j, x in enumerate(X0):
curr_x, curr_acqval = x.clone(), acq_function(x.unsqueeze(1))
while True:
# this generates all feasible neighbors that are one bit away
X_loc = _generate_neighbors(
x=curr_x,
discrete_choices=discrete_choices,
X_avoid=X_avoid,
inequality_constraints=inequality_constraints,
)
# there may not be any neighbors
if len(X_loc) == 0:
break
with torch.no_grad():
acqval_loc = acq_function(X_loc.unsqueeze(1))
# break if no neighbor is better than the current point (local optimum)
if acqval_loc.max() <= curr_acqval:
break
best_ind = acqval_loc.argmax().item()
curr_x, curr_acqval = X_loc[best_ind].unsqueeze(0), acqval_loc[best_ind]
best_xs[j, :], best_acqvals[j] = curr_x, curr_acqval
# pick the best
best_idx = best_acqvals.argmax()
candidate_list.append(best_xs[best_idx].unsqueeze(0))
# set pending points
candidates = torch.cat(candidate_list, dim=-2)
if q > 1:
acq_function.set_X_pending(
torch.cat([base_X_pending, candidates], dim=-2)
if base_X_pending is not None
else candidates
)
# Update points to avoid if unique is True
if unique:
X_avoid = (
torch.cat([base_X_avoid, candidates], dim=-2)
if base_X_avoid is not None
else candidates
)
# Reset acq_func to original X_pending state
if q > 1:
acq_function.set_X_pending(base_X_pending)
with torch.no_grad():
acq_value = acq_function(candidates) # compute joint acquisition value
return candidates, acq_value
