#!/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.
r"""
Methods for optimizing acquisition functions.
"""
from __future__ import annotations
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from botorch.acquisition.acquisition import (
AcquisitionFunction,
OneShotAcquisitionFunction,
)
from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
from botorch.generation.gen import gen_candidates_scipy
from botorch.logging import logger
from botorch.optim.initializers import (
gen_batch_initial_conditions,
gen_one_shot_kg_initial_conditions,
)
from botorch.optim.stopping import ExpMAStoppingCriterion
from torch import Tensor
[docs]def optimize_acqf(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
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,
return_best_only: bool = True,
sequential: bool = False,
) -> 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`.
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.
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. 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.
sequential: If False, uses joint optimization, otherwise uses sequential
optimization.
Returns:
A two-element tuple containing
- a `(num_restarts) x q x d`-dim tensor of generated candidates.
- 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 canidates `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
>>> )
"""
if sequential and q > 1:
if not return_best_only:
raise NotImplementedError(
"return_best_only=False only supported for joint optimization"
)
if isinstance(acq_function, OneShotAcquisitionFunction):
raise NotImplementedError(
"sequential optimization currently not supported for one-shot "
"acquisition functions. Must have `sequential=False`."
)
candidate_list, acq_value_list = [], []
candidates = torch.tensor([], device=bounds.device, dtype=bounds.dtype)
base_X_pending = acq_function.X_pending
for i in range(q):
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,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=None,
return_best_only=True,
sequential=False,
)
candidate_list.append(candidate)
acq_value_list.append(acq_value)
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
)
logger.info(f"Generated sequential candidate {i+1} of {q}")
# Reset acq_func to previous X_pending state
acq_function.set_X_pending(base_X_pending)
return candidates, torch.stack(acq_value_list)
options = options or {}
if batch_initial_conditions is None:
ic_gen = (
gen_one_shot_kg_initial_conditions
if isinstance(acq_function, qKnowledgeGradient)
else gen_batch_initial_conditions
)
# TODO: Generating initial candidates should use parameter constraints.
batch_initial_conditions = ic_gen(
acq_function=acq_function,
bounds=bounds,
q=q,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
)
batch_limit: int = options.get("batch_limit", num_restarts)
batch_candidates_list: List[Tensor] = []
batch_acq_values_list: List[Tensor] = []
start_idcs = list(range(0, num_restarts, batch_limit))
for start_idx in start_idcs:
end_idx = min(start_idx + batch_limit, num_restarts)
# optimize using random restart optimization
batch_candidates_curr, batch_acq_values_curr = gen_candidates_scipy(
initial_conditions=batch_initial_conditions[start_idx:end_idx],
acquisition_function=acq_function,
lower_bounds=bounds[0],
upper_bounds=bounds[1],
options={
k: v
for k, v in options.items()
if k not in ("batch_limit", "nonnegative")
},
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features=fixed_features,
)
batch_candidates_list.append(batch_candidates_curr)
batch_acq_values_list.append(batch_acq_values_curr)
logger.info(f"Generated candidate batch {start_idx+1} of {len(start_idcs)}.")
batch_candidates = torch.cat(batch_candidates_list)
batch_acq_values = torch.cat(batch_acq_values_list)
if post_processing_func is not None:
batch_candidates = post_processing_func(batch_candidates)
if 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 isinstance(acq_function, OneShotAcquisitionFunction):
batch_candidates = acq_function.extract_candidates(X_full=batch_candidates)
return batch_candidates, batch_acq_values
[docs]def optimize_acqf_cyclic(
acq_function: AcquisitionFunction,
bounds: Tensor,
q: int,
num_restarts: int,
raw_samples: int,
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,
) -> 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`.
q: The number of candidates.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization
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.
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}
>>> )
"""
# for the first cycle, optimize the q candidates sequentially
candidates, acq_vals = optimize_acqf(
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,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=batch_initial_conditions,
return_best_only=True,
sequential=True,
)
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=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]
)
candidate_i, acq_val_i = optimize_acqf(
acq_function=acq_function,
bounds=bounds,
q=1,
num_restarts=num_restarts,
raw_samples=raw_samples,
options=options,
inequality_constraints=inequality_constraints,
equality_constraints=equality_constraints,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
batch_initial_conditions=candidates[i].unsqueeze(0),
return_best_only=True,
sequential=True,
)
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: int,
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,
) -> 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`.
num_restarts: Number of starting points for multistart acquisition
function optimization.
raw_samples: Number of samples for initialization
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).
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 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
)
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,
fixed_features=fixed_features,
post_processing_func=post_processing_func,
return_best_only=True,
sequential=False,
)
candidate_list.append(candidate)
acq_value_list.append(acq_value)
candidates = torch.cat(candidate_list, dim=-2)
return candidates, torch.stack(acq_value_list)
