Source code for botorch.optim.optimize

#!/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 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.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: 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, return_best_only: bool = True, sequential: bool = False, **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`. 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` 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. kwargs: Additonal keyword arguments. 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 = [], [] 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 {} # Handle the trivial case when all features are fixed if fixed_features is not None and len(fixed_features) == bounds.shape[-1]: 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 if batch_initial_conditions is None: if raw_samples is None: raise ValueError( "Must specify `raw_samples` when `batch_initial_conditions` is `None`." ) ic_gen = ( gen_one_shot_kg_initial_conditions if isinstance(acq_function, qKnowledgeGradient) else gen_batch_initial_conditions ) batch_initial_conditions = ic_gen( acq_function=acq_function, bounds=bounds, q=q, num_restarts=num_restarts, raw_samples=raw_samples, fixed_features=fixed_features, options=options, inequality_constraints=inequality_constraints, equality_constraints=equality_constraints, ) batch_limit: int = options.get("batch_limit", num_restarts) batch_candidates_list: List[Tensor] = [] batch_acq_values_list: List[Tensor] = [] batched_ics = batch_initial_conditions.split(batch_limit) for i, batched_ics_ in enumerate(batched_ics): # optimize using random restart optimization batch_candidates_curr, batch_acq_values_curr = gen_candidates_scipy( initial_conditions=batched_ics_, 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 ("init_batch_limit", "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 {i+1} of {len(batched_ics)}.") 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): if not kwargs.get("return_full_tree", False): 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: 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, ) -> 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. 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. 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: 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, ) -> 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. 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). 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)
[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, post_processing_func: Optional[Callable[[Tensor], Tensor]] = None, batch_initial_conditions: Optional[Tensor] = None, ) -> 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`. 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` 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. 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 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, fixed_features=fixed_features, post_processing_func=post_processing_func, batch_initial_conditions=batch_initial_conditions, return_best_only=True, ) 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, post_processing_func=post_processing_func, batch_initial_conditions=batch_initial_conditions, ) 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) acq_value = acq_function(candidates) # compute joint acquisition value return candidates, acq_value
[docs]def optimize_acqf_discrete( acq_function: AcquisitionFunction, q: int, choices: Tensor, max_batch_size: int = 2048, unique: bool = True, ) -> 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`). Returns: A three-element tuple containing - a `q x d`-dim tensor of generated candidates. - an associated acquisition value. """ choices_batched = choices.unsqueeze(-2) if q > 1: candidate_list, acq_value_list = [], [] base_X_pending = acq_function.X_pending for _ in range(q): 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) 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)])