Source code for botorch.gen

#!/usr/bin/env python3

r"""
Candidate generation utilities.
"""

from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union

import torch
from scipy.optimize import minimize
from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer

from .optim.parameter_constraints import (
    _arrayify,
    make_scipy_bounds,
    make_scipy_linear_constraints,
)
from .optim.utils import check_convergence, columnwise_clamp, fix_features


[docs]def gen_candidates_scipy( initial_conditions: Tensor, acquisition_function: Module, lower_bounds: Optional[Union[float, Tensor]] = None, upper_bounds: Optional[Union[float, Tensor]] = None, inequality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None, equality_constraints: Optional[List[Tuple[Tensor, Tensor, float]]] = None, options: Optional[Dict[str, Any]] = None, fixed_features: Optional[Dict[int, Optional[float]]] = None, ) -> Tuple[Tensor, Tensor]: r"""Generate a set of candidates using `scipy.optimize.minimize`. Optimizes an acquisition function starting from a set of initial candidates using `scipy.optimize.minimize` via a numpy converter. Args: initial_conditions: Starting points for optimization. acquisition_function: Acquisition function to be used. lower_bounds: Minimum values for each column of initial_conditions. upper_bounds: Maximum values for each column of initial_conditions. 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` options: options used to control the optimization including "method" and "maxiter" fixed_features: This is a dictionary of feature indices to values, where all generated candidates will have features fixed to these values. If the dictionary value is None, then that feature will just be fixed to the clamped value and not optimized. Assumes values to be compatible with lower_bounds and upper_bounds! Returns: 2-element tuple containing - The set of generated candidates. - The acquisition value for each t-batch. Example: >>> qEI = qExpectedImprovement(model, best_f=0.2) >>> bounds = torch.tensor([[0., 0.], [1., 2.]]) >>> Xinit = gen_batch_initial_conditions( >>> qEI, bounds, q=3, num_restarts=25, raw_samples=500 >>> ) >>> batch_candidates, batch_acq_values = gen_candidates_scipy( initial_conditions=Xinit, acquisition_function=qEI, lower_bounds=bounds[0], upper_bounds=bounds[1], ) """ options = options or {} clamped_candidates = columnwise_clamp( initial_conditions, lower_bounds, upper_bounds ).requires_grad_(True) shapeX = clamped_candidates.shape x0 = _arrayify(clamped_candidates.view(-1)) bounds = make_scipy_bounds( X=initial_conditions, lower_bounds=lower_bounds, upper_bounds=upper_bounds ) constraints = make_scipy_linear_constraints( shapeX=clamped_candidates.shape, inequality_constraints=inequality_constraints, equality_constraints=equality_constraints, ) def f(x): X = ( torch.from_numpy(x) .to(initial_conditions) .view(shapeX) .contiguous() .requires_grad_(True) ) X_fix = fix_features(X=X, fixed_features=fixed_features) loss = -acquisition_function(X_fix).sum() loss.backward() fval = loss.item() gradf = _arrayify(X.grad.view(-1)) return fval, gradf res = minimize( f, x0, method=options.get("method", "SLSQP"), jac=True, bounds=bounds, constraints=constraints, options={k: v for k, v in options.items() if k != "method"}, ) candidates = fix_features( X=torch.from_numpy(res.x) # pyre-ignore [16] .to(initial_conditions) .view(shapeX) .contiguous(), fixed_features=fixed_features, ) batch_acquisition = acquisition_function(candidates) return candidates, batch_acquisition
[docs]def gen_candidates_torch( initial_conditions: Tensor, acquisition_function: Callable, lower_bounds: Optional[Union[float, Tensor]] = None, upper_bounds: Optional[Union[float, Tensor]] = None, optimizer: Type[Optimizer] = torch.optim.Adam, options: Optional[Dict[str, Union[float, str]]] = None, verbose: bool = True, fixed_features: Optional[Dict[int, Optional[float]]] = None, ) -> Tuple[Tensor, Tensor]: r"""Generate a set of candidates using a `torch.optim` optimizer. Optimizes an acquisition function starting from a set of initial candidates using an optimizer from `torch.optim`. Args: initial_conditions: Starting points for optimization. acquisition_function: Acquisition function to be used. lower_bounds: Minimum values for each column of initial_conditions. upper_bounds: Maximum values for each column of initial_conditions. optimizer (Optimizer): The pytorch optimizer to use to perform candidate search. options: Options used to control the optimization. Includes maxiter: Maximum number of iterations verbose: If True, provide verbose output. fixed_features: This is a dictionary of feature indices to values, where all generated candidates will have features fixed to these values. If the dictionary value is None, then that feature will just be fixed to the clamped value and not optimized. Assumes values to be compatible with lower_bounds and upper_bounds! Returns: 2-element tuple containing - The set of generated candidates. - The acquisition value for each t-batch. Example: >>> qEI = qExpectedImprovement(model, best_f=0.2) >>> bounds = torch.tensor([[0., 0.], [1., 2.]]) >>> Xinit = gen_batch_initial_conditions( >>> qEI, bounds, q=3, num_restarts=25, raw_samples=500 >>> ) >>> batch_candidates, batch_acq_values = gen_candidates_torch( initial_conditions=Xinit, acquisition_function=qEI, lower_bounds=bounds[0], upper_bounds=bounds[1], ) """ options = options or {} clamped_candidates = columnwise_clamp( initial_conditions, lower_bounds, upper_bounds ).requires_grad_(True) candidates = fix_features(clamped_candidates, fixed_features) bayes_optimizer = optimizer( params=[clamped_candidates], lr=options.get("lr", 0.025) ) param_trajectory: Dict[str, List[Tensor]] = {"candidates": []} loss_trajectory: List[float] = [] i = 0 converged = False while not converged: i += 1 loss = -acquisition_function(candidates).sum() if verbose: print("Iter: {} - Value: {:.3f}".format(i, -loss.item())) loss_trajectory.append(loss.item()) param_trajectory["candidates"].append(candidates.clone()) def closure(): bayes_optimizer.zero_grad() loss = -acquisition_function(candidates).sum() loss.backward() return loss bayes_optimizer.step(closure) # pyre-ignore clamped_candidates.data = columnwise_clamp( clamped_candidates, lower_bounds, upper_bounds ) candidates = fix_features(clamped_candidates, fixed_features) converged = check_convergence( loss_trajectory=loss_trajectory, param_trajectory=param_trajectory, options=options, ) batch_acquisition = acquisition_function(candidates) return candidates, batch_acquisition
[docs]def get_best_candidates(batch_candidates: Tensor, batch_values: Tensor) -> Tensor: r"""Extract best (q-batch) candidate from batch of candidates Args: batch_candidates: A `b x q x d` tensor of `b` q-batch candidates, or a `b x d` tensor of `b` single-point candidates. batch_values: A tensor with `b` elements containing the value of the respective candidate (higher is better). Returns: A tensor of size `q x d` (if q-batch mode) or `d` from batch_candidates with the highest associated value. Example: >>> qEI = qExpectedImprovement(model, best_f=0.2) >>> bounds = torch.tensor([[0., 0.], [1., 2.]]) >>> Xinit = gen_batch_initial_conditions( >>> qEI, bounds, q=3, num_restarts=25, raw_samples=500 >>> ) >>> batch_candidates, batch_acq_values = gen_candidates_scipy( initial_conditions=Xinit, acquisition_function=qEI, lower_bounds=bounds[0], upper_bounds=bounds[1], ) >>> best_candidates = get_best_candidates(batch_candidates, batch_acq_values) """ best = torch.max(batch_values.view(-1), dim=0)[1].item() return batch_candidates[best]