"""Abstract base class for BQ acquisition functions."""
from __future__ import annotations
import abc
from typing import Optional, Tuple
import numpy as np
from probnum.quad.solvers._bq_state import BQState
[docs]class AcquisitionFunction(abc.ABC):
"""An abstract class for an acquisition function for Bayesian quadrature."""
@property
@abc.abstractmethod
def has_gradients(self) -> bool:
"""Whether the acquisition function exposes gradients."""
raise NotImplementedError
[docs] @abc.abstractmethod
def __call__(
self, x: np.ndarray, bq_state: BQState
) -> Tuple[np.ndarray, Optional[np.ndarray]]:
"""Evaluates the acquisition function and optionally its gradients.
Parameters
----------
x
*shape=(batch_size, input_dim)* -- The nodes where the acquisition function
is being evaluated.
bq_state
State of the BQ belief.
Returns
-------
acquisition_values :
*shape=(batch_size, )* -- The acquisition values at nodes ``x``.
acquisition_gradients :
*shape=(batch_size, input_dim)* -- The corresponding gradients (optional).
"""
raise NotImplementedError