Source code for probnum.quad.solvers.acquisition_functions._predictive_variance

"""Uncertainty sampling for Bayesian quadrature."""

from __future__ import annotations

from typing import Optional, Tuple

import numpy as np

from probnum.quad.solvers._bq_state import BQState
from probnum.quad.solvers.belief_updates import BQStandardBeliefUpdate

from ._acquisition_function import AcquisitionFunction

# pylint: disable=too-few-public-methods, fixme

class WeightedPredictiveVariance(AcquisitionFunction):
    r"""The predictive variance acquisition function that yields uncertainty sampling.

    The acquisition function is

    .. math::
        a(x) = \operatorname{Var}(f(x)) p(x)^2

    where :math:`\operatorname{Var}(f(x))` is the predictive variance of the model and
    :math:`p(x)` is the density of the integration measure :math:`\mu`.

        The implementation scales :math:`a(x)` with the inverse of the squared kernel
        scale for numerical stability.

    def has_gradients(self) -> bool:
        # Todo (#581): this needs to return True, once gradients are available
        return False

[docs] def __call__( self, x: np.ndarray, bq_state: BQState, ) -> Tuple[np.ndarray, Optional[np.ndarray]]: _, predictive_variance = BQStandardBeliefUpdate.predict_integrand(x, bq_state) predictive_variance *= 1 / bq_state.scale_sq # for numerical stability values = predictive_variance * bq_state.measure(x) ** 2 return values, None