emukit.quadrature.loop package

Module contents

Loops for Bayesian quadrature.

class emukit.quadrature.loop.BayesianMonteCarlo(model, model_updater=None)

Bases: OuterLoop

The loop for Bayesian Monte Carlo (BMC).

Nodes are samples from the integration measure. Implemented as described in Section 2.1 of the paper.

References

C.E. Rasmussen and Z. Ghahramani, Bayesian Monte Carlo, Advances in Neural Information Processing Systems 15 (NeurIPS) 2003

Note

The BMC point calculator does not depend on past observations. Thus, running this BQ loop should be equivalent to sampling all points with MC from the measure, evaluating them as batch and then fitting a model to them. The purpose of this loop is convenience, as it can be used with the same interface as the active and adaptive learning schemes where point acquisition depends explicitly or implicitly (through hyperparameters) on the previous evaluations.

Hint: The default model_updater FixedIntervalUpdater updates and optimizes the model after each new sample. Since the sampling scheme of BMC does not depend on the model, alternatively, the dummy updater NoopModelUpdater may be used which does not update the model. This may save compute time. However, the model then needs to be updated manually after the loop ran: i) the collected nodes are stored in model.loop_state. ii) call model.set_data(loop_state.X, loop_state.Y) iii) call model.optimize().

Parameters
class emukit.quadrature.loop.VanillaBayesianQuadratureLoop(model, acquisition=None, model_updater=None, acquisition_optimizer=None)

Bases: OuterLoop

The loop for standard (‘vanilla’) Bayesian Quadrature.

Parameters
  • model (VanillaBayesianQuadrature) – The vanilla Bayesian quadrature model.

  • acquisition (Optional[Acquisition]) – The acquisition function that is used to collect new points, defaults to integral-variance-reduction.

  • model_updater (Optional[ModelUpdater]) – Defines how and when the BQ model is updated if new data arrives. Defaults to updating hyper-parameters after every iteration.

  • acquisition_optimizer (Optional[AcquisitionOptimizerBase]) – Optimizer selecting next evaluation points by maximizing acquisition. Gradient based optimizer is used if None. Defaults to None.

class emukit.quadrature.loop.WSABILLoop(model, model_updater=None, acquisition_optimizer=None)

Bases: OuterLoop

The loop for WSABI-L.

References

Gunter et al. 2014 Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature, Advances in Neural Information Processing Systems (NeurIPS), 27, pp. 2789–2797.

Parameters
  • model (WSABIL) – The WSABI-L model.

  • model_updater (Optional[ModelUpdater]) – Defines how and when the quadrature model is updated if new data arrives. Defaults to updating hyper-parameters every iteration.

  • acquisition_optimizer (Optional[AcquisitionOptimizerBase]) – Optimizer selecting next evaluation points by maximizing acquisition. Gradient based optimizer is used if None. Defaults to None.

Submodules

Point calculators for Bayesian quadrature.

class emukit.quadrature.loop.point_calculators.BayesianMonteCarloPointCalculator(model, parameter_space)

Bases: CandidatePointCalculator

This point calculator produces Monte Carlo points from the integration measure.

It can be used for Bayesian Monte Carlo (BMC) [1] as described in Section 2.1 of the paper.

References

[1] C.E. Rasmussen and Z. Ghahramani, Bayesian Monte Carlo, Advances in Neural Information Processing Systems 15 (NeurIPS) 2003

Parameters
Raises

ValueError – If integration measure provided through the model does have sampling capabilities.

compute_next_points(loop_state, context=None)

Computes the next point.

Parameters
  • loop_state (LoopState) – Object that contains current state of the loop.

  • context (Optional[dict]) – Contains variables to fix and the values to fix them to. The dictionary key is the parameter name and the value is the value to fix the parameter to.

Return type

ndarray

Returns

The next point to evaluate the function at, shape (1, input_dim).