trieste.acquisition.function.utils#

This module contains utility functions for acquisition functions.

Module Contents#

class MultivariateNormalCDF(sample_size: int, dim: int, dtype: tensorflow.DType, num_sobol_skip: int = 0)[source]#

Builds the cumulative density function of the multivariate Gaussian using the Genz approximation detailed in [GT16].

This is a Monte Carlo approximation which is more accurate than a naive Monte Carlo estimate of the expected improvent. In order to use reparametrised samples, the helper accepts a tensor of samples, and the callable uses these fixed samples whenever it is called.

Parameters:

  • samples_size – int, number of samples to use.

  • dim – int, dimension of the multivariate Gaussian.

  • dtype – tf.DType, data type to use for calculations.

  • num_sobol_skip – int, number of sobol samples to skip.

_standard_normal_cdf_and_inverse_cdf(dtype: tensorflow.DType) → Tuple[Callable[[trieste.types.TensorType], trieste.types.TensorType], Callable[[trieste.types.TensorType], trieste.types.TensorType]][source]#

Returns two callables Phi and iPhi, which compute the cumulative density function and inverse cumulative density function of a standard univariate Gaussian.

Parameters:

dtype – The data type to use, either tf.float32 or tf.float64.

Returns Phi, iPhi:

Cumulative and inverse cumulative density functions.

_get_update_indices(B: int, S: int, Q: int, q: int) → trieste.types.TensorType[source]#

Returns indices for updating a tensor using tf.tensor_scatter_nd_add, for use within the _mvn_cdf function, for computing the cumulative density function of a multivariate Gaussian. The indices idx returned are such that the following operation

idx = get_update_indices(B, S, Q, q) tensor = tf.tensor_scatter_nd_add(tensor, idx, update)

is equivalent to the numpy operation

tensor = tensor[:, :, q] + update

where tensor is a tensor of shape (B, S, Q).

Parameters:

  • B – First dim. of tensor for which the indices are generated.

  • S – Second dim. of tensor for which the indices are generated.

  • Q – Third dim. of tensor for which the indices are generated.

  • q – Index of tensor along fourth dim. to which the update is applied.

__call__(x: trieste.types.TensorType, mean: trieste.types.TensorType, cov: trieste.types.TensorType, jitter: float = 1e-06) → trieste.types.TensorType[source]#

Computes the cumulative density function of the multivariate Gaussian using the Genz approximation.

Parameters:

  • x – Tensor of shape (B, Q), batch of points to evaluate CDF at.

  • mean – Tensor of shape (B, Q), batch of means.

  • covariance – Tensor of shape (B, Q, Q), batch of covariances.

  • jitter – float, jitter to use in the Cholesky factorisation.

Returns mvn_cdf:

Tensor of shape (B,), CDF values.