trieste.models.gpflux.builders#

This file contains builders for GPflux models supported in Trieste. We found the default configurations used here to work well in most situation, but they should not be taken as universally good solutions.

Module Contents#

NUM_LAYERS: int = 2[source]#

Default number of layers in the deep gaussian process model.

MAX_NUM_INDUCING_POINTS: int = 500[source]#

Default maximum number of inducing points.

NUM_INDUCING_POINTS_PER_DIM: int = 50[source]#

Default number of inducing points per dimension of the search space.

INNER_LAYER_SQRT_FACTOR: float = 1e-05[source]#

Default value for a multiplicative factor used to rescale hidden layers.

LIKELIHOOD_VARIANCE: float = 0.001[source]#

Default value for an initial noise variance in the likelihood function.

build_vanilla_deep_gp(data: trieste.data.Dataset, search_space: trieste.space.SearchSpace, num_layers: int = NUM_LAYERS, num_inducing_points: int | None = None, inner_layer_sqrt_factor: float = INNER_LAYER_SQRT_FACTOR, likelihood_variance: float = LIKELIHOOD_VARIANCE, trainable_likelihood: bool = True) → gpflux.models.DeepGP[source]#

Build a DeepGP model with sensible initial parameters. We found the default configuration used here to work well in most situation, but it should not be taken as a universally good solution.

Note that although we set all the relevant parameters to sensible values, we rely on build_constant_input_dim_deep_gp from architectures to build the model.

Parameters:

  • data – Dataset from the initial design, used to estimate the variance of observations and to provide query points which are used to determine inducing point locations with k-means.

  • search_space – Search space for performing Bayesian optimization. Used for initialization of inducing locations if num_inducing_points is larger than the amount of data.

  • num_layers – Number of layers in deep GP. By default set to NUM_LAYERS.

  • num_inducing_points – Number of inducing points to use in each layer. If left unspecified (default), this number is set to either NUM_INDUCING_POINTS_PER_DIM``*dimensionality of the search space or value given by ``MAX_NUM_INDUCING_POINTS, whichever is smaller.

  • inner_layer_sqrt_factor – A multiplicative factor used to rescale hidden layers, see Config for details. By default set to INNER_LAYER_SQRT_FACTOR.

  • likelihood_variance – Initial noise variance in the likelihood function, see Config for details. By default set to LIKELIHOOD_VARIANCE.

  • trainable_likelihood – Trainable likelihood variance.

Returns:

A DeepGP model with sensible default settings.

Raise:

If non-positive num_layers, inner_layer_sqrt_factor, likelihood_variance or num_inducing_points is provided.