syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.independent.likelihood module

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.independent.likelihood.IndependentGPPerResourceMarginalLikelihood(kernel, mean, resource_attr_range, target_transform=None, separate_noise_variances=False, initial_noise_variance=None, initial_covariance_scale=None, encoding_type=None, **kwargs)

Bases: MarginalLikelihood

Marginal likelihood for GP multi-fidelity model over \(f(x, r)\), where for each \(r\), \(f(x, r)\) is represented by an independent GP. The different processes share the same kernel, but have their own mean functions \(\mu_r\) and covariance scales \(c_r\). If separate_noise_variances == True, each process has its own noise variance, otherwise all processes share the same noise variance.

Parameters:

• kernel (KernelFunction) – Shared kernel function \(k(x, x')\)

• mean (Dict[int, MeanFunction]) – Maps rung level \(r\) to mean function \(\mu_r\)

• resource_attr_range (Tuple[int, int]) – \((r_{min}, r_{max})\)

• target_transform (Optional[ScalarTargetTransform]) – Invertible transform of target values y to latent values z, which are then modelled as Gaussian. Shared across different \(r\). Defaults to the identity

• separate_noise_variances (bool) – See above. Defaults to False

• initial_noise_variance (Optional[float]) – Initial value for noise variance(s). Defaults to INITIAL_NOISE_VARIANCE

• initial_covariance_scale (Optional[float]) – Initial value for covariance scales. Defaults to INITIAL_COVARIANCE_SCALE

• encoding_type (Optional[str]) – Encoding used for noise variance(s) and covariance scales. Defaults to DEFAULT_ENCODING

get_posterior_state(data)

Return type:

PosteriorState

forward(data)

Overrides to implement forward computation using NDArray. Only accepts positional arguments. Parameters ———- *args : list of NDArray

Input tensors.

param_encoding_pairs()

Return a list of tuples with the Gluon parameters of the likelihood and their respective encodings

Return type:

List[tuple]

get_noise_variance(as_ndarray=False)

get_covariance_scale(resource, as_ndarray=False)

set_covariance_scale(resource, val)

get_params()

Return type:

Dict[str, ndarray]

set_params(param_dict)

on_fit_start(data)

Called at the beginning of fit.

Parameters:

data (Dict[str, Any]) – Argument passed to fit