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

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

Bases: IndependentGPPerResourceMarginalLikelihood

Variant of IndependentGPPerResourceMarginalLikelihood, which has the same internal model and marginal likelihood function, but whose posterior state is of HyperTuneIndependentGPPosteriorState, which uses an ensemble predictive distribution, whose weighting distribution has to be passed here at construction.

property ensemble_distribution: Dict[int, float]

set_ensemble_distribution(distribution)

get_posterior_state(data)

Return type:

PosteriorState

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.hypertune.likelihood.HyperTuneJointGPMarginalLikelihood(kernel, mean, resource_attr_range, ensemble_distribution, target_transform=None, initial_noise_variance=None, encoding_type=None, **kwargs)

Bases: GaussianProcessMarginalLikelihood

Variant of GaussianProcessMarginalLikelihood, which has the same internal model and marginal likelihood function, but whose posterior state is of HyperTuneJointGPPosteriorState, which uses an ensemble predictive distribution, whose weighting distribution has to be passed here at construction.

property ensemble_distribution: Dict[int, float]

set_ensemble_distribution(distribution)

get_posterior_state(data)

Return type:

PosteriorState