syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gp_regression module

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gp_regression.GaussianProcessRegression(kernel, mean=None, target_transform=None, initial_noise_variance=None, optimization_config=None, random_seed=None, fit_reset_params=True)[source]

Bases: GaussianProcessOptimizeModel

Gaussian Process Regression

Takes as input a mean function (which depends on X only) and a kernel function.

Parameters:

  • kernel (KernelFunction) – Kernel function

  • mean (Optional[MeanFunction]) – Mean function which depends on the input X only (by default, a scalar fitted while optimizing the likelihood)

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

  • initial_noise_variance (Optional[float]) – Initial value for noise variance parameter

  • optimization_config (Optional[OptimizationConfig]) – Configuration that specifies the behavior of the optimization of the marginal likelihood.

  • random_seed – Random seed to be used (optional)

  • fit_reset_params (bool) – Reset parameters to initial values before running ‘fit’? If False, ‘fit’ starts from the current values

property likelihood: MarginalLikelihood