syne_tune.optimizer.schedulers.searchers.bayesopt.models.gp_mcmc_model module

class syne_tune.optimizer.schedulers.searchers.bayesopt.models.gp_mcmc_model.GaussProcMCMCEstimator(gpmodel, active_metric='target', normalize_targets=True, debug_log=None, filter_observed_data=None, hp_ranges_for_prediction=None)[source]

Bases: GaussProcEstimator

We support pending evaluations via fantasizing. Note that state does not contain the fantasy values, but just the pending configs. Fantasy values are sampled here.

We draw one fantasy sample per MCMC sample here. This could be extended by sampling > 1 fantasy samples for each MCMC sample.

Parameters:

  • gpmodel (GPRegressionMCMC) – GPRegressionMCMC model

  • active_metric (str) – Name of the metric to optimize.

  • normalize_targets (bool) – Normalize target values in state.trials_evaluations?

get_params()[source]
Returns:

Current tunable model parameters

set_params(param_dict)[source]
Parameters:

param_dict – New model parameters