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

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.constants.OptimizationConfig(lbfgs_tol, lbfgs_maxiter, verbose, n_starts)

Bases: object

lbfgs_tol: float

lbfgs_maxiter: int

verbose: bool

n_starts: int

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.constants.MCMCConfig(n_samples, n_burnin, n_thinning)

Bases: object

n_samples is the total number of samples drawn. The first n_burnin of these are dropped (burn-in), and every n_thinning of the rest is returned. This means we return (n_samples - n_burnin) // n_thinning samples.

n_samples: int

n_burnin: int

n_thinning: int