SNOBFIT

This algorithm combines global and local search by branching and local fits, and can be used to solve the noisy optimization of an expensive objective function.

This planner uses the SQSnobFit library, which needs to be installed if you want to use this algorithm. For more information please visit the SNOBFIT website.

class olympus.planners.Snobfit(*args, **kwargs)

Parameters

• init_guess (array, optional) – initial guess for the optimization

• init_guess_method (str) – method to construct initial guesses if init_guess is not provided. Choose from: random

• init_guess_seed (str) – random seed for init_guess_method

Methods

tell([observations])	Provide the planner with all previous observations.
ask([return_as])	suggest new set of parameters
recommend([observations, return_as])	Consecutively executes tell and ask: tell the planner about all previous observations, and ask about the next query point.
optimize(emulator[, num_iter, verbose])	Optimizes a surface for a fixed number of iterations.
set_param_space(param_space)	Defines the parameter space over which the planner will search.