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(goal='minimize', init_guess=None, init_guess_method='random', init_guess_seed=None)[source] - 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.
-
ask(return_as=None) suggest new set of parameters
- Parameters
return_as (string) – choose data type for returned parameters allowed options (dict, array)
- Returns
newly generated parameters
- Return type
ParameterVector
-
optimize(emulator, num_iter=1, verbose=False) Optimizes a surface for a fixed number of iterations.
- Parameters
emulator (object) – Emulator or a Surface instance to optimize over.
num_iter (int) – Maximum number of iterations allowed.
verbose (bool) – Whether to print information to screen.
- Returns
- Campaign object with information about the optimization, including all parameters
tested and measurements obtained.
- Return type
campaign (Campaign)
-
recommend(observations=None, return_as=None) Consecutively executes tell and ask: tell the planner about all previous observations, and ask about the next query point.
- Parameters
observations (list of ???) –
return_as (string) – choose data type for returned parameters allowed options (dict, array)
- Returns
newly generated parameters
- Return type
list
-
set_param_space(param_space) Defines the parameter space over which the planner will search.
- Parameters
param_space (ParameterSpace) – a ParameterSpace object defining the space over which to search.
-
tell(observations=<olympus.campaigns.observations.Observations object>) Provide the planner with all previous observations.
- Parameters
observations (Observations) – an Observation object containing all previous observations. This defines the history of the campaign seen by the planner. The default is None, i.e. there are no previous observations.