Phoenics¶
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class
olympus.planners.Phoenics(goal='minimize', batches=1, boosted=True, parallel=True, sampling_strategies=2)[source] A Bayesian optimization algorithm based on Bayesian Kernel Density estimation.
- Parameters
goal (str) – The optimization goal, either ‘minimize’ or ‘maximize’. Default is ‘minimize’.
batches (int) – number of parameter batches to return at each ‘ask’ iteration.
boosted (bool) – whether to use a lower fidelity approximation in regions of low density during kernel density estimation. Setting this to True reduces the run time of the planner.
parallel (bool) – whether to run the code in parallel.
sampling_strategies (int) – number of sampling strategies to use. Each sampling strategy uses a different balance between exploration and exploitation.
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.
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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
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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)
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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
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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.
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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.