Phoenics

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class olympus.planners.Phoenics(*args, **kwargs)[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.