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Getting Started

  • Overview
  • Installation
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Examples

  • Experiment emulators
  • Run a simple benchmark
  • Run a larger benchmark
  • Custom Dataset
  • Custom Planner

Core Classes

  • Planners
    • Bayesian Algorithms
    • Evolutionary Algorithms
    • Gradient Methods
    • Grid-Like Searches
      • Grid Search
      • Latin Hypercube Sampling
      • Sobol Sampling
      • Random Search
    • Others
    • Planner Function
  • Datasets
  • Models
  • Emulators
  • Surfaces
  • Noises

Advanced Usage

  • Custom Emulators
  • Custom Planners

Complete API Reference

  • olympus package
Olympus
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  • Random Search
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Random Search¶

does this and that…

class olympus.planners.RandomSearch(goal='minimize', seed=None)[source]

creates empty object and loads defaults

Parameters
  • me (str) – arbitrary name to identify the object

  • indent (int) – number of spaces used in string representation

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.

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© Copyright 2020, Matteo Aldeghi, Riley Hickman and Florian Häse

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