GPyOpt¶
GPyOpt does this and that…
For details please refer to http://sheffieldml.github.io/GPyOpt/
Not sure why it is not showing the docstring and Args here below…?
-
class
olympus.planners.Gpyopt(goal='minimize', batch_size=1, exact_eval=True, model_type='GP_MCMC', acquisition_type='EI_MCMC')[source] Gaussian Process optimization as implemented in GPyOpt.
- Parameters
goal (str) – The optimization goal, either ‘minimize’ or ‘maximize’. Default is ‘minimize’.
batch_size (int) – size of the batch in which the objective is evaluated.
exact_eval (bool) – whether the outputs are exact.
model_type (str) – type of model to use as surrogate. ‘GP’: standard Gaussian process. ‘GP_MCMC’: Gaussian process with prior in the hyper-parameters. ‘sparseGP’: sparse Gaussian process. ‘warperdGP’: warped Gaussian process. ‘InputWarpedGP’: input warped Gaussian process. ‘RF’: random forest (scikit-learn).
acquisition_type (str) – type of acquisition function to use. ‘EI’: expected improvement. ‘EI_MCMC’: integrated expected improvement (requires GP_MCMC model). ‘MPI’: maximum probability of improvement. ‘MPI_MCMC’: maximum probability of improvement (requires GP_MCMC model). ‘LCB’: GP-Lower confidence bound. ‘LCB_MCMC’: integrated GP-Lower confidence bound (requires GP_MCMC model).
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.