GPyOpt.acquisitions package¶
Submodules¶
GPyOpt.acquisitions.EI module¶
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class
GPyOpt.acquisitions.EI.
AcquisitionEI
(model, space, optimizer=None, cost_withGradients=None, jitter=0.01)¶ Bases:
GPyOpt.acquisitions.base.AcquisitionBase
Expected improvement acquisition function
Parameters: - model – GPyOpt class of model
- space – GPyOpt class of domain
- optimizer – optimizer of the acquisition. Should be a GPyOpt optimizer
- cost_withGradients – function
- jitter – positive value to make the acquisition more explorative.
Note
allows to compute the Improvement per unit of cost
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analytical_gradient_prediction
= True¶
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static
fromConfig
(space, optimizer, cost_withGradients, config)¶
GPyOpt.acquisitions.EI_mcmc module¶
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class
GPyOpt.acquisitions.EI_mcmc.
AcquisitionEI_MCMC
(model, space, optimizer=None, cost_withGradients=None, jitter=0.01)¶ Bases:
GPyOpt.acquisitions.EI.AcquisitionEI
Integrated Expected improvement acquisition function
Parameters: - model – GPyOpt class of model
- space – GPyOpt class of domain
- optimizer – optimizer of the acquisition. Should be a GPyOpt optimizer
- cost_withGradients – function
- jitter – positive value to make the acquisition more explorative
Note
allows to compute the Improvement per unit of cost
-
analytical_gradient_prediction
= True¶
GPyOpt.acquisitions.LCB module¶
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class
GPyOpt.acquisitions.LCB.
AcquisitionLCB
(model, space, optimizer=None, cost_withGradients=None, exploration_weight=2)¶ Bases:
GPyOpt.acquisitions.base.AcquisitionBase
GP-Lower Confidence Bound acquisition function
Parameters: - model – GPyOpt class of model
- space – GPyOpt class of domain
- optimizer – optimizer of the acquisition. Should be a GPyOpt optimizer
- cost_withGradients – function
- jitter – positive value to make the acquisition more explorative
Note
does not allow to be used with cost
-
analytical_gradient_prediction
= True¶
GPyOpt.acquisitions.LCB_mcmc module¶
-
class
GPyOpt.acquisitions.LCB_mcmc.
AcquisitionLCB_MCMC
(model, space, optimizer=None, cost_withGradients=None, exploration_weight=2)¶ Bases:
GPyOpt.acquisitions.LCB.AcquisitionLCB
Integrated GP-Lower Confidence Bound acquisition function
Parameters: - model – GPyOpt class of model
- space – GPyOpt class of domain
- optimizer – optimizer of the acquisition. Should be a GPyOpt optimizer
- cost_withGradients – function
- exploration_weight – positive parameter to control exploration / exploitation
Note
allows to compute the Improvement per unit of cost
-
analytical_gradient_prediction
= True¶
GPyOpt.acquisitions.LP module¶
-
class
GPyOpt.acquisitions.LP.
AcquisitionLP
(model, space, optimizer, acquisition, transform='none')¶ Bases:
GPyOpt.acquisitions.base.AcquisitionBase
Class for Local Penalization acquisition. Used for batch design. :param model: model of the class GPyOpt :param space: design space of the class GPyOpt. :param optimizer: optimizer of the class GPyOpt. :param acquisition: acquisition function of the class GPyOpt :param transform: transformation applied to the acquisition (default, none).
Note
irrespective of the transformation applied the penalized acquisition is always mapped again to the log space.
This way gradients can be computed additively and are more stable.
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acquisition_function
(x)¶ Returns the value of the acquisition function at x.
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acquisition_function_withGradients
(x)¶ Returns the acquisition function and its its gradient at x.
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analytical_gradient_prediction
= True¶
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d_acquisition_function
(x)¶ Returns the gradient of the acquisition function at x.
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update_batches
(X_batch, L, Min)¶ Updates the batches internally and pre-computes the
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GPyOpt.acquisitions.MPI module¶
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class
GPyOpt.acquisitions.MPI.
AcquisitionMPI
(model, space, optimizer=None, cost_withGradients=None, jitter=0.01)¶ Bases:
GPyOpt.acquisitions.base.AcquisitionBase
Maximum probability of improvement acquisition function
Parameters: - model – GPyOpt class of model
- space – GPyOpt class of domain
- optimizer – optimizer of the acquisition. Should be a GPyOpt optimizer
- cost_withGradients – function
- jitter – positive value to make the acquisition more explorative
Note
allows to compute the Improvement per unit of cost
-
analytical_gradient_prediction
= True¶
-
static
fromConfig
(space, optimizer, cost_withGradients, config)¶
GPyOpt.acquisitions.MPI_mcmc module¶
-
class
GPyOpt.acquisitions.MPI_mcmc.
AcquisitionMPI_MCMC
(model, space, optimizer=None, cost_withGradients=None, jitter=0.01)¶ Bases:
GPyOpt.acquisitions.MPI.AcquisitionMPI
Integrated Maximum Probability of Improvement acquisition function
Parameters: - model – GPyOpt class of model
- space – GPyOpt class of domain
- optimizer – optimizer of the acquisition. Should be a GPyOpt optimizer
- cost_withGradients – function
- jitter – positive value to make the acquisition more explorative
Note
allows to compute the Improvement per unit of cost
-
analytical_gradient_prediction
= True¶
GPyOpt.acquisitions.base module¶
-
class
GPyOpt.acquisitions.base.
AcquisitionBase
(model, space, optimizer, cost_withGradients=None)¶ Bases:
object
Base class for acquisition functions in Bayesian Optimization
Parameters: - model – GPyOpt class of model
- space – GPyOpt class of domain
- optimizer – optimizer of the acquisition. Should be a GPyOpt optimizer
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acquisition_function
(x)¶ Takes an acquisition and weights it so the domain and cost are taken into account.
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acquisition_function_withGradients
(x)¶ Takes an acquisition and it gradient and weights it so the domain and cost are taken into account.
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analytical_gradient_prediction
= False¶
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static
fromDict
(space, optimizer, cost_withGradients, config)¶
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optimize
(duplicate_manager=None)¶ Optimizes the acquisition function (uses a flag from the model to use gradients or not).