GPyOpt.util package¶
Submodules¶
GPyOpt.util.arguments_manager module¶
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class
GPyOpt.util.arguments_manager.
ArgumentsManager
(kwargs)¶ Bases:
object
Class to handle extra configurations in the definition of the BayesianOptimization class
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acquisition_creator
(acquisition_type, model, space, acquisition_optimizer, cost_withGradients)¶ Acquisition chooser from the available options. Extra parameters can be passed via **kwargs.
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evaluator_creator
(evaluator_type, acquisition, batch_size, model_type, model, space, acquisition_optimizer)¶ Acquisition chooser from the available options. Guide the optimization through sequential or parallel evalutions of the objective.
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GPyOpt.util.duplicate_manager module¶
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class
GPyOpt.util.duplicate_manager.
DuplicateManager
(space, zipped_X, pending_zipped_X=None, ignored_zipped_X=None)¶ Bases:
object
Class to manage potential duplicates in the suggested candidates.
Parameters: - space – object managing all the logic related the domain of the optimization
- zipped_X – matrix of evaluated configurations
- pending_zipped_X – matrix of configurations in the pending state
- ignored_zipped_X – matrix of configurations that the user desires to ignore (e.g., because they may have led to failures)
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is_unzipped_x_duplicate
(unzipped_x)¶ param: unzipped_x: configuration assumed to be unzipped
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is_zipped_x_duplicate
(zipped_x)¶ param: zipped_x: configuration assumed to be zipped
GPyOpt.util.general module¶
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GPyOpt.util.general.
best_guess
(f, X)¶ Gets the best current guess from a vector. :param f: function to evaluate. :param X: locations.
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GPyOpt.util.general.
best_value
(Y, sign=1)¶ Returns a vector whose components i are the minimum (default) or maximum of Y[:i]
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GPyOpt.util.general.
compute_integrated_acquisition
(acquisition, x)¶ Used to compute the acquisition function when samples of the hyper-parameters have been generated (used in GP_MCMC model).
Parameters: - acquisition – acquisition function with GpyOpt model type GP_MCMC.
- x – location where the acquisition is evaluated.
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GPyOpt.util.general.
compute_integrated_acquisition_withGradients
(acquisition, x)¶ Used to compute the acquisition function with gradients when samples of the hyper-parameters have been generated (used in GP_MCMC model).
Parameters: - acquisition – acquisition function with GpyOpt model type GP_MCMC.
- x – location where the acquisition is evaluated.
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GPyOpt.util.general.
evaluate_function
(f, X)¶ Returns the evaluation of a function f and the time per evaluation
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GPyOpt.util.general.
get_d_moments
(model, x)¶ Gradients with respect to x of the moments (mean and sdev.) of the GP :param model: GPy model. :param x: location where the gradients are evaluated.
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GPyOpt.util.general.
get_moments
(model, x)¶ Moments (mean and sdev.) of a GP model at x
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GPyOpt.util.general.
get_quantiles
(acquisition_par, fmin, m, s)¶ Quantiles of the Gaussian distribution useful to determine the acquisition function values :param acquisition_par: parameter of the acquisition function :param fmin: current minimum. :param m: vector of means. :param s: vector of standard deviations.
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GPyOpt.util.general.
merge_values
(values1, values2)¶ Merges two numpy arrays by calculating all possible combinations of rows
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GPyOpt.util.general.
normalize
(Y, normalization_type='stats')¶ Normalize the vector Y using statistics or its range.
Parameters: - Y – Row or column vector that you want to normalize.
- normalization_type – String specifying the kind of normalization
to use. Options are ‘stats’ to use mean and standard deviation, or ‘maxmin’ to use the range of function values. :return Y_normalized: The normalized vector.
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GPyOpt.util.general.
reshape
(x, input_dim)¶ Reshapes x into a matrix with input_dim columns
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GPyOpt.util.general.
samples_multidimensional_uniform
(bounds, num_data)¶ Generates a multidimensional grid uniformly distributed. :param bounds: tuple defining the box constraints. :num_data: number of data points to generate.
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GPyOpt.util.general.
spawn
(f)¶ Function for parallel evaluation of the acquisition function
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GPyOpt.util.general.
values_to_array
(input_values)¶ Transforms a values of int, float and tuples to a column vector numpy array
GPyOpt.util.io module¶
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GPyOpt.util.io.
gen_datestr
()¶ Returns a string with the yy/mm/dd and hh/mm/ss