pyGPGO.GPGO module¶
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class
pyGPGO.GPGO.GPGO(surrogate, acquisition, f, parameter_dict, n_jobs=1)[source]¶ Bases:
objectBayesian Optimization class.
Parameters: - Surrogate (Surrogate model instance) – Gaussian Process surrogate model instance.
- Acquisition (Acquisition instance) – Acquisition instance.
- f (fun) – Function to maximize over parameters specified by parameter_dict.
- parameter_dict (dict) – Dictionary specifying parameter, their type and bounds.
- n_jobs (int. Default 1) – Parallel threads to use during acquisition optimization.
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__init__(surrogate, acquisition, f, parameter_dict, n_jobs=1)[source]¶ Bayesian Optimization class.
Parameters: - Surrogate (Surrogate model instance) – Gaussian Process surrogate model instance.
- Acquisition (Acquisition instance) – Acquisition instance.
- f (fun) – Function to maximize over parameters specified by parameter_dict.
- parameter_dict (dict) – Dictionary specifying parameter, their type and bounds.
- n_jobs (int. Default 1) – Parallel threads to use during acquisition optimization.
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parameter_key Parameters to consider in optimization
Type: list
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parameter_type Parameter types.
Type: list
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parameter_range Parameter bounds during optimization
Type: list
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history Target values evaluated along the procedure.
Type: list
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_acqWrapper(xnew)[source]¶ Evaluates the acquisition function on a point.
Parameters: xnew (np.ndarray, shape=((len(self.parameter_key),))) – Point to evaluate the acquisition function on. Returns: Acquisition function value for xnew. Return type: float
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_firstRun(n_eval=3)[source]¶ Performs initial evaluations before fitting GP.
Parameters: n_eval (int) – Number of initial evaluations to perform. Default is 3.
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_optimizeAcq(method='L-BFGS-B', n_start=100)[source]¶ Optimizes the acquisition function using a multistart approach.
Parameters: - method (str. Default 'L-BFGS-B'.) – Any scipy.optimize method that admits bounds and gradients. Default is ‘L-BFGS-B’.
- n_start (int.) – Number of starting points for the optimization procedure. Default is 100.
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_sampleParam()[source]¶ Randomly samples parameters over bounds.
Returns: A random sample of specified parameters. Return type: dict
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getResult()[source]¶ Prints best result in the Bayesian Optimization procedure.
Returns: - OrderedDict – Point yielding best evaluation in the procedure.
- float – Best function evaluation.