bayeso.bo.bo_w_tp¶
It defines a class of Bayesian optimization with Student-\(t\) process regression.
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class
bayeso.bo.bo_w_tp.
BOwTP
(range_X: numpy.ndarray, str_cov: str = 'matern52', str_acq: str = 'ei', normalize_Y: bool = True, use_ard: bool = True, prior_mu: Optional[Callable] = None, str_optimizer_method_tp: str = 'SLSQP', str_optimizer_method_bo: str = 'L-BFGS-B', debug: bool = False)¶ Bases:
bayeso.bo.base_bo.BaseBO
It is a Bayesian optimization class with Student-\(t\) process regression.
Parameters: - range_X (numpy.ndarray) – a search space. Shape: (d, 2).
- str_cov (str., optional) – the name of covariance function.
- str_acq (str., optional) – the name of acquisition function.
- normalize_Y (bool., optional) – flag for normalizing outputs.
- use_ard (bool., optional) – flag for automatic relevance determination.
- prior_mu (NoneType, or callable, optional) – None, or prior mean function.
- str_optimizer_method_tp (str., optional) – the name of optimization method for Student-\(t\) process regression.
- str_optimizer_method_bo (str., optional) – the name of optimization method for Bayesian optimization.
- debug (bool., optional) – flag for printing log messages.
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_abc_impl
= <_abc_data object>¶
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_optimize
(fun_negative_acquisition: Callable, str_sampling_method: str, num_samples: int) → Tuple[numpy.ndarray, numpy.ndarray]¶ It optimizes fun_negative_function with self.str_optimizer_method_bo. num_samples examples are determined by str_sampling_method, to start acquisition function optimization.
Parameters: - fun_objective (callable) – negative acquisition function.
- str_sampling_method (str.) – the name of sampling method.
- num_samples (int.) – the number of samples.
Returns: tuple of next point to evaluate and all candidates determined by acquisition function optimization. Shape: ((d, ), (num_samples, d)).
Return type: (numpy.ndarray, numpy.ndarray)
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compute_acquisitions
(X: numpy.ndarray, X_train: numpy.ndarray, Y_train: numpy.ndarray, cov_X_X: numpy.ndarray, inv_cov_X_X: numpy.ndarray, hyps: dict) → numpy.ndarray¶ It computes acquisition function values over ‘X’, where X_train, Y_train, cov_X_X, inv_cov_X_X, and hyps are given.
Parameters: - X (numpy.ndarray) – inputs. Shape: (l, d) or (l, m, d).
- X_train (numpy.ndarray) – inputs. Shape: (n, d) or (n, m, d).
- Y_train (numpy.ndarray) – outputs. Shape: (n, 1).
- cov_X_X (numpy.ndarray) – kernel matrix over X_train. Shape: (n, n).
- inv_cov_X_X (numpy.ndarray) – kernel matrix inverse over X_train. Shape: (n, n).
- hyps (dict.) – dictionary of hyperparameters.
Returns: acquisition function values over X. Shape: (l, ).
Return type: numpy.ndarray
Raises: AssertionError
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compute_posteriors
(X_train: numpy.ndarray, Y_train: numpy.ndarray, X_test: numpy.ndarray, cov_X_X: numpy.ndarray, inv_cov_X_X: numpy.ndarray, hyps: dict) → numpy.ndarray¶ It returns posterior mean and standard deviation over X_test.
Parameters: - X_train (numpy.ndarray) – inputs. Shape: (n, d) or (n, m, d).
- Y_train (numpy.ndarray) – outputs. Shape: (n, 1).
- X_test (numpy.ndarray) – inputs. Shape: (l, d) or (l, m, d).
- cov_X_X (numpy.ndarray) – kernel matrix over X_train. Shape: (n, n).
- inv_cov_X_X (numpy.ndarray) – kernel matrix inverse over X_train. Shape: (n, n).
- hyps (dict.) – dictionary of hyperparameters for Gaussian process.
Returns: posterior mean and standard deviation over X_test. Shape: ((l, ), (l, )).
Return type: (numpy.ndarray, numpy.ndarray)
Raises: AssertionError
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optimize
(X_train: numpy.ndarray, Y_train: numpy.ndarray, str_sampling_method: str = 'sobol', num_samples: int = 100) → Tuple[numpy.ndarray, dict]¶ It computes acquired example, candidates of acquired examples, acquisition function values for the candidates, covariance matrix, inverse matrix of the covariance matrix, hyperparameters optimized, and execution times.
Parameters: - X_train (numpy.ndarray) – inputs. Shape: (n, d) or (n, m, d).
- Y_train (numpy.ndarray) – outputs. Shape: (n, 1).
- str_sampling_method (str., optional) – the name of sampling method for acquisition function optimization.
- num_samples (int., optional) – the number of samples.
Returns: acquired example and dictionary of information. Shape: ((d, ), dict.).
Return type: (numpy.ndarray, dict.)
Raises: AssertionError