bayeso.bo.base_bo¶
It defines an abstract class of Bayesian optimization.
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class
bayeso.bo.base_bo.
BaseBO
(range_X: numpy.ndarray, str_surrogate: str, str_acq: str, str_optimizer_method_bo: str, normalize_Y: bool, debug: bool)¶ Bases:
abc.ABC
It is a Bayesian optimization class.
Parameters: - range_X (numpy.ndarray) – a search space. Shape: (d, 2).
- str_surrogate (str.) – the name of surrogate model.
- str_acq (str.) – the name of acquisition function.
- str_optimizer_method_bo (str.) – the name of optimization method for Bayesian optimization.
- normalize_Y (bool.) – flag for normalizing outputs.
- debug (bool.) – flag for printing log messages.
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_abc_impl
= <_abc_data object>¶
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_get_bounds
() → List[T]¶ It returns list of range tuples, obtained from self.range_X.
Returns: list of range tuples. Return type: list
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_get_random_state
(seed: Optional[int])¶ It returns a random state, defined by seed.
Parameters: seed (NoneType or int.) – None, or a random seed. Returns: a random state. Return type: numpy.random.RandomState Raises: AssertionError
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_get_samples_gaussian
(num_samples: int, seed: Optional[int] = None) → numpy.ndarray¶ It returns num_samples examples sampled from Gaussian distribution.
Parameters: - num_samples (int.) – the number of samples.
- seed (NoneType or int., optional) – None, or random seed.
Returns: random examples. Shape: (num_samples, d).
Return type: numpy.ndarray
Raises: AssertionError
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_get_samples_grid
(num_grids: int = 50) → numpy.ndarray¶ It returns grids of self.range_X.
Parameters: num_grids (int., optional) – the number of grids. Returns: grids of self.range_X. Shape: (num_grids\(^{\text{d}}\), d). Return type: numpy.ndarray Raises: AssertionError
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_get_samples_halton
(num_samples: int, seed: Optional[int] = None) → numpy.ndarray¶ It returns num_samples examples sampled by Halton algorithm.
Parameters: - num_samples (int.) – the number of samples.
- seed (NoneType or int., optional) – None, or random seed.
Returns: examples sampled by Halton algorithm. Shape: (num_samples, d).
Return type: numpy.ndarray
Raises: AssertionError
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_get_samples_sobol
(num_samples: int, seed: Optional[int] = None) → numpy.ndarray¶ It returns num_samples examples sampled from Sobol’ sequence.
Parameters: - num_samples (int.) – the number of samples.
- seed (NoneType or int., optional) – None, or random seed.
Returns: examples sampled from Sobol’ sequence. Shape: (num_samples, d).
Return type: numpy.ndarray
Raises: AssertionError
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_get_samples_uniform
(num_samples: int, seed: Optional[int] = None) → numpy.ndarray¶ It returns num_samples examples uniformly sampled.
Parameters: - num_samples (int.) – the number of samples.
- seed (NoneType or int., optional) – None, or random seed.
Returns: random examples. Shape: (num_samples, d).
Return type: numpy.ndarray
Raises: AssertionError
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compute_acquisitions
()¶ It is an abstract method.
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compute_posteriors
()¶ It is an abstract method.
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get_initials
(str_initial_method: str, num_initials: int, seed: Optional[int] = None) → numpy.ndarray¶ It returns num_initials examples, sampled by a sampling method str_initial_method.
Parameters: - str_initial_method (str.) – the name of sampling method.
- num_initials (int.) – the number of samples.
- seed (NoneType or int., optional) – None, or random seed.
Returns: sampled examples. Shape: (num_samples, d).
Return type: numpy.ndarray
Raises: AssertionError
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get_samples
(str_sampling_method: str, fun_objective: Optional[Callable] = None, num_samples: int = 100, seed: Optional[int] = None) → numpy.ndarray¶ It returns num_samples examples, sampled by a sampling method str_sampling_method.
Parameters: - str_sampling_method (str.) – the name of sampling method.
- fun_objective (NoneType or callable, optional) – None, or objective function.
- num_samples (int., optional) – the number of samples.
- seed (NoneType or int., optional) – None, or random seed.
Returns: sampled examples. Shape: (num_samples, d).
Return type: numpy.ndarray
Raises: AssertionError
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optimize
()¶ It is an abstract method.