bayeso.acquisition¶
It defines acquisition functions.
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bayeso.acquisition.
aei
(pred_mean: numpy.ndarray, pred_std: numpy.ndarray, Y_train: numpy.ndarray, noise: float, jitter: float = 1e-05) → numpy.ndarray¶ It is an augmented expected improvement criterion.
Parameters: - pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
- pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
- Y_train (numpy.ndarray) – outputs of X_train. Shape: (n, 1).
- noise (float) – noise for augmenting exploration.
- jitter (float, optional) – jitter for pred_std.
Returns: acquisition function values. Shape: (l, ).
Return type: numpy.ndarray
Raises: AssertionError
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bayeso.acquisition.
ei
(pred_mean: numpy.ndarray, pred_std: numpy.ndarray, Y_train: numpy.ndarray, jitter: float = 1e-05) → numpy.ndarray¶ It is an expected improvement criterion.
Parameters: - pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
- pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
- Y_train (numpy.ndarray) – outputs of X_train. Shape: (n, 1).
- jitter (float, optional) – jitter for pred_std.
Returns: acquisition function values. Shape: (l, ).
Return type: numpy.ndarray
Raises: AssertionError
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bayeso.acquisition.
pi
(pred_mean: numpy.ndarray, pred_std: numpy.ndarray, Y_train: numpy.ndarray, jitter: float = 1e-05) → numpy.ndarray¶ It is a probability improvement criterion.
Parameters: - pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
- pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
- Y_train (numpy.ndarray) – outputs of X_train. Shape: (n, 1).
- jitter (float, optional) – jitter for pred_std.
Returns: acquisition function values. Shape: (l, ).
Return type: numpy.ndarray
Raises: AssertionError
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bayeso.acquisition.
pure_exploit
(pred_mean: numpy.ndarray) → numpy.ndarray¶ It is a pure exploitation criterion.
Parameters: pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ). Returns: acquisition function values. Shape: (l, ). Return type: numpy.ndarray Raises: AssertionError
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bayeso.acquisition.
pure_explore
(pred_std: numpy.ndarray) → numpy.ndarray¶ It is a pure exploration criterion.
Parameters: pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ). Returns: acquisition function values. Shape: (l, ). Return type: numpy.ndarray Raises: AssertionError
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bayeso.acquisition.
ucb
(pred_mean: numpy.ndarray, pred_std: numpy.ndarray, Y_train: Optional[numpy.ndarray] = None, kappa: float = 2.0, increase_kappa: bool = True) → numpy.ndarray¶ It is a Gaussian process upper confidence bound criterion.
Parameters: - pred_mean (numpy.ndarray) – posterior predictive mean function over X_test. Shape: (l, ).
- pred_std (numpy.ndarray) – posterior predictive standard deviation function over X_test. Shape: (l, ).
- Y_train (numpy.ndarray, optional) – outputs of X_train. Shape: (n, 1).
- kappa (float, optional) – trade-off hyperparameter between exploration and exploitation.
- increase_kappa (bool., optional) – flag for increasing a kappa value as Y_train grows. If Y_train is None, it is ignored, which means kappa is fixed.
Returns: acquisition function values. Shape: (l, ).
Return type: numpy.ndarray
Raises: AssertionError