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Abstract prior

smac.model.gaussian_process.priors.abstract_prior #

AbstractPrior #

AbstractPrior(seed: int = 0)

Abstract base class to define the interface for priors of Gaussian process hyperparameters.

This class is adapted from RoBO:

Klein, A. and Falkner, S. and Mansur, N. and Hutter, F. RoBO: A Flexible and Robust Bayesian Optimization Framework in Python In: NIPS 2017 Bayesian Optimization Workshop

Note#

Whenever lnprob or the gradient is computed for a scalar input, we use math. rather than np..

Parameters#

seed : int, defaults to 0

Source code in smac/model/gaussian_process/priors/abstract_prior.py 
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def __init__(self, seed: int = 0):
    self._seed = seed
    self._rng = np.random.RandomState(seed)

meta property #

meta: dict[str, Any]

Returns the meta data of the created object.

get_gradient #

get_gradient(theta: float) -> float

Computes the gradient of the prior with respect to theta. Internally, his method calls self._get_gradient.

Warning#

Theta must be on the original scale.

Parameters#

theta : float Hyperparameter configuration in log space

Returns#

gradient : float The gradient of the prior at theta.

Source code in smac/model/gaussian_process/priors/abstract_prior.py 
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def get_gradient(self, theta: float) -> float:
    """Computes the gradient of the prior with respect to theta. Internally, his method calls `self._get_gradient`.

    Warning
    -------
    Theta must be on the original scale.

    Parameters
    ----------
    theta : float
        Hyperparameter configuration in log space

    Returns
    -------
    gradient : float
        The gradient of the prior at theta.
    """
    return self._get_gradient(np.exp(theta))

get_log_probability #

get_log_probability(theta: float) -> float

Returns the log probability of theta. This method exponentiates theta and calls self._get_log_probability.

Warning#

Theta must be on a log scale!

Parameters#

theta : float Hyperparameter configuration in log space.

Returns#

float The log probability of theta

Source code in smac/model/gaussian_process/priors/abstract_prior.py 
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def get_log_probability(self, theta: float) -> float:
    """Returns the log probability of theta. This method exponentiates theta and calls `self._get_log_probability`.

    Warning
    -------
    Theta must be on a log scale!

    Parameters
    ----------
    theta : float
        Hyperparameter configuration in log space.

    Returns
    -------
    float
        The log probability of theta
    """
    return self._get_log_probability(np.exp(theta))

sample_from_prior #

sample_from_prior(n_samples: int) -> ndarray

Returns n_samples from the prior. All samples are on a log scale. This method calls self._sample_from_prior and applies a log transformation to the obtained values.

Parameters#

n_samples : int The number of samples that will be drawn.

Returns#

samples : np.ndarray

Source code in smac/model/gaussian_process/priors/abstract_prior.py 
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def sample_from_prior(self, n_samples: int) -> np.ndarray:
    """Returns `n_samples` from the prior. All samples are on a log scale. This method calls
    `self._sample_from_prior` and applies a log transformation to the obtained values.

    Parameters
    ----------
    n_samples : int
        The number of samples that will be drawn.

    Returns
    -------
    samples : np.ndarray
    """
    if np.ndim(n_samples) != 0:
        raise ValueError("argument n_samples needs to be a scalar (is %s)" % n_samples)

    if n_samples <= 0:
        raise ValueError("argument n_samples needs to be positive (is %d)" % n_samples)

    sample = np.log(self._sample_from_prior(n_samples=n_samples))

    if np.any(~np.isfinite(sample)):
        raise ValueError("Sample %s from prior %s contains infinite values!" % (sample, self))

    return sample