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Gamma prior

smac.model.gaussian_process.priors.gamma_prior #

GammaPrior #

GammaPrior(
    a: float, scale: float, loc: float, seed: int = 0
)

Bases: AbstractPrior

Implementation of gamma prior.

f(x) = (x-loc)(a-1) * e(-(x-loc)) * (1/scale)**a / gamma(a)

Parameters#

a : float The shape parameter. Must be greater than 0. scale : float The scale parameter (1/scale corresponds to parameter p in canonical form). Must be greather than 0. loc : float Mean parameter for the distribution. seed : int, defaults to 0

Source code in smac/model/gaussian_process/priors/gamma_prior.py 
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def __init__(
    self,
    a: float,
    scale: float,
    loc: float,
    seed: int = 0,
):
    super().__init__(seed=seed)
    assert a > 0
    assert scale > 0

    self._a = a
    self._loc = loc
    self._scale = scale

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