smac.model.gaussian_process.priors

Interfaces

class smac.model.gaussian_process.priors.GammaPrior(a, scale, loc, seed=0)[source]

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)

property meta: dict[str, Any]

Returns the meta data of the created object.

class smac.model.gaussian_process.priors.HorseshoePrior(scale, seed=0)[source]

Bases: AbstractPrior

Horseshoe Prior as it is used in spearmint.

Parameters:

  • scale (float) – Scaling parameter.

  • seed (int, defaults to 0)

property meta: dict[str, Any]

Returns the meta data of the created object.

class smac.model.gaussian_process.priors.LogNormalPrior(sigma, mean=0, seed=0)[source]

Bases: AbstractPrior

Implements the log normal prior.

Parameters:

  • sigma (float) – Specifies the standard deviation of the normal distribution.

  • mean (float) – Specifies the mean of the normal distribution.

  • seed (int, defaults to 0)

property meta: dict[str, Any]

Returns the meta data of the created object.

class smac.model.gaussian_process.priors.SoftTopHatPrior(lower_bound, upper_bound, exponent, seed=0)[source]

Bases: AbstractPrior

Soft Tophat prior as it used in the original spearmint code.

Parameters:

  • lower_bound (float) – Lower bound of the prior. In original scale.

  • upper_bound (float) – Upper bound of the prior. In original scale.

  • exponent (float) – Exponent of the prior.

  • seed (int, defaults to 0)

get_gradient(theta)[source]

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 – The gradient of the prior at theta.

Return type:

float

get_log_probability(theta)[source]

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:

The log probability of theta

Return type:

float

property meta: dict[str, Any]

Returns the meta data of the created object.

class smac.model.gaussian_process.priors.TophatPrior(lower_bound, upper_bound, seed=0)[source]

Bases: AbstractPrior

Tophat prior as it used in the original spearmint code.

Parameters:

  • lower_bound (float) – Lower bound of the prior. In original scale.

  • upper_bound (float) – Upper bound of the prior. In original scale.

  • seed (int, defaults to 0)

get_gradient(theta)[source]

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 – The gradient of the prior at theta.

Return type:

float

property meta: dict[str, Any]

Returns the meta data of the created object.

Modules

smac.model.gaussian_process.priors.abstract_prior

smac.model.gaussian_process.priors.gamma_prior

smac.model.gaussian_process.priors.horseshoe_prior

smac.model.gaussian_process.priors.log_normal_prior

smac.model.gaussian_process.priors.tophat_prior

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