smac.model.gaussian_process.priors.abstract_prior

Classes

AbstractPrior([seed])

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

Interfaces

class smac.model.gaussian_process.priors.abstract_prior.AbstractPrior(seed=0)

Bases: object

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) –

get_gradient(theta)

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)

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.

Return type:

dict[str, Any]

sample_from_prior(n_samples)

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

Return type:

np.ndarray

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