Source code for smac.model.gaussian_process.priors.abstract_prior

from __future__ import annotations

from abc import abstractmethod
from typing import Any

import numpy as np

__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"


[docs] class AbstractPrior: """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 """ def __init__(self, seed: int = 0): self._seed = seed self._rng = np.random.RandomState(seed) @property def meta(self) -> dict[str, Any]: """Returns the meta data of the created object.""" return { "name": self.__class__.__name__, "seed": self._seed, }
[docs] 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
[docs] 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))
[docs] 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))
@abstractmethod def _get_log_probability(self, theta: float) -> float: """Return the log probability of theta. Warning ------- Theta must be on the original scale. Parameters ---------- theta : float Hyperparameter configuration on the original scale. Returns ------- float The log probability of theta """ raise NotImplementedError() @abstractmethod def _get_gradient(self, theta: float) -> float: """Computes the gradient of the prior with respect to theta. Parameters ---------- theta : float Hyperparameter configuration in the original space space Returns ------- gradient : float The gradient of the prior at theta. """ raise NotImplementedError() @abstractmethod def _sample_from_prior(self, n_samples: int) -> np.ndarray: """Returns `n_samples` from the prior. All samples are on a original scale. Parameters ---------- n_samples : int The number of samples that will be drawn. Returns ------- np.ndarray """ raise NotImplementedError()