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

from __future__ import annotations

from typing import Any

import warnings

import numpy as np

from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior

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


[docs]class TophatPrior(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 """ def __init__( self, lower_bound: float, upper_bound: float, seed: int = 0, ): super().__init__(seed=seed) self._min = lower_bound self._log_min = np.log(lower_bound) self._max = upper_bound self._log_max = np.log(upper_bound) self._prob = 1 / (self._max - self._min) self._log_prob = np.log(self._prob) if not (self._max > self._min): raise Exception("Upper bound of Tophat prior must be greater than the lower bound.") @property def meta(self) -> dict[str, Any]: # noqa: D102 meta = super().meta meta.update({"lower_bound": self._min, "upper_bound": self._max}) return meta def _get_log_probability(self, theta: float) -> float: if theta < self._min or theta > self._max: return -np.inf else: return self._log_prob def _sample_from_prior(self, n_samples: int) -> np.ndarray: if np.ndim(n_samples) != 0: raise ValueError("The argument `n_samples` needs to be a scalar (is %s)." % n_samples) if n_samples <= 0: raise ValueError("The argument `n_samples` needs to be positive (is %d)." % n_samples) p0 = np.exp(self._rng.uniform(low=self._log_min, high=self._log_max, size=(n_samples,))) return p0 def _get_gradient(self, theta: float) -> float: return 0
[docs] def get_gradient(self, theta: float) -> float: # noqa: D102 return 0
[docs]class SoftTopHatPrior(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 """ def __init__( self, lower_bound: float, upper_bound: float, exponent: float, seed: int = 0, ) -> None: super().__init__(seed=seed) with warnings.catch_warnings(): warnings.simplefilter("error") self._lower_bound = lower_bound try: self._log_lower_bound = np.log(lower_bound) except RuntimeWarning as w: if "invalid value encountered in log" in w.args[0]: raise ValueError("Invalid lower bound %f (cannot compute log)" % lower_bound) raise w self._upper_bound = upper_bound try: self._log_upper_bound = np.log(upper_bound) except RuntimeWarning as w: if "invalid value encountered in log" in w.args[0]: raise ValueError("Invalid lower bound %f (cannot compute log)" % lower_bound) raise w if exponent <= 0: raise ValueError("Exponent cannot be less or equal than zero (but is %f)" % exponent) self._exponent = exponent def __repr__(self) -> str: return "SoftTopHatPrior(lower_bound=%f, upper_bound=%f)" % ( self._lower_bound, self._upper_bound, ) @property def meta(self) -> dict[str, Any]: # noqa: D102 meta = super().meta meta.update({"lower_bound": self._lower_bound, "upper_bound": self._upper_bound, "exponent": self._exponent}) return meta
[docs] def get_log_probability(self, theta: float) -> float: # noqa: D102 # We need to use lnprob here instead of _lnprob to have the squared function work # in the logarithmic space, too. if np.ndim(theta) == 0: if theta < self._log_lower_bound: return -((theta - self._log_lower_bound) ** self._exponent) elif theta > self._log_upper_bound: return -((self._log_upper_bound - theta) ** self._exponent) else: return 0 else: raise NotImplementedError()
[docs] def get_gradient(self, theta: float) -> float: # noqa: D102 if np.ndim(theta) == 0: if theta < self._log_lower_bound: return -self._exponent * (theta - self._log_lower_bound) elif theta > self._log_upper_bound: return self._exponent * (self._log_upper_bound - theta) else: return 0 else: raise NotImplementedError()
def _get_log_probability(self, theta: float) -> float: return 0 def _get_gradient(self, theta: float) -> float: return 0 def _sample_from_prior(self, n_samples: int) -> np.ndarray: return np.exp(self._rng.uniform(self._log_lower_bound, self._log_upper_bound, size=(n_samples,)))