from __future__ import annotations
from typing import Any
import numpy as np
import scipy.stats as sps
from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior
__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"
[docs]
class GammaPrior(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
"""
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
@property
def meta(self) -> dict[str, Any]: # noqa: D102
meta = super().meta
meta.update(
{
"a": self._a,
"loc": self._loc,
"scale": self._scale,
}
)
return meta
def _sample_from_prior(self, n_samples: int) -> np.ndarray:
return self._rng.gamma(shape=self._a, scale=self._scale, size=n_samples)
def _get_log_probability(self, theta: float) -> float:
"""Returns the log pdf of theta."""
if np.ndim(theta) != 0:
raise NotImplementedError()
return sps.gamma.logpdf(theta, a=self._a, scale=self._scale, loc=self._loc)
def _get_gradient(self, theta: float) -> float:
"""Get gradient as computed by Wolfram Alpha."""
if np.ndim(theta) == 0:
# Multiply by theta because of the chain rule...
return ((self._a - 1) / theta - (1 / self._scale)) * theta
else:
raise NotImplementedError()