from __future__ import annotations
from typing import Any
import numpy as np
from smac.random_design.abstract_random_design import AbstractRandomDesign
from smac.utils.logging import get_logger
__copyright__ = "Copyright 2022, automl.org"
__license__ = "3-clause BSD"
logger = get_logger(__name__)
[docs]class CosineAnnealingRandomDesign(AbstractRandomDesign):
"""Interleaves a random configuration according to a given probability which is decreased
according to a cosine annealing schedule.
Parameters
----------
max_probability : float
Initial (maximum) probability of a random configuration.
min_probability : float
Final (minimal) probability of a random configuration used in iteration `restart_iteration`.
restart_iteration : int
Restart the annealing schedule every `restart_iteration` iterations.
seed : int
Integer used to initialize random state.
"""
def __init__(self, min_probability: float, max_probability: float, restart_iteration: int, seed: int = 0):
super().__init__(seed)
assert 0 <= min_probability <= 1
assert 0 <= max_probability <= 1
assert max_probability > min_probability
assert restart_iteration > 2
self._max_probability = max_probability
self._min_probability = min_probability
# Internally, iteration indices start at 0, so we need to decrease this
self._restart_iteration = restart_iteration - 1
self._iteration = 0
self._probability = max_probability
@property
def meta(self) -> dict[str, Any]: # noqa: D102
meta = super().meta
meta.update(
{
"max_probability": self._max_probability,
"min_probability": self._min_probability,
"restart_iteration": self._restart_iteration,
}
)
return meta
[docs] def next_iteration(self) -> None: # noqa: D102
"""Moves to the next iteration and set ``self._probability``."""
self._iteration += 1
if self._iteration > self._restart_iteration:
self._iteration = 0
logger.debug("Perform a restart.")
self._probability = self._min_probability + (
0.5
* (self._max_probability - self._min_probability)
* (1 + np.cos(self._iteration * np.pi / self._restart_iteration))
)
logger.debug(f"Probability for random configs: {self._probability}")
[docs] def check(self, iteration: int) -> bool: # noqa: D102
assert iteration >= 0
if self._rng.rand() <= self._probability:
return True
else:
return False