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|
"""Contains utility functions."""
from __future__ import annotations
import json
from os import PathLike
from pathlib import Path
from typing import TYPE_CHECKING
import numpy as np
from bayes_opt.exception import NotUniqueError
if TYPE_CHECKING:
from collections.abc import Iterable
from bayes_opt.bayesian_optimization import BayesianOptimization
def load_logs(
optimizer: BayesianOptimization, logs: str | PathLike[str] | Iterable[str | PathLike[str]]
) -> BayesianOptimization:
"""Load previous ...
Parameters
----------
optimizer : BayesianOptimizer
Optimizer the register the previous observations with.
logs : str or os.PathLike
File to load the logs from.
Returns
-------
The optimizer with the state loaded.
"""
if isinstance(logs, (str, PathLike)):
logs = [logs]
for log in logs:
with Path(log).open("r") as j:
while True:
try:
iteration = next(j)
except StopIteration:
break
iteration = json.loads(iteration)
try:
optimizer.register(
params=iteration["params"],
target=iteration["target"],
constraint_value=(iteration["constraint"] if optimizer.is_constrained else None),
)
except NotUniqueError:
continue
return optimizer
def ensure_rng(random_state: int | np.random.RandomState | None = None) -> np.random.RandomState:
"""Create a random number generator based on an optional seed.
Parameters
----------
random_state : np.random.RandomState or int or None, default=None
Random state to use. if `None`, will create an unseeded random state.
If `int`, creates a state using the argument as seed. If a
`np.random.RandomState` simply returns the argument.
Returns
-------
np.random.RandomState
"""
if random_state is None:
random_state = np.random.RandomState()
elif isinstance(random_state, int):
random_state = np.random.RandomState(random_state)
elif not isinstance(random_state, np.random.RandomState):
error_msg = "random_state should be an instance of np.random.RandomState, an int, or None."
raise TypeError(error_msg)
return random_state
|
