Source code for orion.algo.random.random

Random sampler as optimization algorithm

Draw and deliver samples from prior defined in problem's domain.

from __future__ import annotations

from typing import Sequence

import numpy

from orion.algo.base import BaseAlgorithm
from import Space

[docs]class Random(BaseAlgorithm): """An algorithm that samples randomly from the problem's space. Parameters ---------- space: `` Optimisation space with priors for each dimension. seed: None, int or sequence of int Seed for the random number generator used to sample new trials. Default: ``None`` """ def __init__(self, space: Space, seed: int | Sequence[int] | None = None): super().__init__(space) self.seed = seed self.seed_rng(seed)
[docs] def seed_rng(self, seed: int | Sequence[int] | None): """Seed the state of the random number generator. :param seed: Integer seed for the random number generator. """ self.rng = numpy.random.RandomState(seed)
@property def state_dict(self): """Return a state dict that can be used to reset the state of the algorithm.""" _state_dict = super().state_dict _state_dict["rng_state"] = self.rng.get_state() return _state_dict
[docs] def set_state(self, state_dict): """Reset the state of the algorithm based on the given state_dict :param state_dict: Dictionary representing state of an algorithm """ super().set_state(state_dict) self.seed_rng(0) self.rng.set_state(state_dict["rng_state"])
[docs] def suggest(self, num): """Suggest a `num` of new sets of parameters. Randomly draw samples from the search space and return them. Parameters ---------- num: int Number of trials to suggest. Returns ------- List of unique trials suggested. """ trials = [] while len(trials) < num and not self.is_done: # NOTE: space.sample() uses (and modifies) the random state here. seed = tuple(self.rng.randint(0, 1000000, size=3)) new_trial =, seed=seed)[0] if not self.has_suggested(new_trial): self.register(new_trial) trials.append(new_trial) return trials