# -*- coding: utf-8 -*-
"""
Random sampler as optimization algorithm
========================================
Draw and deliver samples from prior defined in problem's domain.
"""
import numpy
from orion.algo.base import BaseAlgorithm
[docs]class Random(BaseAlgorithm):
"""An algorithm that samples randomly from the problem's space.
Parameters
----------
space: `orion.algo.space.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, seed=None):
super(Random, self).__init__(space, seed=seed)
[docs] def seed_rng(self, seed):
"""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(Random, self).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(Random, self).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 import space and return them.
:param num: how many sets to be suggested.
.. note:: New parameters must be compliant with the problem's domain
`orion.algo.space.Space`.
"""
points = []
while len(points) < num and not self.is_done:
seed = tuple(self.rng.randint(0, 1000000, size=3))
new_point = self.space.sample(1, seed=seed)[0]
if not self.has_suggested(new_point):
self.register(new_point)
points.append(new_point)
return points