Population: return [ np.random.random(self.hparams.member_size) < 0.5 for i in range(self.hparams.population_size) ] if __name__ == '__main__': # create and train the population module = OneMaxModule(population_size=300, member_size=100) pop, logbook, halloffame = Trainer(generations=40, progress=True).fit(module) print('initial stats:', logbook[0]) print('final stats:', logbook[-1]) print('best member:', halloffame.members[0]) ">

"""
OneMax serial example based on:
https://github.com/DEAP/deap/blob/master/examples/ga/onemax_numpy.py
"""

import functools
import numpy as np
from ruck import *


class OneMaxModule(EaModule):

    def __init__(
        self,
        population_size: int = 300,
        offspring_num: int = None,  # offspring_num (lambda) is automatically set to population_size (mu) when `None`
        member_size: int = 100,
        p_mate: float = 0.5,
        p_mutate: float = 0.5,
        ea_mode: str = 'simple'
    ):
        # save the arguments to the .hparams property. values are taken from the
        # local scope so modifications can be captured if the call to this is delayed.
        self.save_hyperparameters()
        # implement the required functions for `EaModule`
        self.generate_offspring, self.select_population = R.make_ea(
            mode=self.hparams.ea_mode,
            offspring_num=self.hparams.offspring_num,
            mate_fn=R.mate_crossover_1d,
            mutate_fn=functools.partial(R.mutate_flip_bit_groups, p=0.05),
            select_fn=functools.partial(R.select_tournament, k=3),
            p_mate=self.hparams.p_mate,
            p_mutate=self.hparams.p_mutate,
        )

    def evaluate_values(self, values):
        return map(np.sum, values)

    def gen_starting_values(self) -> Population:
        return [
            np.random.random(self.hparams.member_size) < 0.5
            for i in range(self.hparams.population_size)
        ]


if __name__ == '__main__':
    # create and train the population
    module = OneMaxModule(population_size=300, member_size=100)
    pop, logbook, halloffame = Trainer(generations=40, progress=True).fit(module)

    print('initial stats:', logbook[0])
    print('final stats:', logbook[-1])
    print('best member:', halloffame.members[0])

"""
OneMax parallel example using ray's object store.

8 bytes * 1_000_000 * 128 members ~= 128 MB of memory to store this population.
This is quite a bit of processing that needs to happen! But using ray
and its object store we can do this efficiently!
"""

from functools import partial
import numpy as np
from ruck import *
from ruck.external.ray import *


class OneMaxRayModule(EaModule):

    def __init__(
        self,
        population_size: int = 300,
        offspring_num: int = None,  # offspring_num (lambda) is automatically set to population_size (mu) when `None`
        member_size: int = 100,
        p_mate: float = 0.5,
        p_mutate: float = 0.5,
        ea_mode: str = 'mu_plus_lambda'
    ):
        self.save_hyperparameters()
        # implement the required functions for `EaModule`
        self.generate_offspring, self.select_population = R.make_ea(
            mode=self.hparams.ea_mode,
            offspring_num=self.hparams.offspring_num,
            # decorate the functions with `ray_remote_put` which automatically
            # `ray.get` arguments that are `ObjectRef`s and `ray.put`s returned results
            mate_fn=ray_remote_puts(R.mate_crossover_1d).remote,
            mutate_fn=ray_remote_put(R.mutate_flip_bit_groups).remote,
            # efficient to compute locally
            select_fn=partial(R.select_tournament, k=3),
            p_mate=self.hparams.p_mate,
            p_mutate=self.hparams.p_mutate,
            # ENABLE multiprocessing
            map_fn=ray_map,
        )
        # eval function, we need to cache it on the class to prevent
        # multiple calls to ray.remote. We use ray.remote instead of
        # ray_remote_put like above because we want the returned values
        # not object refs to those values.
        self._ray_eval = ray.remote(np.mean).remote

    def evaluate_values(self, values):
        # values is a list of `ray.ObjectRef`s not `np.ndarray`s
        # ray_map automatically converts np.sum to a `ray.remote` function which
        # automatically handles `ray.get`ing of `ray.ObjectRef`s passed as arguments
        return ray_map(self._ray_eval, values)

    def gen_starting_values(self):
        # generate objects and place in ray's object store
        return [
            ray.put(np.random.random(self.hparams.member_size) < 0.5)
            for i in range(self.hparams.population_size)
        ]


if __name__ == '__main__':
    # initialize ray to use the specified system resources
    ray.init()

    # create and train the population
    module = OneMaxRayModule(population_size=128, member_size=1_000_000)
    pop, logbook, halloffame = Trainer(generations=200, progress=True).fit(module)

    print('initial stats:', logbook[0])
    print('final stats:', logbook[-1])
    print('best member:', halloffame.members[0])