🧬 Ruck

Performant evolutionary algorithms for Python

Visit the docs for more info, or browse the releases.

Contributions are welcome!

Goals

Ruck aims to fill the following criteria:

1. Provide high quality, readable implementations of algorithms.
2. Be easily extensible and debuggable.
3. Performant while maintaining its simplicity.

Citing Ruck

Please use the following citation if you use Ruck in your research:

@Misc{Michlo2021Ruck,
  author =       {Nathan Juraj Michlo},
  title =        {Ruck - Performant evolutionary algorithms for Python},
  howpublished = {Github},
  year =         {2021},
  url =          {https://github.com/nmichlo/ruck}
}

Overview

Ruck takes inspiration from PyTorch Lightning's module system. The population creation, offspring, evaluation and selection steps are all contained within a single module inheriting from EaModule. While the training logic and components are separated into its own class.

Members of a Population (A list of Members) are intended to be read-only. Modifications should not be made to members, instead new members should be created with the modified values. This enables us to easily implement efficient multi-threading, see below!

The trainer automatically constructs HallOfFame and LogBook objects which keep track of your population and offspring. EaModule provides defaults for get_stats_groups that can be overridden if you wish to customize the tracked statistics.

Minimal OneMax Example

import random
import numpy as np
from ruck import *


class OneMaxMinimalModule(EaModule):
    """
    Minimal onemax example
    - The goal is to flip all the bits of a boolean array to True
    - Offspring are generated as bit flipped versions of the previous population
    - Selection tournament is performed between the previous population and the offspring
    """

    # evaluate unevaluated members according to their values
    def evaluate_values(self, values):
        return [v.sum() for v in values]

    # generate 300 random members of size 100 with 50% bits flipped
    def gen_starting_values(self):
        return [np.random.random(100) < 0.5 for _ in range(300)]

    # randomly flip 5% of the bits of each each member in the population
    # the previous population members should never be modified
    def generate_offspring(self, population):
        return [Member(m.value ^ (np.random.random(m.value.shape) < 0.05)) for m in population]

    # selection tournament between population and offspring
    def select_population(self, population, offspring):
        combined = population + offspring
        return [max(random.sample(combined, k=3), key=lambda m: m.fitness) for _ in range(len(population))]


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

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

Advanced OneMax Example

Ruck provides various helper functions and implementations of evolutionary algorithms for convenience. The following example makes use of these additional features so that components and behaviour can easily be swapped out.

The three basic evolutionary algorithms provided are based around deap's default algorithms from deap.algorithms: eaSimple, eaMuPlusLambda, and eaMuCommaLambda. These algorithms can be accessed from ruck.functional which has the alias R: R.factory_simple_ea, R.factory_mu_plus_lambda and R.factory_mu_comma_lambda.

"""
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,
        member_size: int = 100,
        p_mate: float = 0.5,
        p_mutate: float = 0.5,
    ):
        # 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.factory_simple_ea(
            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])

Multithreading OneMax Example (Ray)

If we need to scale up the computational requirements, for example requiring increased member and population sizes, the above serial implementations will soon run into performance problems.

The basic Ruck implementations of various evolutionary algorithms are designed around a map function that can be swapped out to add multi-threading support. We can easily do this using ray and we even provide various helper functions that enhance ray support.

1. We begin by placing member's values into shared memory using ray's read-only object store and the ray.put function. These ObjectRef's values point to the original np.ndarray values. When retrieved with ray.get they obtain the original arrays using an efficient zero-copy procedure. This is advantageous over something like Python's multiprocessing module which uses expensive pickle operations to pass data around.

2. The second step is to swap out the aforementioned map function in the previous example to a multiprocessing equivalent. We provide the ray_map function that can be used instead, which automatically wraps functions using ray.remote, and provides additional benefits when using ObjectRefs.

3. Finally we need to update our mate and mutate functions to handle ObjectRefs, we provide a convenient wrapper to automatically call ray.get on function arguments and ray.out on function results so that you can re-use your existing code.

"""
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
import ray
from ruck import *
from ruck.util import *


class OneMaxRayModule(EaModule):

    def __init__(
        self,
        population_size: int = 300,
        member_size: int = 100,
        p_mate: float = 0.5,
        p_mutate: float = 0.5,
    ):
        self.save_hyperparameters()
        # implement the required functions for `EaModule`
        # - decorate the functions with `ray_refs_wrapper` which
        #   automatically `ray.get` arguments and `ray.put` returned results
        self.generate_offspring, self.select_population = R.factory_simple_ea(
            mate_fn=ray_refs_wrapper(R.mate_crossover_1d, iter_results=True),
            mutate_fn=ray_refs_wrapper(partial(R.mutate_flip_bit_groups, p=0.05)),
            select_fn=partial(R.select_tournament, k=3),  # OK to compute locally, because we only look at the fitness
            p_mate=self.hparams.p_mate,
            p_mutate=self.hparams.p_mutate,
            map_fn=ray_map,  # specify the map function to enable multiprocessing
        )

    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(np.sum, 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=100, progress=True).fit(module)

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