Hello World! genetic algorithm in python

genetic.py

# File: genetic.py
#    from chapter 1 of _Genetic Algorithms with Python_, an ebook
#    available for purchase at http://leanpub.com/genetic_algorithms_with_python
#
# Author: Clinton Sheppard <[email protected]>
# Repository: https://drive.google.com/open?id=0B2tHXnhOFnVkRU95SC12alNkU2M
# Copyright (c) 2016 Clinton Sheppard
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.  See the License for the specific language governing
# permissions and limitations under the License.

import random
import statistics
import time
import sys


def _generate_parent(length, geneSet, get_fitness):
    genes = []
    while len(genes) < length:
        sampleSize = min(length - len(genes), len(geneSet))
        genes.extend(random.sample(geneSet, sampleSize))
    genes = ''.join(genes)
    fitness = get_fitness(genes)
    return Chromosome(genes, fitness)


def _mutate(parent, geneSet, get_fitness):
    index = random.randrange(0, len(parent.Genes))
    childGenes = list(parent.Genes)
    newGene, alternate = random.sample(geneSet, 2)
    childGenes[index] = alternate \
        if newGene == childGenes[index] \
        else newGene
    genes = ''.join(childGenes)
    fitness = get_fitness(genes)
    return Chromosome(genes, fitness)


def get_best(get_fitness, targetLen, optimalFitness, geneSet, display):
    random.seed()
    bestParent = _generate_parent(targetLen, geneSet, get_fitness)
    display(bestParent)
    if bestParent.Fitness >= optimalFitness:
        return bestParent
    while True:
        child = _mutate(bestParent, geneSet, get_fitness)
        if bestParent.Fitness >= child.Fitness:
            continue
        display(child)
        if child.Fitness >= optimalFitness:
            return child
        bestParent = child


class Chromosome:
    Genes = None
    Fitness = None

    def __init__(self, genes, fitness):
        self.Genes = genes
        self.Fitness = fitness


class Benchmark:
    @staticmethod
    def run(function):
        timings = []
        stdout = sys.stdout
        for i in range(100):
            sys.stdout = None
            startTime = time.time()
            function()
            seconds = time.time() - startTime
            sys.stdout = stdout
            timings.append(seconds)
            mean = statistics.mean(timings)
            if i < 10 or i % 10 == 9:
                print("{0} {1:3.2f} {2:3.2f}".format(
                    1 + i, mean,
                    statistics.stdev(timings, mean)
                    if i > 1 else 0))

guessPasswordTests.py

# File: guessPasswordTests.py
#    from chapter 1 of _Genetic Algorithms with Python_, an ebook
#    available for purchase at http://leanpub.com/genetic_algorithms_with_python
#
# Author: Clinton Sheppard <[email protected]>
# Repository: https://drive.google.com/open?id=0B2tHXnhOFnVkRU95SC12alNkU2M
# Copyright (c) 2016 Clinton Sheppard
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.  See the License for the specific language governing
# permissions and limitations under the License.

import datetime
import genetic
import unittest
import random


def get_fitness(guess, target):
    return sum(1 for expected, actual in zip(target, guess)
               if expected == actual)


def display(candidate, startTime):
    timeDiff = datetime.datetime.now() - startTime
    print("{0}\t{1}\t{2}".format(
        candidate.Genes, candidate.Fitness, str(timeDiff)))


class GuessPasswordTests(unittest.TestCase):
    geneset = " abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!.,"

    def test_Hello_World(self):
        target = "Hello World!"
        self.guess_password(target)

    def test_For_I_am_fearfully_and_wonderfully_made(self):
        target = "For I am fearfully and wonderfully made."
        self.guess_password(target)

    def guess_password(self, target):
        startTime = datetime.datetime.now()

        def fnGetFitness(genes):
            return get_fitness(genes, target)

        def fnDisplay(candidate):
            display(candidate, startTime)

        optimalFitness = len(target)
        best = genetic.get_best(fnGetFitness, len(target),
                                optimalFitness, self.geneset, fnDisplay)
        self.assertEqual(best.Genes, target)

    def test_Random(self):
        length = 150
        target = ''.join(random.choice(self.geneset) for _ in
                         range(length))

        self.guess_password(target)

    def test_benchmark(self):
        genetic.Benchmark.run(lambda: self.test_Random())


if __name__ == '__main__':
    unittest.main()