Thesis-OptimizationModules/setup.py

from setuptools import setup
import distutils.command.check

class TestCommand(distutils.command.check.check):
    """Custom test command."""

    def run(self):

        # run tests using doctest
        import doctest
        
        # folders
        # from macop.algorithms import Algorithm
        print("==============================")
        print("Run test program...")
        from macop.solutions.BinarySolution import BinarySolution
        from macop.evaluators.EvaluatorExample import evaluatorExample

        from macop.operators.mutators.SimpleMutation import SimpleMutation
        from macop.operators.mutators.SimpleBinaryMutation import SimpleBinaryMutation
        from macop.operators.crossovers.SimpleCrossover import SimpleCrossover
        from macop.operators.crossovers.RandomSplitCrossover import RandomSplitCrossover

        from macop.operators.policies.RandomPolicy import RandomPolicy
        from macop.operators.policies.UCBPolicy import UCBPolicy

        from macop.algorithms.mono.IteratedLocalSearch import IteratedLocalSearch as ILS
        from macop.callbacks.BasicCheckpoint import BasicCheckpoint
        import random

        random.seed(42)

        elements_score = [ random.randint(1, 20) for _ in range(30) ]
        elements_weight = [ random.randint(2, 5) for _ in range(30) ]

        def knapsackWeight(_solution):

            weight_sum = 0
            for index, elem in enumerate(_solution.data):
                weight_sum += elements_weight[index] * elem

            return weight_sum

        # default validator
        def validator(_solution):

            if knapsackWeight(_solution) <= 80:
                return True
            else:
                False

        # define init random solution
        def init():
            return BinarySolution([], 30).random(validator)

        def evaluator(_solution):

            fitness = 0
            for index, elem in enumerate(_solution.data):
                fitness += (elements_score[index] * elem)

            return fitness

        operators = [SimpleBinaryMutation(), SimpleMutation(), SimpleCrossover(), RandomSplitCrossover()]
        policy = UCBPolicy(operators)
        # callback = BasicCheckpoint(_every=5, _filepath=filepath)

        algo = ILS(init, evaluator, operators, policy, validator, _maximise=True)
        
        # add callback into callback list
        # algo.addCallback(callback)
        algo.run(200)

        print("==============================")
        print("Run test using doctest...")

        # pass test using doctest
        distutils.command.check.check.run(self)


setup(
    name='macop',
    version='1.0.0',
    description='Minimalist And Customizable Optimization Package',
    long_description=open('README.md').read(),
    long_description_content_type='text/markdown',
    classifiers=[
        'Development Status :: 4 - Beta',
        'License :: OSI Approved :: MIT License',
        'Programming Language :: Python :: 3.7',
        'Topic :: Scientific/Engineering',
        'Topic :: Utilities'
    ],
    url='https://github.com/prise-3d/macop',
    author='Jérôme BUISINE',
    author_email='jerome.buisine@univ-littoral.fr',
    license='MIT',
    packages=['macop', 
        'macop.algorithms', 
        'macop.algorithms.mono', 
        'macop.algorithms.multi', 
        'macop.callbacks', 
        'macop.evaluators', 
        'macop.operators',  
        'macop.operators.mutators',  
        'macop.operators.crossovers',  
        'macop.operators.policies', 
        'macop.solutions', 
        'macop.utils'],
    install_requires=[
        'numpy',
    ],
    cmdclass={
        'test': TestCommand,
    },
    zip_safe=False)