main_jmetal.py

import scenarios

from evaluation import NAPFDMetric
from jmetal.algorithm.singleobjective.genetic_algorithm import GeneticAlgorithm
from jmetal.operator import BinaryTournamentSelection
from jmetal.operator.crossover import PMXCrossover
from jmetal.operator.mutation import PermutationSwapMutation
from jmetal.util.observer import PrintObjectivesObserver
from jmetal.util.density_estimator import CrowdingDistance
from jmetal.util.observer import PrintObjectivesObserver
from jmetal.util.ranking import FastNonDominatedRanking
from jmetal.util.solutions.comparator import MultiComparator
from jmetal.util.termination_criterion import StoppingByEvaluations
from problem import TCPCI

if __name__ == "__main__":
    metric = NAPFDMetric()

    repo_path = "data"
    dataset = 'deeplearning4j@deeplearning4j'

    scenario_provider = scenarios.IndustrialDatasetScenarioProvider(f"{repo_path}/{dataset}/features-engineered.csv")

    for (t, vsc) in enumerate(scenario_provider, start=1):
        if(t > 1):
            break

        available_time = vsc.get_available_time()
        metric.update_available_time(available_time)

        test_cases = vsc.get_testcases()
        IND_SIZE = len(test_cases)

        if (IND_SIZE > 1):
            # Run GA to find the best NAPFD in current commit
            problem = TCPCI(metric=metric, test_cases=test_cases,
                            number_of_variables=IND_SIZE)

            algorithm = GeneticAlgorithm(
                problem=problem,
                population_size=300,
                offspring_population_size=300,
                mutation=PermutationSwapMutation(0.01),
                crossover=PMXCrossover(0.9),
                selection=BinaryTournamentSelection(),
                termination_criterion=StoppingByEvaluations(max=60000)
            )

            # algorithm.observable.register(observer=PrintObjectivesObserver(1000))

            algorithm.run()
            result = algorithm.get_result()

            print(f"Commit: {t} - Fitness: {result.objectives[0]*-1} - Computing time: {algorithm.total_computing_time}")
        else:
            print(f"Commit: {t} - Fitness: {1}")
        # print('Algorithm: ' + algorithm.get_name())
        # print('Problem: ' + problem.get_name())
        # print('Solution: ' + str(result.variables[0]))
        # print('Fitness:  ' + str(result.objectives[0]))
        print()