def update(self, *args, **kwargs):
problem = kwargs['PROBLEM']
solutions = kwargs['SOLUTIONS']
if solutions:
if isinstance(problem, DynamicProblem):
termination_criterion_is_met = kwargs.get(
'TERMINATION_CRITERIA_IS_MET', None)
if termination_criterion_is_met:
if self.counter > 0:
igd = InvertedGenerationalDistance(self.last_front)
igd_value = igd.compute(solutions)
else:
igd_value = 1
if igd_value > 0.005:
self.fronts += solutions
self.plot_front.plot([self.fronts],
label=[problem.get_name()],
filename='{}/front-{}'.format(
self.directory, self.counter))
self.counter += 1
self.last_front = solutions
else:
self.plot_front.plot([solutions],
filename='{}/front-{}'.format(
self.directory, self.counter))
self.counter += 1
def replacement(self, population: List[S],
offspring_population: List[S]) -> List[List[S]]:
ranking = StrengthRanking(self.dominance_comparator)
density_estimator = KNearestNeighborDensityEstimator()
r = RankingAndDensityEstimatorReplacement(ranking, density_estimator,
RemovalPolicyType.SEQUENTIAL)
solutions = r.replace(population, offspring_population)
front = get_non_dominated_solutions(solutions)
objective1 = -1 / self.Uobjecive1 * np.array(
[solution.objectives[0] for solution in front])
objective2 = -1 / self.Uobjecive2 * np.array(
[solution.objectives[1] for solution in front])
HV = calculateHypervolume(list(zip(objective1, objective2)))
print('Hypervolume;', HV)
self.hypervolumeByGeneration.append(HV)
# DO IGD calculation here
IGD = InvertedGenerationalDistance(self.reference_front)
igd = IGD.compute(
list(
zip(-np.array([solution.objectives[0] for solution in front]),
-np.array([solution.objectives[1]
for solution in front]))))
print('IGD1:', igd)
# obj1front=1/self.Uobjecive1*self.reference_front[:, 0]
# obj2front=1/self.Uobjecive2*self.reference_front[:,1]
# igd2 = calculateIGD(list(zip(objective1,objective2)), list(zip(obj1front,obj2front)))
# print('IGD2:',igd2)
self.IGDbyGeneration.append(igd)
return solutions
def igd(true_front: NonDominatedSolutionsArchive,
solution_list: List[Solution]) -> float:
# prepare indicator
array_true_front = ResultHandler.to_numpy_array(
true_front.solution_list)
indicator = InvertedGenerationalDistance(array_true_front)
# calculate
solutions = ResultHandler.to_numpy_array(solution_list)
return indicator.compute(solutions)
def test_case1(self):
"""
Case 1. Reference front: [[1.0, 1.0]], front: [[1.0, 1.0]]
Expected result = 0.0
Comment: simplest case
:return:
"""
indicator = InvertedGenerationalDistance(np.array([[1.0, 1.0]]))
front = np.array([[1.0, 1.0]])
result = indicator.compute(front)
self.assertEqual(0.0, result)
def test_case5(self):
"""
Case 5. reference front: [[1.0, 1.0], [2.1, 2.1]], front: [[1.5, 1.5], [2.2, 2.2], [1.9, 1.9]]
Expected result: average of the sum of the distances of the points of the reference front to the front.
Example with three objectives
:return:
"""
indicator = InvertedGenerationalDistance(np.array([[1.0, 1.0], [2.0, 2.0]]))
front = np.array([[1.5, 1.5], [2.2, 2.2], [1.9, 1.9]])
result = indicator.compute(front)
distance_of_first_point = np.sqrt(pow(1.0 - 1.5, 2) + pow(1.0 - 1.5, 2))
distance_of_second_point = np.sqrt(pow(2.0 - 1.9, 2) + pow(2.0 - 1.9, 2))
self.assertEqual((distance_of_first_point + distance_of_second_point) / 2.0, result)
def test_case2(self):
"""
Case 2. Reference front: [[1.0, 1.0], [2.0, 2.0], front: [[1.0, 1.0]]
Expected result: average of the sum of the distances of the points of the reference front to the front
:return:
"""
indicator = InvertedGenerationalDistance(np.array([[1.0, 1.0], [2.0, 2.0]]))
front = np.array([[1.0, 1.0]])
result = indicator.compute(front)
distance_of_first_point = np.sqrt(pow(1.0 - 1.0, 2) + pow(1.0 - 1.0, 2))
distance_of_second_point = np.sqrt(pow(2.0 - 1.0, 2) + pow(2.0 - 1.0, 2))
self.assertEqual((distance_of_first_point + distance_of_second_point) / 2.0, result)
def update(self, *args, **kwargs):
problem = kwargs["PROBLEM"]
solutions = kwargs["SOLUTIONS"]
evaluations = kwargs["EVALUATIONS"]
if solutions:
if (evaluations % self.step) == 0:
if isinstance(problem, DynamicProblem):
termination_criterion_is_met = kwargs.get(
"TERMINATION_CRITERIA_IS_MET", None)
if termination_criterion_is_met:
if self.counter > 0:
igd = InvertedGenerationalDistance(
np.array(
[s.objectives for s in self.last_front]))
igd_value = igd.compute(
np.array([s.objectives for s in solutions]))
else:
igd_value = 1
if igd_value > 0.005:
self.fronts += solutions
self.plot_front.plot(
[self.fronts],
label=problem.get_name(),
filename=
f"{self.directory}/front-{evaluations}",
)
self.counter += 1
self.last_front = solutions
else:
self.plot_front.plot(
[solutions],
label=f"{evaluations} evaluations",
filename=f"{self.directory}/front-{evaluations}",
)
self.counter += 1
def test_get_short_name_return_the_right_value(self):
self.assertEqual("IGD", InvertedGenerationalDistance([]).get_short_name())
def test_should_constructor_create_a_non_null_object(self) -> None:
indicator = InvertedGenerationalDistance([])
self.assertIsNotNone(indicator)
from jmetal.core.quality_indicator import GenerationalDistance, InvertedGenerationalDistance, HyperVolume import numpy as np import os file_folder_orgin = os.path.abspath(os.path.join(os.getcwd(), "..")) var_file = file_folder_orgin + "/FUN.NSGAII.OvertakeProblem" solution_file = file_folder_orgin + "/VAR.NSGAII.OvertakeProblem" var = np.loadtxt(var_file) solution = np.loadtxt(solution_file) # print(var, solution) indicator1 = GenerationalDistance(solution) GD = indicator1.compute(solution) print(GD) indicator1 = InvertedGenerationalDistance(solution) GD = indicator1.compute(solution) print(GD)
termination_criterion=StoppingByEvaluationsCustom(max_evaluations=max_evaluations,
reference_point=REFERENCE_POINT,
AlgorithmName='SMPSO')
),
algorithm_tag='SMPSO',
problem_tag=problem_tag,
run=run,
)
)
return jobs
if __name__ == '__main__':
# Configure the experiments
jobs = configure_experiment(problems={'OTN': problemOTN}, n_run=TIMES_TO_RUN)
# Run the study
output_directory = 'data'
experiment = Experiment(output_dir=output_directory, jobs=jobs)
experiment.run()
Filter.RemovePenalty(output_directory)
# Reference fronts is the folder where is the reference to be compared with.
generate_summary_from_experiment(
input_dir=output_directory,
reference_fronts='C:\\Users\\aryss\\Documents\\Repositories\\OTN_Mastering\\Output\\CT3\\8 services',
quality_indicators=[InvertedGenerationalDistance(), EpsilonIndicator(), HyperVolume(REFERENCE_POINT)]
#quality_indicators = [HyperVolume(REFERENCE_POINT)]
)
