class PlotFrontToFileObserver(Observer):
def __init__(self, output_directory: str) -> None:
self.directory = output_directory
self.plot_front = Plot(plot_title='Pareto front approximation')
self.last_front = []
self.fronts = []
self.counter = 0
if Path(self.directory).is_dir():
LOGGER.warning('Directory {} exists. Removing contents.'.format(
self.directory))
for file in os.listdir(self.directory):
os.remove('{0}/{1}'.format(self.directory, file))
else:
LOGGER.warning('Directory {} does not exist. Creating it.'.format(
self.directory))
Path(self.directory).mkdir(parents=True)
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 __init__(self, output_directory: str) -> None:
self.directory = output_directory
self.plot_front = Plot(plot_title='Pareto front approximation')
self.last_front = []
self.fronts = []
self.counter = 0
if Path(self.directory).is_dir():
LOGGER.warning('Directory {} exists. Removing contents.'.format(
self.directory))
for file in os.listdir(self.directory):
os.remove('{0}/{1}'.format(self.directory, file))
else:
LOGGER.warning('Directory {} does not exist. Creating it.'.format(
self.directory))
Path(self.directory).mkdir(parents=True)
comparator=RankingAndCrowdingDistanceComparator()),
dominance_comparator=GDominanceComparator(reference_point),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(
observer=VisualizerObserver(reference_front=problem.reference_front,
reference_point=(reference_point)))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation',
axis_labels=problem.obj_labels,
reference_point=reference_point,
reference_front=problem.reference_front)
plot_front.plot(front, label='gNSGAII-ZDT1', filename='gNSGAII-ZDT1')
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation',
axis_labels=problem.obj_labels,
reference_point=reference_point,
reference_front=problem.reference_front)
plot_front.plot(front, label='gNSGAII-ZDT1', filename='gNSGAII-ZDT1')
# Save results to file
print_function_values_to_file(
front, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
front, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
comparator=RankingAndCrowdingDistanceComparator()),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
dominance_comparator=DominanceComparator())
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(
reference_front=problem.reference_front))
algorithm.run()
front = algorithm.get_result()
label = algorithm.get_name() + "." + problem.get_name()
algorithm_name = label
# Plot front
plot_front = Plot(plot_title='Pareto front approximation',
axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=algorithm_name)
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation',
axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=algorithm_name)
# Save results to file
print_function_values_to_file(front, 'FUN.' + label)
print_variables_to_file(front, 'VAR.' + label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
problem.number_of_variables,
distribution_index=20),
reference_points=reference_point,
leaders=archives_with_reference_points,
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(
observer=VisualizerObserver(reference_front=problem.reference_front,
reference_point=reference_point))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(plot_title='SMPSORP-ZDT1',
reference_front=problem.reference_front,
reference_point=algorithm.reference_points,
axis_labels=problem.obj_labels)
plot_front.plot(algorithm.get_result(), filename='SMPSORP-ZDT1')
# Plot interactive front
plot_front = InteractivePlot(plot_title='SMPSORP-ZDT1',
reference_front=problem.reference_front,
reference_point=algorithm.reference_points,
axis_labels=problem.obj_labels)
plot_front.plot(front, filename='SMPSORP-ZDT1')
# Save results to file
print_function_values_to_file(
front, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
front, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(max=25000))
algorithm.observable.register(observer=ProgressBarObserver(max=25000))
algorithm.run()
front = algorithm.get_result()
# Plot front
label = '{}-{} with {} objectives'.format(algorithm.get_name(),
problem.get_name(),
problem.number_of_objectives)
plot_front = Plot(plot_title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=label)
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation',
reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front, label=label, filename=label, normalize=True)
# Save variables to file
print_function_values_to_file(front, 'FUN-' + label)
print_variables_to_file(front, 'VAR-' + label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())