def execute(self, output_path: str = ''):
self.algorithm.run()
if output_path:
file_name = os.path.join(output_path, 'FUN.{}.tsv'.format(self.run_tag))
print_function_values_to_file(self.algorithm.get_result(), filename=file_name)
file_name = os.path.join(output_path, 'VAR.{}.tsv'.format(self.run_tag))
print_variables_to_file(self.algorithm.get_result(), filename=file_name)
file_name = os.path.join(output_path, 'TIME.{}'.format(self.run_tag))
with open(file_name, 'w+') as of:
of.write(str(self.algorithm.total_computing_time))
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:
print_function_values_to_file(solutions, '{}/FUN.{}'.format(self.directory, self.counter))
self.counter += 1
else:
print_function_values_to_file(solutions, '{}/FUN.{}'.format(self.directory, self.counter))
self.counter += 1
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
leaders=CrowdingDistanceArchive(100),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
algorithm.run()
front = algorithm.get_result()
label = algorithm.get_name() + "." + problem.get_name()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation', reference_front=problem.reference_front,
axis_labels=problem.obj_labels)
plot_front.plot(front, label=algorithm.label, filename=algorithm.get_name())
# 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=algorithm.label, filename=algorithm.get_name())
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.' + algorithm.label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
if __name__ == '__main__':
problem = Rastrigin(10)
max_evaluations = 50000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
crossover=SBXCrossover(probability=0.9, distribution_index=20.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
dominance_comparator=DominanceComparator())
algorithm.observable.register(observer=PrintObjectivesObserver(1000))
algorithm.run()
front = algorithm.get_result()
# 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())
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.util.observer import PrintObjectivesObserver
from jmetal.util.solutions import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = OneMax(number_of_bits=1024)
max_evaluations = 10000
algorithm = LocalSearch(
problem=problem,
mutation=BitFlipMutation(probability=1.0 / problem.number_of_bits),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
objectives_observer = PrintObjectivesObserver(frequency=100)
algorithm.observable.register(observer=objectives_observer)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(
result, 'FUN.' + algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(
result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm: ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Solution: ' + result.get_binary_string())
print('Fitness: ' + str(result.objectives[0]))
print('Computing time: ' + str(algorithm.total_computing_time))
def train(self):
problem = Ejemplo(X=self.Xtrain,
Y=self.Ytrain,
kernel=self.kernel,
gamma=self.gamma,
degree=self.degree,
C=self.C,
coef0=self.coef0)
#problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
max_evaluations = self.maxEvaluations
algorithm = NSGAII(
problem=problem,
population_size=self.popsize,
offspring_population_size=self.popsize,
mutation=BitFlipMutation(probability=1.0 /
np.shape(self.Xtrain)[0]),
crossover=SPXCrossover(probability=1.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
#algorithm.observable.register(observer=VisualizerObserver(reference_front=problem.reference_front))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation',
reference_front=None,
axis_labels=problem.obj_labels)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation',
axis_labels=problem.obj_labels)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.' + algorithm.label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
# Get normalized matrix of results
normed_matrix = normalize(
list(map(lambda result: result.objectives, front)))
# Get the sum of each objective results and select the best (min)
scores = list(map(lambda item: sum(item), normed_matrix))
solution = front[scores.index(min(scores))]
self.instances = solution.variables[0]
self.attributes = solution.variables[1]
# Generate masks
# Crop by characteristics and instances
X = self.Xtrain[self.instances, :]
X = X[:, self.attributes]
Y = self.Ytrain[self.instances]
self.model = SVC(gamma=self.gamma,
C=self.C,
degree=self.degree,
kernel=self.kernel)
self.model.fit(X=X, y=Y)
# write your code here
return self.model
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.util.solutions import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = ZDT1Modified()
max_evaluations = 100
algorithm = NSGAII(
problem=problem,
population_size=10,
offspring_population_size=10,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
def train(self):
problem = SVM_Problem(X=self.Xtrain, Y=self.Ytrain)
#problem.reference_front = read_solutions(filename='resources/reference_front/ZDT1.pf')
max_evaluations = self.maxEvaluations
algorithm = NSGAII(
problem=problem,
population_size=self.popsize,
offspring_population_size=self.popsize,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
#algorithm.observable.register(observer=VisualizerObserver(reference_front=problem.reference_front))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(plot_title='Pareto front approximation',
reference_front=None,
axis_labels=problem.obj_labels)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Plot interactive front
plot_front = InteractivePlot(plot_title='Pareto front approximation',
axis_labels=problem.obj_labels)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
print_variables_to_file(front, 'VAR.' + algorithm.label)
print('Algorithm (continuous problem): ' + algorithm.get_name())
print(
"-----------------------------------------------------------------------------"
)
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
# Get normalized matrix of results
normed_matrix = normalize(
list(map(lambda result: result.objectives, front)))
# Get the sum of each objective results and select the best (min)
scores = list(map(lambda item: sum(item), normed_matrix))
solution = front[scores.index(min(scores))]
# Get our variables
self.gamma = solution.variables[0]
self.C = solution.variables[1]
self.coef0 = solution.variables[2]
self.degree = solution.variables[3]
self.kernel = solution.variables[4]
self.instances = solution.masks[0]
self.attributes = solution.masks[1]
# Select pick a random array with length of the variable
X = self.Xtrain[self.instances, :]
X = X[:, self.attributes]
Y = self.Ytrain[self.instances]
print(*front, sep=", ")
# Contruct model
self.model = SVM(Xtrain=X,
Ytrain=Y,
kernel=self.kernel,
C=self.C,
degree=self.degree,
coef0=self.coef0,
gamma=self.gamma,
seed=self.seed).train()
print('Objectives: ', *solution.objectives, sep=", ")
# write your code here
return self.model