jMetalPy: Python version of the jMetal framework¶
Warning
Documentation is WIP!! Some information may be missing.
Getting started¶
Examples of running EAs¶
<<<<<<< HEAD NSGA-II with plotting ======= NSGA-II >>>>>>> 52e0b172f0c6d651ba08b961a90a382f0a4b8e0f ————————————
- <<<<<<< HEAD
from jmetal.algorithm.multiobjective.nsgaii import NSGAII from jmetal.component import RankingAndCrowdingDistanceComparator from jmetal.operator import SBX, Polynomial, BinaryTournamentSelection from jmetal.problem import ZDT1, DTLZ1 from jmetal.util.graphic import FrontPlot from jmetal.util.solution_list import print_function_values_to_file, print_variables_to_file, read_front
problem = ZDT1() # DTLZ1() problem.reference_front = read_front(file_path=’/resources/reference_front/{}.pf’.format(problem.get_name()))
>>>>>>> 52e0b172f0c6d651ba08b961a90a382f0a4b8e0f
max_evaluations = 25000 algorithm = NSGAII(
problem=problem, population_size=100,
- <<<<<<< HEAD
- offspring_size=100, mating_pool_size=100, max_evaluations=25000, mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20), crossover=SBX(probability=1.0, distribution_index=20), selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
crossover=SBXCrossover(probability=1.0, distribution_index=20), selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator()), termination_criterion=StoppingByEvaluations(max=max_evaluations), dominance_comparator=DominanceComparator()
- >>>>>>> 52e0b172f0c6d651ba08b961a90a382f0a4b8e0f
)
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
algorithm.run() front = algorithm.get_result()
- <<<<<<< HEAD
pareto_front = FrontPlot(plot_title=’NSGAII’, axis_labels=problem.obj_labels) pareto_front.plot(front, reference_front=problem.reference_front) pareto_front.to_html(filename=’NSGAII’)
print_function_values_to_file(front, ‘FUN.NSGAII’) print_variables_to_file(front, ‘VAR.NSGAII’)
MOEA/D¶
<<<<<<< HEAD .. raw:: html
<script src=”https://cdn.plot.ly/plotly-latest.min.js”></script> <script>
- var waitForPlotly = setInterval( function() {
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- MathJax.Hub.Config({ SVG: { font: “STIX-Web” }, displayAlign: “center” }); MathJax.Hub.Queue([“setRenderer”, MathJax.Hub, “SVG”]); clearInterval(waitForPlotly);
}}, 250 );
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MOEA/D¶
from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.operator import Polynomial, DifferentialEvolution
from jmetal.problem import LZ09_F2
from jmetal.util.aggregative_function import Chebyshev
from jmetal.util.neighborhood import WeightVectorNeighborhood
from jmetal.util.solution_list import read_front
problem = LZ09_F2()
problem.reference_front = read_front(file_path='/resources/reference_front/{}.pf'.format(problem.get_name()))
population_size = 100
algorithm = MOEAD(
problem=problem,
population_size=population_size,
max_evaluations=150000,
crossover=DifferentialEvolution(CR=1.0, F=0.5, K=0.5),
mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20),
aggregative_function=Chebyshev(dimension=problem.number_of_objectives),
neighbourhood=WeightVectorNeighborhood(population_size, 20, weights_path='/resources/MOEAD_weights/'),
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2
)
progress_bar = ProgressBarObserver(initial=algorithm.population_size, step=algorithm.offspring_size, maximum=algorithm.max_evaluations)
algorithm.observable.register(observer=progress_bar)
problem = LZ09_F2() problem.reference_front = read_solutions(filename=’/resources/reference_front/{}.pf’.format(problem.get_name()))
max_evaluations = 150000
- algorithm = MOEAD(
- problem=problem, population_size=300, crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5), mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20), aggregative_function=Tschebycheff(dimension=problem.number_of_objectives), neighbor_size=20, neighbourhood_selection_probability=0.9, max_number_of_replaced_solutions=2, weight_files_path=’../../resources/MOEAD_weights’, termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
>>>>>>> 52e0b172f0c6d651ba08b961a90a382f0a4b8e0f
algorithm.run() front = algorithm.get_result()
Examples of running PSOs¶
SMPSO with standard settings¶
- <<<<<<< HEAD
from jmetal.algorithm.multiobjective.smpso import SMPSO from jmetal.component import ProgressBarObserver, CrowdingDistanceArchive from jmetal.operator import Polynomial from jmetal.problem import DTLZ1 from jmetal.util.graphic import FrontPlot
problem = DTLZ1(number_of_objectives=5)
- algorithm = SMPSO(
- problem=problem, swarm_size=100, max_evaluations=25000, mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20), leaders=CrowdingDistanceArchive(100)
)
algorithm.run() front = algorithm.get_result()
pareto_front = FrontPlot(plot_title=’SMPSO-DTLZ1-5’, axis_labels=problem.obj_labels) pareto_front.plot(front, reference_front=problem.reference_front) pareto_front.to_html(filename=’SMPSO-DTLZ1-5’)
max_evaluations = 25000 algorithm = SMPSO(
problem=problem, swarm_size=100, mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20), leaders=CrowdingDistanceArchive(100), termination_criterion=StoppingByEvaluations(max=max_evaluations))
>>>>>>> 52e0b172f0c6d651ba08b961a90a382f0a4b8e0f
algorithm.observable.register(observer=ProgressBarObserver(max=max_evaluations))
algorithm.run() front = algorithm.get_result()
SMPSO/RP with standard settings¶
- <<<<<<< HEAD
from jmetal.algorithm.multiobjective.smpso import SMPSORP from jmetal.component import CrowdingDistanceArchiveWithReferencePoint from jmetal.operator import Polynomial from jmetal.problem import ZDT1 from jmetal.util.graphic import FrontPlot from jmetal.util.solution_list import read_front
- def points_to_solutions(points):
solutions = [] for i, _ in enumerate(points):
point = problem.create_solution() point.objectives = points[i] solutions.append(point)return solutions
problem = ZDT1() problem.reference_front = read_front(file_path=’/resources/reference_front/{}.pf’.format(problem.get_name()))
swarm_size = 100
reference_points = [[0.5, 0.5], [0.2, 0.8]] archives_with_reference_points = []
- for point in reference_points:
- archives_with_reference_points.append(
- CrowdingDistanceArchiveWithReferencePoint(swarm_size, point)
)
- algorithm = SMPSORP(
- problem=problem, swarm_size=swarm_size, max_evaluations=25000, mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20), reference_points=reference_points, leaders=archives_with_reference_points
)
algorithm.run() front = algorithm.get_result()
pareto_front = FrontPlot(plot_title=’SMPSORP-ZDT1’, axis_labels=problem.obj_labels) pareto_front.plot(front, reference_front=problem.reference_front) pareto_front.update(points_to_solutions(reference_points), legend=’reference points’) pareto_front.to_html(filename=’SMPSORP-ZDT1’)
swarm_size = 100
reference_point = [[0.1, 0.8],[0.8, 0.2]] archives_with_reference_points = []
- for point in reference_point:
- archives_with_reference_points.append(
- CrowdingDistanceArchiveWithReferencePoint(int(swarm_size / len(reference_point)), point)
)
max_evaluations = 50000 algorithm = SMPSORP(
problem=problem, swarm_size=swarm_size, mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20), reference_points=reference_point, leaders=archives_with_reference_points, termination_criterion=StoppingByEvaluations(max=max_evaluations))
>>>>>>> 52e0b172f0c6d651ba08b961a90a382f0a4b8e0f
algorithm.observable.register(observer=VisualizerObserver(reference_front=problem.reference_front, reference_point=reference_point))
algorithm.run() front = algorithm.get_result()
Observer entities¶
It is possible to attach any number of observers to any algorithm to get information from each iteration. For example, a basic algorithm observer will print the number of evaluations, the objectives from the best individual in the population and the computing time:
basic = BasicAlgorithmObserver(frequency=1.0)
algorithm.observable.register(observer=basic)
A progress bar observer will print a smart progress meter that increases, on each iteration, a fixed value (step) until the maximum is reached.
max_evaluations = 25000
algorithm = GeneticAlgorithm(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=BitFlipMutation(1.0 / problem.number_of_bits),
crossover=SPXCrossover(0.9),
selection=BinaryTournamentSelection(),
termination_criterion=StoppingByEvaluations(max=25000)
)
progress_bar = ProgressBarObserver(max=max_evaluations)
algorithm.observable.register(progress_bar)
algorithm.run()
$ Progress: 50%|##### | 12500/25000 [13:59<14:12, 14.66it/s]
A full list of all available observers can be found at the jmetal.util.observer module.
Defining new problems¶
To include a problem in jMetalPy, it must implement the Problem interface from the jmetal.core.problem module.
Use case: Subset Sum¶
The goal is to find a subset S of W (list of non-negative integers) whose elements sum is closest to (without exceeding) C. For example, for the input \(W=\{3, 34, 4, 12, 5, 2\}\) and \(C=9\), one output could be \(S=\{4, 5\}\) (as it is a subset with sum 9).
In jMetalPy, this problem can be encoded as a binary problem with one objective and one variable:
class SubsetSum(BinaryProblem):
def __init__(self, C: int, W: list):
super(SubsetSum, self).__init__(reference_front=None)
self.C = C
self.W = W
self.number_of_bits = len(self.W)
self.number_of_objectives = 1
self.number_of_variables = 1
self.number_of_constraints = 0
self.obj_directions = [self.MAXIMIZE]
self.obj_labels = ['Sum']
def evaluate(self, solution: BinarySolution) -> BinarySolution:
pass
def create_solution(self) -> BinarySolution:
pass
def get_name(self) -> str:
return 'Subset Sum'
Each solution consists of one objective function to be maximized to be as close as possible to \(C\):
In the former example, one solution could be created and evaluated as follows:
Note
jMetalPy assumes minimization by default. Therefore, we will have to multiply the solution objective by \(-1.0\).
def evaluate(self, solution: BinarySolution) -> BinarySolution:
total_sum = 0.0
for index, bits in enumerate(solution.variables[0]):
if bits:
total_sum += self.W[index]
if total_sum > self.C:
total_sum = self.C - total_sum * 0.1
if total_sum < 0.0:
total_sum = 0.0
solution.objectives[0] = -1.0 * total_sum
return solution
def create_solution(self) -> BinarySolution:
new_solution = BinarySolution(number_of_variables=1, number_of_objectives=1)
new_solution.variables[0] = \
[True if random.randint(0, 1) == 0 else False for _ in range(self.number_of_bits)]
return new_solution
Running experiments¶
This is an example of an experimental study based on solving two problems of the ZDT family with two versions of the same algorithm (NSGA-II). The hypervolume indicator is used for performance assessment.
from jmetal.util.laboratory import Experiment, Job
def configure_experiment(problems: list, n_run: int):
jobs = []
for run in range(n_run):
for problem in problems:
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_size=100,
mating_pool_size=100,
offspring_size=100,
max_evaluations=5000,
mutation=NullMutation(),
crossover=SBX(probability=1.0, distribution_index=20),
selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
),
label='NSGAII with Null Mutation',
run=run,
)
)
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_size=100,
mating_pool_size=100,
offspring_size=100,
max_evaluations=5000,
mutation=Polynomial(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBX(probability=1.0, distribution_index=20),
selection=BinaryTournamentSelection(comparator=RankingAndCrowdingDistanceComparator())
),
label='NSGAII with Polynomial Mutation',
run=run
)
)
return jobs
if __name__ == '__main__':
jobs = configure_experiment(problems=[ZDT1(), ZDT2()], n_run=3)
test = Experiment(
base_directory='./experiment',
jobs=jobs
)
test.run()
metrics = [HyperVolume(reference_point=[1, 1]), ComputingTime()]
data = test.compute_metrics(metrics)
print(data)
| NSGAII with Null Mutation | NSGAII with Polynomial Mutation | |||
|---|---|---|---|---|
| Problem | Metric | Run | ||
| ZDT1 | Hypervolume | 0 | 0.315708 | 0.516258 |
| 1 | 0.323271 | 0.491973 | ||
| 2 | 0.414953 | 0.507568 | ||
| ZDT2 | Hypervolume | 0 | 0.293225 | 0.504027 |
| 1 | 0.280499 | 0.417048 | ||
| 2 | 0.357358 | 0.489576 |
The return value of compute_metrics() is a pandas DataFrame, so we can use all the methods that pandas makes available for us:
mean_results = data.groupby(['problem', 'metric']).mean()
median_results = data.groupby(['problem', 'metric']).median()
only_one_metric = data.xs('Hypervolume', level='metric')
min_values = data.groupby(['problem', 'metric']).min()
max_values = data.groupby(['problem', 'metric']).max()
Front visualization¶
The jmetal.util.visualization submodule contains several classes useful for plotting solutions. jMetalPy includes three types of visualization charts: static, interactive and streaming.
Static plots¶
It is possible to visualize the final front approximation by means of the
Plotclass:from jmetal.util.visualization import Plot front = algorithm.get_result() plot_front = Plot(plot_title='Pareto front approximation', axis_labels=['x', 'y'], reference_front=None) plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')
Note
Static charts can be shown in the screen or stored in a file by setting the filename.
For problems with two and three objectives, the output is a scatter plot; for problem with more than three objectives, a parallel coordinates plot is used. Note that any arbitrary number of fronts can be plotted for comparison purposes:
plot_front = Plot(plot_title='Title', axis_labels=['x', 'y'], reference_front=reference_front) plot_front.plot([front1, front2], label=['ProblemA', 'ProblemB'], filename='output', format='eps')
Interactive plots¶
This kind of plots are interactive in such a way that every solution can be manipulated (e.g., actions such as zoom, selecting part of the graph, or clicking in a point to see its objective values are allowed).
plot_front = InteractivePlot(plot_title='Pareto front approximation') plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1-interactive')
<<<<<<< HEAD .. raw:: html =======
>>>>>>> 52e0b172f0c6d651ba08b961a90a382f0a4b8e0f
<script src=”https://cdn.plot.ly/plotly-latest.min.js”></script> <script>
- var waitForPlotly = setInterval( function() {
- if( typeof(window.Plotly) !== “undefined” ){
- MathJax.Hub.Config({ SVG: { font: “STIX-Web” }, displayAlign: “center” }); MathJax.Hub.Queue([“setRenderer”, MathJax.Hub, “SVG”]); clearInterval(waitForPlotly);
}}, 250 );
</script> <div id=”26e6eceb-0c75-4e0a-9568-e8aee77e00d1” style=”height: 800px; width: 100%;” class=”plotly-graph-div”></div><script type=”text/javascript”>window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=”https://plot.ly”;Plotly.newPlot(“26e6eceb-0c75-4e0a-9568-e8aee77e00d1”, [{“customdata”: [“Solution(objectives=[2.0700608508968706e-09, 1.001321515526852],variables=[2.0700608508968706e-09, 4.6109714708518756e-05, 0.00020920243573761596, 0.00033107571321909203, 0.0006739304772857598, 0.0002037673264636186, 0.00040994089694614547, 0.00026932729765008403, 0.00014676302746086274, 0.00010528166934354055, 3.068348308513757e-06, 3.2101274099769395e-05, 0.00024814676585220374, 0.0002149941762075906, 0.00010733806967890155, 0.00015677553574816217, 3.04516980807362e-05, 5.5619017784062374e-05, 0.0003276635228635259, 3.696130406711847e-05, 1.589476809256459e-05, 0.0001541844015021343, 3.1595840115902584e-05, 0.00017755806260125903, 0.0002651468474361999, 7.853526821373493e-05, 4.393160550952412e-06, 1.995666638562581e-05, 4.77426648453947e-05, 1.3953329130440113e-06])”, “Solution(objectives=[0.9999892724735988, 0.000695572379081093],variables=[0.9999892724735988, 0.00018133769516537874, 0.00019677875206945347, 0.00037663744624519773, 0.00045420752250407374, 2.9519515549840272e-05, 2.7566473362377602e-05, 3.707685206381589e-05, 8.864103011429426e-05, 8.828944846137586e-05, 1.5662879975144696e-05, 0.0004947387950817613, 0.00033123921905088815, 3.400569706688437e-05, 9.098611893656417e-06, 0.00018516835514333018, 1.419113118901809e-05, 0.00036008991850961045, 0.0003283067214581865, 4.6872464189760924e-05, 1.195945956105711e-05, 0.00038568083236773936, 2.758780478277208e-05, 0.00015012425126431503, 7.971645820215275e-05, 0.00011214350946407974, 1.0420648050990161e-05, 0.00012808255010793757, 0.00016072164606925155, 8.058856091025185e-05])”, “Solution(objectives=[0.04416844469510328, 0.7913781712185283],variables=[0.04416844469510328, 4.885263076605893e-05, 0.0005241903290653543, 0.00019331197211171675, 0.0004874086096406119, 0.00020379218702785572, 0.0009586288800166286, 5.5821499640719646e-05, 0.00011107833980644895, 0.00010418841427023534, 4.883430878498374e-05, 5.103378175480106e-06, 0.00026665197308100956, 4.1385391194593847e-05, 0.00010681527541802895, 0.00018571011493655578, 2.6752892520281752e-05, 8.464773170526504e-05, 0.00032144265123671756, 4.680029023594579e-05, 0.00019076487613610776, 0.00011947457096800851, 9.562568072169775e-05, 0.0001240173575635529, 2.8741562667180035e-05, 0.0005253938687332385, 0.000508074632313297, 5.7177620848686685e-06, 4.771226917620063e-05, 8.15256811484487e-05])”, “Solution(objectives=[0.07135560931631874, 0.7341797666395559],variables=[0.07135560931631874, 5.457293182505926e-05, 0.0005697277783616166, 2.517773913924431e-06, 0.0009426283321596611, 0.00020382630951465837, 1.4464713490450968e-05, 0.00016554523904905026, 2.82649623019135e-05, 0.0001100014412506759, 8.509658635753205e-06, 0.0007014694573382236, 0.00014933278481585872, 5.033334178988416e-05, 2.3120711848795105e-05, 0.00014743965654284705, 3.398618463933836e-05, 0.00037187025458568305, 0.000465924371130118, 0.00012202200780138544, 0.00017881740243498421, 8.389392780067772e-05, 1.0716587852347186e-05, 0.00018060339064221768, 2.299283200826674e-05, 4.7138786141660946e-05, 1.6102360895370235e-05, 8.721769447912016e-06, 0.0001360326483622678, 3.9078897360336904e-07])”, “Solution(objectives=[0.36205167161833274, 0.39929136195160975],variables=[0.36205167161833274, 0.0001630364566229704, 0.000570786577955664, 0.00036754953832703703, 0.0004561902560942482, 4.128708923481285e-05, 1.0970299210866804e-05, 4.221505795271107e-05, 9.395389704601378e-05, 5.320525570540046e-05, 8.726798460993775e-05, 0.00027320096059497756, 0.0002152731691745601, 3.383106313662484e-05, 3.731764198057536e-05, 0.00014890216265437054, 0.00012383251782176153, 3.0230276979575587e-05, 0.0005227978849445783, 0.00011030187208198165, 5.830993197086399e-05, 0.000385596258362265, 1.4151846891421829e-05, 0.0001425776008422332, 0.0002613390959631393, 7.755716900442764e-05, 9.545033695792387e-06, 6.42931396400895e-05, 0.00016069065865054964, 4.569852439732018e-05])”, “Solution(objectives=[0.6282891426712559, 0.21110149955619095],variables=[0.6282891426712559, 0.00015754125893357844, 6.712649791001328e-05, 0.00034364715154925176, 0.000443886316457543, 0.00018280198506746967, 0.0007724269326942245, 0.0002461403636681486, 2.4163277316476375e-05, 3.2680695365649046e-06, 2.3349237090054332e-05, 0.0006915138003344608, 0.00024685216054367453, 3.6423679875118755e-05, 1.9560735589107972e-05, 0.0001516268808218469, 0.00014193229932131241, 3.125784212719057e-05, 0.0006206316753814157, 2.6279681770283468e-05, 0.0015272730111496094, 0.0002500811571465994, 8.94816237957168e-05, 0.00011385358726813397, 5.2048412856526215e-05, 6.800645052814638e-05, 7.37186025417955e-05, 8.258344759004755e-05, 0.00015876514953807585, 0.013341284272362636])”, “Solution(objectives=[0.21854317727631287, 0.5340946229781235],variables=[0.21854317727631287, 0.00018776016590306562, 3.2545771144371375e-06, 3.318067331373997e-05, 0.0007885491377115876, 0.0002196750222129949, 4.424237760083293e-05, 0.00024867264361209897, 3.794235871214861e-05, 0.0005894026421771264, 4.563929696981979e-05, 0.0007775089447490242, 7.900743702978915e-05, 0.0006019382515804943, 2.119586369927928e-05, 0.000154624708713539, 2.8765972311120615e-05, 2.380450724435384e-05, 0.000191676929264491, 4.95188201881301e-05, 0.0007633097563620216, 0.0002770892049593349, 4.944721787573868e-06, 0.0005730640529697552, 0.0002716350985219128, 0.0003399505611415646, 5.150789971283946e-05, 2.089294604422877e-05, 4.837487898318838e-05, 0.00016865292620232424])”, “Solution(objectives=[0.4643572559541502, 0.31969290425556574],variables=[0.4643572559541502, 4.573370516514375e-05, 0.000154236035548697, 0.0003532115959195954, 0.0006613909189855753, 0.00023233865339116627, 9.758499872050002e-05, 0.00018086269439538552, 0.00017013627417093193, 0.00010437816356335845, 4.8357336151622296e-05, 0.0006884553983677391, 0.0002803451573900204, 0.0001698388032683513, 9.69840265238557e-06, 0.00013697570315274668, 3.966088080640106e-05, 3.091182696680916e-05, 0.0003197291684145342, 3.615242022152735e-05, 0.000464572226090453, 0.00013827245579109166, 9.473173881305911e-05, 0.0001682670343704673, 0.00019769250193019846, 0.00037488189911308073, 5.000513187494287e-05, 7.541485566285252e-05, 0.00015217200476674365, 4.827500319144275e-05])”, “Solution(objectives=[0.9344148208180205, 0.03430844398387379],variables=[0.9344148208180205, 3.0819498128094536e-05, 0.000618073194891181, 2.9724324228391465e-05, 0.0008023042011731493, 0.00021986832336080825, 5.007109582222678e-05, 1.5129491260689907e-05, 0.00011512702476330807, 4.09230606868779e-06, 8.062346752173306e-07, 0.0007775089447490242, 0.0003201349245438885, 0.0003155192764304454, 2.0580025252483477e-05, 0.0001546455321926272, 3.153646061296846e-05, 2.3728529724914734e-05, 0.00019397911084329713, 2.1112671855523137e-05, 0.0007633097563620216, 0.00013973285576825877, 1.3968059454191312e-06, 0.0005730640529697552, 0.00027251277619299034, 0.00038871715923686107, 5.119673669642632e-05, 2.0884214905968315e-05, 9.303774558668857e-06, 1.8210629103003188e-05])”, “Solution(objectives=[0.0015986960594809863, 0.9615495859231544],variables=[0.0015986960594809863, 0.00018963370763951868, 0.0005340277263622001, 0.0003143835729345211, 0.000766144633149615, 0.00020418073974413727, 0.00052786869980056, 0.0003352498729417983, 8.972117717809757e-05, 9.763295946735244e-05, 4.8458016861132644e-05, 3.9215220921485386e-05, 0.0002716178083510866, 3.069040291990528e-05, 0.00014643558891785268, 0.00014636692541581585, 2.6695281167359446e-05, 8.627873261039224e-05, 0.00013069445780421218, 4.708861657232415e-05, 0.0004403957742704777, 0.00012311749990561363, 9.562568072169775e-05, 0.00012995164399794867, 2.8684651908258155e-05, 3.0022516367649043e-05, 4.041861871301904e-05, 2.8933644508173518e-08, 4.129105784740912e-05, 7.940517792930007e-05])”, “Solution(objectives=[0.05491447722475207, 0.7667625277762208],variables=[0.05491447722475207, 0.00020243637884107067, 0.0005545444286411143, 1.3703000531392442e-05, 3.345705317910541e-05, 0.00020384210492384677, 0.000250142427204232, 0.00016596324558149105, 3.379839012067736e-05, 0.00010418831726816855, 5.1196685533615107e-05, 0.00016922340465849316, 0.00021208554639300234, 0.00017906086776906107, 0.0001058090714571719, 0.00017245057168199023, 3.541904952601861e-05, 4.6580039573698056e-05, 0.0005594760998124961, 3.8548840693094144e-05, 1.1074330532279101e-05, 0.00014920161290025394, 9.511989140550142e-05, 0.00032803566256086263, 2.8671482434901242e-05, 0.00011842471106135555, 4.798709538088583e-05, 4.713341190877815e-05, 5.640915464954495e-05, 4.033789395298979e-06])”, “Solution(objectives=[0.48426032180566053, 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4.795688746936339e-05, 5.102749880100648e-07, 0.00017001435971103886, 2.523458663907614e-05])”, “Solution(objectives=[0.5603456790575683, 0.25243625587861873],variables=[0.5603456790575683, 5.1717615912595176e-05, 0.00033192162892617464, 7.467879822409645e-05, 0.00011178167222613373, 0.00023151264525249758, 0.00024807025864174303, 0.0002100638066660941, 0.00012626655149956798, 8.201978490163855e-05, 5.8926421701578975e-05, 4.345946765801453e-05, 0.00021675620184935266, 0.0006303161392594073, 0.00014485036263221855, 3.0336423298286392e-05, 0.0002935051264498882, 0.0003855801241630936, 0.0004864172110746173, 1.343749468328351e-05, 0.00013163041793965403, 0.00014366894073795987, 2.4544576782210352e-05, 0.00015029566903816742, 0.0002715990113426702, 8.56264760950185e-06, 4.424685880327845e-05, 0.00019389943142166245, 0.00016486151109495038, 0.00023660123368167164])”, “Solution(objectives=[0.004834441256159654, 0.9321754483716368],variables=[0.004834441256159654, 0.00016581656954839724, 0.0005647955744473424, 1.2097590759383667e-05, 0.0007357527477931789, 9.249038221432416e-05, 5.290328692764742e-05, 0.00010056064832497034, 0.00016301473620380686, 0.00010393613241098073, 0.00010955772364213513, 0.00045741977421564296, 0.0003401777427267936, 0.0002682574078187584, 0.00013237147436966334, 0.0007064210848325863, 3.394034315958436e-05, 4.2705506331694866e-05, 2.9266963609450494e-05, 4.027728637721381e-05, 0.00011736358261430407, 0.0004449722224946766, 0.00010159951889557931, 9.575733965922644e-05, 0.00016650696718307665, 0.0003741379635262246, 2.7899854405584005e-05, 0.00014055163178715196, 6.14451092805967e-05, 1.1661246190625966e-05])”, “Solution(objectives=[0.027295234461418502, 0.836127825687533],variables=[0.027295234461418502, 8.519790431288921e-06, 0.0005201402758610751, 3.982541881506914e-05, 5.6977376885544576e-05, 4.230505966408763e-05, 0.0002495400473848372, 0.00013647544519922189, 0.0004225307949055297, 0.00010217532506516862, 9.676877261171983e-06, 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0.47778317087935873, 0.9290486126698299, 0.6841594847704869, 0.8253440321341403, 0.375738089220234, 0.6057559332694682, 0.9887247342041664, 0.08676864945949915], “type”: “scattergl”, “uid”: “94dc55e8-1e60-11e9-8d1c-335a39c169c8”}], {“height”: 800, “hovermode”: “closest”, “margin”: {“b”: 80, “l”: 80, “r”: 80, “t”: 150}, “scene”: {“xaxis”: {“title”: “x”}, “yaxis”: {“title”: “y”}}, “title”: “Pareto front approximation<br>NSGAII-ZDT1”}, {“showLink”: false, “linkText”: “Export to plot.ly”})</script>
Streaming plots¶
This plot is intended to be used as an observer entity. The visualizer observer displays the front in real-time (although it only works for problems with two and three objectives) during the execution of the algorithms (and can also be included in a Jupyter notebook); this can be useful to observe the evolution of the current Pareto front approximation produced by the algorithm:
from jmetal.util.observer import VisualizerObserver algorithm.observable.register(observer=VisualizerObserver(reference_front=problem.reference_front))
Contributing¶
Contributions to the jMetalPy project are welcome. Please, take into account the following guidelines (all developers should follow these guidelines):
Git WorkFlow¶
We have a set of branches on the remote Git server. Some branches are temporary, and others are constant throughout the life of the repository.
- Branches always present in the repository:
- master: You have the latest released to production, receive merges from the develop branch, or merge from a hotfix branch (emergency).
- Do I have to put a TAG when doing a merge from develop to master? yes
- Do I have to put a TAG when doing a merge from a hotfix branch to master? yes
- After merge from a hotfix to master, do I have to merge from master to develop? yes
- develop: It is considered the “Next Release”, receives merges from branches of each developer, either corrections (fix) or new features (feature).
- Temporary branches:
- feature/<task-id>-<description>: When we are doing a development, we create a local branch with the prefix “feature/”, then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are developing. The words are separated by hyphens.
- Where does this branch emerge? This branch always emerge from the develop branch
- When I finish the development in my feature branch, which branch to merge into?: You always merge feature branch into develop branch
- fix/<task-id>-<description>: When we are making a correction, we create a local branch with the prefix “fix/”, then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are correcting. The words are separated by hyphens.
- Where does this branch emerge? This branch always emerge from the develop branch
- When I finish the correction in my fix branch, which branch to merge into?: You always merge feature branch into develop branch
- hotfix/<task-id>-<description>: When we are correcting an emergency incidence in production, we create a local branch with the prefix “hotfix/”, then only if there is a task id, we indicate it and we add a hyphen. The following we indicate a description according to the functionality that we are correcting. The words are separated by hyphens.
- Where does this branch emerge?: This branch always emerge from the master branch
- When I finish the correction in my hotfix branch, which branch to merge into?: This branch always emerge from the master and develop branch
- Steps to follow when you are creating or going to work on a branch of any kind (feature / fix / hotfix):
- After you create your branch (feature / fix / hotfix) locally, upload it to the remote Git server. The integration system will verify your code from the outset.
- Each time you commit, as much as possible, you send a push to the server. Each push will trigger the automated launch of the tests, etc.
- Once the development is finished, having done a push to the remote Git server, and that the test phase has passed without problem, you create an pull request.
Note
Do not forget to remove your branch (feature / fix / hotfix) once the merge has been made.
Some useful Git commands:
- git fetch –prune: Cleaning branches removed and bringing new branches
PEP8!¶
It is really important to follow some standards when a team develops an application. If all team members format the code in the same format, then it is much easier to read the code. PEP8 is Python’s style guide. It’s a set of rules for how to format your Python code.
Some style rules:
- Package and module names: Modules should have short, all-lowercase names. Underscores can be used in the module name if it improves readability. Python packages should also have short, all-lowercase names, although the use of underscores is discouraged. In Python, a module is a file with the suffix ‘.py’.
- Class names: Class names should normally use the CapWords convention.
- Method names and instance variables: Lowercase with words separated by underscores as necessary to improve readability.
There are many more style standards in PEP8 so, please, refer to PEP8 documentation . The most appropriate is to use an IDE that has support for PEP8. For example, PyCharm.
Object-oriented programming¶
Object-oriented programming should be the single programming paradigm used. Avoiding as far as possible, imperative and functional programming.
In classes, we directly access the attributes, which are usually defined as public.
Only when we want to implement additional logic in the accesses to the attributes we define getter/setter methods, but always by using the *property* annotation or the *property* function:
By using *property*, we continue to access the attributes directly:
Do not use getter/setter methods without the property annotation or the property function:
Since this way of accessing the attribute is not commonly used in Python:
Structure¶
Python is not Java. In Java you cannot, by design, have more than one class in a file. In Python, you can do it.
In Python, it is appropriate to group several classes into a single .py file. For that reason, the .py files are called modules.
Python 3.6¶
We always define types in the parameters of the arguments and the return value:
We can define abstract classes (ABCs) in Python:
In the case that we want to define an interface class, it is done in the same way. We just have to define all the methods of the class as abstract.
Example of use of generic types:
In the code below, the IDE displays a warning, since although the 2nd parameter is a float type, which is a type allowed in the definition of the generic type X, it is not of the same type as the first, since the first 2 parameters must be of the same generic type (S):
In the code below, the IDE displays a warning, since the 2nd parameter is a type not allowed in the definition of the generic type ( TypeVar(‘S’, int, float) ):
When the class inherits from Generic[…], the class is defined as generic. In this way we can indicate the types that will have the values of the generic types, when using the class as type. Look at the add_car() method of the Parking class.
Note
The generic classes inherit from abc.ABCMeta, so they are abstract classes and abstract methods can be used.
In the code below, the IDE displays a warning in the call to the add_car() method when adding the car, since the 3rd parameter of the init must be a str type, as defined in the add_car() method of the Parking class.
When inheriting from generic classes, some type variables could be fixed:
Example of inheritance from non-generic class to generic class:
Example of inheritance from generic class to another generic class:
Create automatic documentation files with Sphinx¶
First, you need to know how to correctly document your code. It is important to follow these simple rules in order to automatically create good documentation for the project.
When you create a new module file (testDoc.py in this example), you should mention it using this format:
"""
.. module:: testDoc
:platform: Unix, Windows
:synopsis: A useful module indeed.
.. moduleauthor:: Andrew Carter <andrew@invalid.com>
"""
class testDoc(object):
"""We use this as a public class example class.
This class is ruled by the very trendy important method :func:`public_fn_with_sphinxy_docstring`.
.. note::
An example of intersphinx is this: you **cannot** use :mod:`pickle` on this class.
"""
def __init__(self):
pass
This code snippet generates the following documentation:
Now, you can document your methods using the following sintax:
def public_fn_with_sphinxy_docstring(self, name: str, state: bool = False) -> int:
"""This function does something.
:param name: The name to use.
:type name: str.
:param state: Current state to be in.
:type state: bool.
:returns: int -- the return code.
:raises: AttributeError, KeyError
"""
return 0
def public_fn_without_docstring(self):
return True
And the produced output doc will be:
As you may notice, if you don’t use any docstring, the method documentation will be empty.
About¶
jMetalPy is being developed by Antonio J. Nebro, associate professor at the University of Málaga, and Antonio Benítez-Hidalgo.
References¶
- J.J. Durillo, A.J. Nebro jMetal: a Java Framework for Multi-Objective Optimization. Advances in Engineering Software 42 (2011) 760-771.
- A.J. Nebro, J.J. Durillo, M. Vergne Redesigning the jMetal Multi-Objective Optimization Framework. GECCO (Companion) 2015, pp: 1093-1100. July 2015.
- Nebro A.J. et al. (2018) Extending the Speed-Constrained Multi-objective PSO (SMPSO) with Reference Point Based Preference Articulation. In: Auger A., Fonseca C., Lourenço N., Machado P., Paquete L., Whitley D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science, vol 11101. Springer, Cham
Documentation¶
Algorithms¶
Core¶
This subpackage store templates used in jMetalPy.
Algorithm¶
Observable¶
Operator¶
Problem¶
Solution¶
Quality Indicator¶
Problems¶
Installation steps¶
Via pip:
$ pip install jmetalpy
Via Github:
$ git clone https://github.com/jMetal/jMetalPy.git
$ pip install -r requirements.txt
$ python setup.py install
Features¶
The current release of jMetalPy (v0.9.0) contains the following components:
- Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, SMPSO, OMOPSO, MOEA/D, GDE3. Preference articulation-based algorithms; G-NSGA-II and SMPSO/RP; Dynamic versions of NSGA-II and SMPSO.
- Parallel computing based on Apache Spark and Dask.
- Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
- Encodings: real, binary, permutations.
- Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).
- Quality indicators: hypervolume, additive epsilon, GD, IGD.
- Pareto front plotting for problems with two or more objectives (as scatter plot/parallel coordinates/chordplot) in real-time, static or interactive.
- Experiment class for performing studies either alone or alongside jMetal.
- Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.
