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package org.uma.jmetal.experiment;

import org.uma.jmetal.algorithm.Algorithm;
import org.uma.jmetal.algorithm.multiobjective.nsgaii.NSGAIIBuilder;
import org.uma.jmetal.operator.impl.crossover.SBXCrossover;
import org.uma.jmetal.operator.impl.mutation.PolynomialMutation;
import org.uma.jmetal.problem.Problem;
import org.uma.jmetal.problem.multiobjective.zdt.*;
import org.uma.jmetal.qualityindicator.impl.*;
import org.uma.jmetal.qualityindicator.impl.hypervolume.PISAHypervolume;
import org.uma.jmetal.solution.DoubleSolution;
import org.uma.jmetal.util.JMetalException;
import org.uma.jmetal.util.experiment.Experiment;
import org.uma.jmetal.util.experiment.ExperimentBuilder;
import org.uma.jmetal.util.experiment.component.*;
import org.uma.jmetal.util.experiment.util.ExperimentAlgorithm;
import org.uma.jmetal.util.experiment.util.ExperimentProblem;

import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;

/**
 * Example of experimental study based on solving the ZDT problems with four versions of NSGA-II,
 * each of them applying a different crossover probability (from 0.7 to 1.0).
 *
 * This experiment assumes that the reference Pareto front are known, so the names of files
 * containing them and the directory where they are located must be specified.
 *
 * Six quality indicators are used for performance assessment.
 *
 * The steps to carry out the experiment are: 1. Configure the experiment 2. Execute the algorithms
 * 3. Compute the quality indicators 4. Generate Latex tables reporting means and medians 5.
 * Generate Latex tables with the result of applying the Wilcoxon Rank Sum Test 6. Generate Latex
 * tables with the ranking obtained by applying the Friedman test 7. Generate R scripts to obtain
 * boxplots
 *
 * @author Antonio J. Nebro 
 */
public class NSGAIIStudy {
  private static final int INDEPENDENT_RUNS = 25;

  public static void main(String[] args) throws IOException {
    if (args.length != 1) {
      throw new JMetalException("Missing argument: experimentBaseDirectory");
    }
    String experimentBaseDirectory = args[0];

    List> problemList = new ArrayList<>();
    problemList.add(new ExperimentProblem<>(new ZDT1()));
    problemList.add(new ExperimentProblem<>(new ZDT2()));
    problemList.add(new ExperimentProblem<>(new ZDT3()));
    problemList.add(new ExperimentProblem<>(new ZDT4()));
    problemList.add(new ExperimentProblem<>(new ZDT6()));

    List>> algorithmList =
            configureAlgorithmList(problemList);

    List referenceFrontFileNames = Arrays.asList("ZDT1.pf", "ZDT2.pf", "ZDT3.pf", "ZDT4.pf", "ZDT6.pf");

    Experiment> experiment =
            new ExperimentBuilder>("NSGAIIStudy")
                    .setAlgorithmList(algorithmList)
                    .setProblemList(problemList)
                    .setExperimentBaseDirectory(experimentBaseDirectory)
                    .setOutputParetoFrontFileName("FUN")
                    .setOutputParetoSetFileName("VAR")
                    .setReferenceFrontDirectory("/pareto_fronts")
                    .setReferenceFrontFileNames(referenceFrontFileNames)
                    .setIndicatorList(Arrays.asList(
                            new Epsilon(),
                            new Spread(),
                            new GenerationalDistance(),
                            new PISAHypervolume(),
                            new InvertedGenerationalDistance(),
                            new InvertedGenerationalDistancePlus()))
                    .setIndependentRuns(INDEPENDENT_RUNS)
                    .setNumberOfCores(8)
                    .build();

    new ExecuteAlgorithms<>(experiment).run();
    new ComputeQualityIndicators<>(experiment).run();
    new GenerateLatexTablesWithStatistics(experiment).run();
    new GenerateWilcoxonTestTablesWithR<>(experiment).run();
    new GenerateFriedmanTestTables<>(experiment).run();
    new GenerateBoxplotsWithR<>(experiment).setRows(3).setColumns(3).run();
  }

  /**
   * The algorithm list is composed of pairs {@link Algorithm} + {@link Problem} which form part of
   * a {@link ExperimentAlgorithm}, which is a decorator for class {@link Algorithm}. The {@link
   * ExperimentAlgorithm} has an optional tag component, that can be set as it is shown in this example,
   * where four variants of a same algorithm are defined.
   */
  static List>> configureAlgorithmList(
          List> problemList) {
    List>> algorithms = new ArrayList<>();

      for (int i = 0; i < problemList.size(); i++) {
        Algorithm> algorithm = new NSGAIIBuilder<>(
                problemList.get(i).getProblem(),
                new SBXCrossover(1.0, 5),
                new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(), 10.0))
                .setMaxEvaluations(25000)
                .setPopulationSize(100)
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, "NSGAIIa", problemList.get(i).getTag()));
      }

      for (int i = 0; i < problemList.size(); i++) {
        Algorithm> algorithm = new NSGAIIBuilder<>(
                problemList.get(i).getProblem(),
                new SBXCrossover(1.0, 20.0),
                new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(), 20.0))
                .setMaxEvaluations(25000)
                .setPopulationSize(100)
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, "NSGAIIb", problemList.get(i).getTag()));
      }

      for (int i = 0; i < problemList.size(); i++) {
        Algorithm> algorithm = new NSGAIIBuilder<>(problemList.get(i).getProblem(), new SBXCrossover(1.0, 40.0),
                new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(), 40.0))
                .setMaxEvaluations(25000)
                .setPopulationSize(100)
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, "NSGAIIc", problemList.get(i).getTag()));
      }

      for (int i = 0; i < problemList.size(); i++) {
        Algorithm> algorithm = new NSGAIIBuilder<>(problemList.get(i).getProblem(), new SBXCrossover(1.0, 80.0),
                new PolynomialMutation(1.0 / problemList.get(i).getProblem().getNumberOfVariables(), 80.0))
                .setMaxEvaluations(25000)
                .setPopulationSize(100)
                .build();
        algorithms.add(new ExperimentAlgorithm<>(algorithm, "NSGAIId", problemList.get(i).getTag()));
      }

    return algorithms;
  }
}