1 package net.bmahe.genetics4j.samples.symbolicregression;
2
3 import java.io.File;
4 import java.io.IOException;
5 import java.util.Comparator;
6 import java.util.Optional;
7 import java.util.Random;
8 import java.util.stream.IntStream;
9
10 import org.apache.commons.cli.CommandLine;
11 import org.apache.commons.cli.CommandLineParser;
12 import org.apache.commons.cli.DefaultParser;
13 import org.apache.commons.cli.HelpFormatter;
14 import org.apache.commons.cli.Options;
15 import org.apache.commons.cli.ParseException;
16 import org.apache.commons.io.FileUtils;
17 import org.apache.commons.lang3.StringUtils;
18 import org.apache.commons.lang3.Validate;
19 import org.apache.logging.log4j.LogManager;
20 import org.apache.logging.log4j.Logger;
21
22 import net.bmahe.genetics4j.core.EASystem;
23 import net.bmahe.genetics4j.core.EASystemFactory;
24 import net.bmahe.genetics4j.core.Fitness;
25 import net.bmahe.genetics4j.core.Genotype;
26 import net.bmahe.genetics4j.core.chromosomes.TreeChromosome;
27 import net.bmahe.genetics4j.core.evolutionlisteners.EvolutionListeners;
28 import net.bmahe.genetics4j.core.spec.EAConfiguration;
29 import net.bmahe.genetics4j.core.spec.EAExecutionContext;
30 import net.bmahe.genetics4j.core.spec.EvolutionResult;
31 import net.bmahe.genetics4j.core.spec.Optimization;
32 import net.bmahe.genetics4j.core.spec.mutation.MultiMutations;
33 import net.bmahe.genetics4j.core.spec.replacement.Elitism;
34 import net.bmahe.genetics4j.core.termination.Terminations;
35 import net.bmahe.genetics4j.gp.Operation;
36 import net.bmahe.genetics4j.gp.math.SimplificationRules;
37 import net.bmahe.genetics4j.gp.program.Program;
38 import net.bmahe.genetics4j.gp.spec.GPEAExecutionContexts;
39 import net.bmahe.genetics4j.gp.spec.chromosome.ProgramTreeChromosomeSpec;
40 import net.bmahe.genetics4j.gp.spec.combination.ProgramRandomCombine;
41 import net.bmahe.genetics4j.gp.spec.mutation.NodeReplacement;
42 import net.bmahe.genetics4j.gp.spec.mutation.ProgramApplyRules;
43 import net.bmahe.genetics4j.gp.spec.mutation.ProgramRandomMutate;
44 import net.bmahe.genetics4j.gp.spec.mutation.ProgramRandomPrune;
45 import net.bmahe.genetics4j.gp.utils.ProgramUtils;
46 import net.bmahe.genetics4j.gp.utils.TreeNodeUtils;
47 import net.bmahe.genetics4j.moo.FitnessVector;
48 import net.bmahe.genetics4j.moo.nsga2.spec.NSGA2Selection;
49 import net.bmahe.genetics4j.moo.nsga2.spec.TournamentNSGA2Selection;
50
51 public class SymbolicRegressionWithMOO {
52 final static public Logger logger = LogManager.getLogger(SymbolicRegressionWithMOO.class);
53
54 final static public String PARAM_DEST_CSV = "d";
55 final static public String LONG_PARAM_DEST_CSV = "csv-dest";
56
57 final static public String PARAM_POPULATION_SIZE = "p";
58 final static public String LONG_PARAM_POPULATION_SIZE = "population-size";
59
60 final static public String DEFAULT_DEST_CSV = SymbolicRegressionWithMOO.class.getSimpleName() + ".csv";
61
62 final static public int DEFAULT_POPULATION_SIZE = 500;
63
64 public static void cliError(final Options options, final String errorMessage) {
65 final HelpFormatter formatter = new HelpFormatter();
66 logger.error(errorMessage);
67 formatter.printHelp(SymbolicRegressionWithMOO.class.getSimpleName(), options);
68 System.exit(-1);
69 }
70
71 @SuppressWarnings("unchecked")
72 public void run(String csvFilename, int populationSize) {
73 Validate.isTrue(StringUtils.isNotBlank(csvFilename));
74 Validate.isTrue(populationSize > 0);
75
76 final Random random = new Random();
77
78 final Program program = SymbolicRegressionUtils.buildProgram(random);
79
80 final Comparator<Genotype> deduplicator = (a, b) -> TreeNodeUtils.compare(a, b, 0);
81
82
83 final Fitness<FitnessVector<Double>> computeFitness = (genoType) -> {
84 final TreeChromosome<Operation<?>> chromosome = (TreeChromosome<Operation<?>>) genoType.getChromosome(0);
85 final Double[][] inputs = new Double[100][1];
86 for (int i = 0; i < 100; i++) {
87 inputs[i][0] = (i - 50) * 1.2;
88 }
89
90 double mse = 0;
91 for (final Double[] input : inputs) {
92
93 final double x = input[0];
94 final double expected = SymbolicRegressionUtils.evaluate(x);
95 final Object result = ProgramUtils.execute(chromosome, input);
96
97 if (Double.isFinite(expected)) {
98 final Double resultDouble = (Double) result;
99 if (Double.isFinite(resultDouble)) {
100 mse += (expected - resultDouble) * (expected - resultDouble);
101 } else {
102 mse += 1_000_000_000;
103 }
104 }
105 }
106
107 return Double.isFinite(mse) ? new FitnessVector<Double>(mse / 100.0, (double) chromosome.getRoot().getSize())
108 : new FitnessVector<Double>(Double.MAX_VALUE, Double.MAX_VALUE);
109 };
110
111
112
113 final var eaConfigurationBuilder = new EAConfiguration.Builder<FitnessVector<Double>>();
114 eaConfigurationBuilder.chromosomeSpecs(ProgramTreeChromosomeSpec.of(program))
115 .parentSelectionPolicy(TournamentNSGA2Selection.ofFitnessVector(2, 3, deduplicator))
116 .replacementStrategy(
117 Elitism.builder()
118 .offspringRatio(0.995)
119 .offspringSelectionPolicy(TournamentNSGA2Selection.ofFitnessVector(2, 3, deduplicator))
120 .survivorSelectionPolicy(NSGA2Selection.ofFitnessVector(2, deduplicator))
121 .build())
122 .combinationPolicy(ProgramRandomCombine.build())
123 .mutationPolicies(
124 MultiMutations.of(
125 ProgramRandomMutate.of(0.15 * 3),
126 ProgramRandomPrune.of(0.15 * 3),
127 NodeReplacement.of(0.15 * 3)),
128 ProgramApplyRules.of(SimplificationRules.SIMPLIFY_RULES))
129 .optimization(Optimization.MINIMIZE)
130 .termination(
131 Terminations.or(
132 Terminations.<FitnessVector<Double>>ofMaxGeneration(200),
133 (eaConfiguration, generation, population, fitness) -> fitness.stream()
134 .anyMatch(fv -> fv.get(0) <= 0.000001 && fv.get(1) <= 20)))
135 .fitness(computeFitness);
136 final EAConfiguration<FitnessVector<Double>> eaConfiguration = eaConfigurationBuilder.build();
137
138
139
140 final var eaExecutionContextBuilder = GPEAExecutionContexts.<FitnessVector<Double>>forGP(random);
141
142 eaExecutionContextBuilder.populationSize(populationSize);
143 eaExecutionContextBuilder.numberOfPartitions(Math.max(1, Runtime.getRuntime().availableProcessors() - 3));
144
145 eaExecutionContextBuilder.addEvolutionListeners(
146 EvolutionListeners.ofLogTopN(
147 logger,
148 5,
149 Comparator.<FitnessVector<Double>, Double>comparing(fv -> fv.get(0)).reversed(),
150 (genotype) -> TreeNodeUtils.toStringTreeNode(genotype, 0)),
151 SymbolicRegressionUtils.csvLogger(
152 csvFilename,
153 evolutionStep -> evolutionStep.fitness().get(0),
154 evolutionStep -> evolutionStep.fitness().get(1)));
155
156 final EAExecutionContext<FitnessVector<Double>> eaExecutionContext = eaExecutionContextBuilder.build();
157 final EASystem<FitnessVector<Double>> eaSystem = EASystemFactory.from(eaConfiguration, eaExecutionContext);
158
159 final EvolutionResult<FitnessVector<Double>> evolutionResult = eaSystem.evolve();
160 final Genotype bestGenotype = evolutionResult.bestGenotype();
161 final TreeChromosome<Operation<?>> bestChromosome = (TreeChromosome<Operation<?>>) bestGenotype.getChromosome(0);
162 logger.info("Best genotype: {}", bestChromosome.getRoot());
163 logger.info("Best genotype - pretty print: {}", TreeNodeUtils.toStringTreeNode(bestChromosome.getRoot()));
164
165 final int depthIdx = 1;
166 for (int i = 0; i < 15; i++) {
167 final int depth = i;
168 final Optional<Integer> optIdx = IntStream.range(0, evolutionResult.fitness().size())
169 .boxed()
170 .filter((idx) -> evolutionResult.fitness().get(idx).get(depthIdx) == depth)
171 .sorted(
172 (a, b) -> Double
173 .compare(evolutionResult.fitness().get(a).get(0), evolutionResult.fitness().get(b).get(0)))
174 .findFirst();
175
176 optIdx.stream().forEach((idx) -> {
177 final TreeChromosome<Operation<?>> treeChromosome = (TreeChromosome<Operation<?>>) evolutionResult
178 .population()
179 .get(idx)
180 .getChromosome(0);
181
182 logger.info(
183 "Best genotype for depth {} - score {} -> {}",
184 depth,
185 evolutionResult.fitness().get(idx).get(0),
186 TreeNodeUtils.toStringTreeNode(treeChromosome.getRoot()));
187 });
188 }
189 }
190
191 public static void main(String[] args) throws IOException {
192
193
194
195
196
197 final CommandLineParser parser = new DefaultParser();
198
199 final Options options = new Options();
200 options.addOption(PARAM_DEST_CSV, LONG_PARAM_DEST_CSV, true, "destination csv file");
201
202 options.addOption(PARAM_POPULATION_SIZE, LONG_PARAM_POPULATION_SIZE, true, "Population size");
203
204 String csvFilename = DEFAULT_DEST_CSV;
205 int populationSize = DEFAULT_POPULATION_SIZE;
206 try {
207 final CommandLine line = parser.parse(options, args);
208
209 if (line.hasOption(PARAM_DEST_CSV)) {
210 csvFilename = line.getOptionValue(PARAM_DEST_CSV);
211 }
212
213 if (line.hasOption(PARAM_POPULATION_SIZE)) {
214 populationSize = Integer.parseInt(line.getOptionValue(PARAM_POPULATION_SIZE));
215 }
216
217 } catch (ParseException exp) {
218 cliError(options, "Unexpected exception:" + exp.getMessage());
219 }
220
221 logger.info("Population size: {}", populationSize);
222
223 logger.info("CSV output located at {}", csvFilename);
224 FileUtils.forceMkdirParent(new File(csvFilename));
225
226 final var symbolicRegression = new SymbolicRegressionWithMOO();
227 symbolicRegression.run(csvFilename, populationSize);
228 }
229 }