mirror of
https://github.com/ethereum/solidity
synced 2023-10-03 13:03:40 +00:00
409 lines
16 KiB
C++
409 lines
16 KiB
C++
/*
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This file is part of solidity.
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solidity is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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solidity is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with solidity. If not, see <http://www.gnu.org/licenses/>.
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*/
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// SPDX-License-Identifier: GPL-3.0
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#include <test/yulPhaser/TestHelpers.h>
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#include <tools/yulPhaser/FitnessMetrics.h>
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#include <tools/yulPhaser/GeneticAlgorithms.h>
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#include <tools/yulPhaser/Population.h>
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#include <libsolutil/CommonIO.h>
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#include <boost/test/unit_test.hpp>
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#include <algorithm>
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#include <vector>
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using namespace std;
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using namespace boost::unit_test::framework;
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using namespace boost::test_tools;
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using namespace solidity::util;
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namespace solidity::phaser::test
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{
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class GeneticAlgorithmFixture
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{
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protected:
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shared_ptr<FitnessMetric> m_fitnessMetric = make_shared<ChromosomeLengthMetric>();
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};
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class ClassicGeneticAlgorithmFixture: public GeneticAlgorithmFixture
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{
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protected:
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ClassicGeneticAlgorithm::Options m_options = {
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/* elitePoolSize = */ 0.0,
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/* crossoverChance = */ 0.0,
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/* mutationChance = */ 0.0,
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/* deletionChance = */ 0.0,
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/* additionChance = */ 0.0,
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/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
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/* uniformCrossoverSwapChance= */ 0.5,
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};
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};
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BOOST_AUTO_TEST_SUITE(Phaser, *boost::unit_test::label("nooptions"))
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BOOST_AUTO_TEST_SUITE(GeneticAlgorithmsTest)
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BOOST_AUTO_TEST_SUITE(RandomAlgorithmTest)
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_preserve_elite_and_randomise_rest_of_population, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
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RandomAlgorithm algorithm({0.5, 1, 1});
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{1, 1, 1, 1, 3, 3, 3, 3}));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_elite_with_worse_individuals, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
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RandomAlgorithm algorithm({0.5, 7, 7});
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{3, 3, 3, 3, 7, 7, 7, 7}));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_replace_all_chromosomes_if_zero_size_elite, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
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RandomAlgorithm algorithm({0.0, 1, 1});
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{1, 1, 1, 1, 1, 1, 1, 1}));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_any_chromosomes_if_whole_population_is_the_elite, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
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RandomAlgorithm algorithm({1.0, 1, 1});
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5}));
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}
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE(GenerationalElitistWithExclusivePoolsTest)
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_preserve_elite_and_regenerate_rest_of_population, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 6, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 3, 3, 5, 5, 5, 5}));
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.2,
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/* crossoverPoolSize = */ 0.2,
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/* randomisationChance = */ 0.0,
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/* deletionVsAdditionChance = */ 1.0,
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/* percentGenesToRandomise = */ 0.0,
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/* percentGenesToAddOrDelete = */ 1.0,
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/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
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/* uniformCrossoverSwapChance= */ 0.5,
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};
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GenerationalElitistWithExclusivePools algorithm(options);
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{0, 0, 3, 3, 3, 3, 3, 3, 3, 3}));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_not_replace_elite_with_worse_individuals, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 6, 3, 3) + Population::makeRandom(m_fitnessMetric, 4, 5, 5);
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assert(chromosomeLengths(population) == (vector<size_t>{3, 3, 3, 3, 3, 3, 5, 5, 5, 5}));
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.2,
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/* crossoverPoolSize = */ 0.2,
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/* randomisationChance = */ 0.0,
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/* deletionVsAdditionChance = */ 0.0,
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/* percentGenesToRandomise = */ 0.0,
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/* percentGenesToAddOrDelete = */ 1.0,
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/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
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/* uniformCrossoverSwapChance= */ 0.5,
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};
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GenerationalElitistWithExclusivePools algorithm(options);
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{3, 3, 3, 3, 3, 3, 3, 3, 7, 7}));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_generate_individuals_in_the_crossover_pool_by_mutating_the_elite, GeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 20, 5, 5);
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.8,
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/* crossoverPoolSize = */ 0.0,
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/* randomisationChance = */ 0.5,
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/* deletionVsAdditionChance = */ 0.5,
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/* percentGenesToRandomise = */ 1.0,
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/* percentGenesToAddOrDelete = */ 1.0,
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/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
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/* uniformCrossoverSwapChance= */ 0.5,
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};
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GenerationalElitistWithExclusivePools algorithm(options);
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SimulationRNG::reset(1);
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((
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chromosomeLengths(newPopulation) ==
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vector<size_t>{0, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 11, 11, 11}
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));
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_generate_individuals_in_the_crossover_pool_by_crossing_over_the_elite, GeneticAlgorithmFixture)
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{
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auto population = (
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Population(m_fitnessMetric, {Chromosome("aa"), Chromosome("ff")}) +
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Population::makeRandom(m_fitnessMetric, 8, 6, 6)
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);
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assert((chromosomeLengths(population) == vector<size_t>{2, 2, 6, 6, 6, 6, 6, 6, 6, 6}));
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GenerationalElitistWithExclusivePools::Options options = {
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/* mutationPoolSize = */ 0.0,
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/* crossoverPoolSize = */ 0.8,
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/* randomisationChance = */ 0.0,
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/* deletionVsAdditionChance = */ 0.0,
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/* percentGenesToRandomise = */ 0.0,
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/* percentGenesToAddOrDelete = */ 0.0,
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/* CrossoverChoice = */ CrossoverChoice::SinglePoint,
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/* uniformCrossoverSwapChance= */ 0.5,
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};
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GenerationalElitistWithExclusivePools algorithm(options);
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SimulationRNG::reset(1);
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Population newPopulation = algorithm.runNextRound(population);
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vector<Individual> const& newIndividuals = newPopulation.individuals();
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{2, 2, 2, 2, 2, 2, 2, 2, 2, 2}));
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for (auto& individual: newIndividuals)
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BOOST_TEST((
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individual.chromosome == Chromosome("aa") ||
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individual.chromosome == Chromosome("af") ||
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individual.chromosome == Chromosome("fa") ||
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individual.chromosome == Chromosome("ff")
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));
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BOOST_TEST(any_of(newIndividuals.begin() + 2, newIndividuals.end(), [](auto& individual){
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return individual.chromosome != Chromosome("aa") && individual.chromosome != Chromosome("ff");
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}));
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}
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE(ClassicGeneticAlgorithmTest)
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_select_individuals_with_probability_proportional_to_fitness, ClassicGeneticAlgorithmFixture)
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{
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constexpr double relativeTolerance = 0.1;
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constexpr size_t populationSize = 1000;
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assert(populationSize % 4 == 0 && "Choose a number divisible by 4 for this test");
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auto population =
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Population::makeRandom(m_fitnessMetric, populationSize / 4, 0, 0) +
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Population::makeRandom(m_fitnessMetric, populationSize / 4, 1, 1) +
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Population::makeRandom(m_fitnessMetric, populationSize / 4, 2, 2) +
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Population::makeRandom(m_fitnessMetric, populationSize / 4, 3, 3);
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map<size_t, double> expectedProbabilities = {
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{0, 4.0 / (4 + 3 + 2 + 1)},
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{1, 3.0 / (4 + 3 + 2 + 1)},
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{2, 2.0 / (4 + 3 + 2 + 1)},
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{3, 1.0 / (4 + 3 + 2 + 1)},
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};
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double const expectedValue = (
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0.0 * expectedProbabilities[0] +
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1.0 * expectedProbabilities[1] +
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2.0 * expectedProbabilities[2] +
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3.0 * expectedProbabilities[3]
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);
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double const variance = (
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(0.0 - expectedValue) * (0.0 - expectedValue) * expectedProbabilities[0] +
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(1.0 - expectedValue) * (1.0 - expectedValue) * expectedProbabilities[1] +
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(2.0 - expectedValue) * (2.0 - expectedValue) * expectedProbabilities[2] +
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(3.0 - expectedValue) * (3.0 - expectedValue) * expectedProbabilities[3]
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);
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ClassicGeneticAlgorithm algorithm(m_options);
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST(newPopulation.individuals().size() == population.individuals().size());
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vector<size_t> newFitness = chromosomeLengths(newPopulation);
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BOOST_TEST(abs(mean(newFitness) - expectedValue) < expectedValue * relativeTolerance);
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BOOST_TEST(abs(meanSquaredError(newFitness, expectedValue) - variance) < variance * relativeTolerance);
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_select_only_individuals_existing_in_the_original_population, ClassicGeneticAlgorithmFixture)
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{
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constexpr size_t populationSize = 1000;
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auto population = Population::makeRandom(m_fitnessMetric, populationSize, 1, 10);
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set<string> originalSteps;
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for (auto const& individual: population.individuals())
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originalSteps.insert(toString(individual.chromosome));
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ClassicGeneticAlgorithm algorithm(m_options);
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Population newPopulation = algorithm.runNextRound(population);
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for (auto const& individual: newPopulation.individuals())
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BOOST_TEST(originalSteps.count(toString(individual.chromosome)) == 1);
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_crossover, ClassicGeneticAlgorithmFixture)
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{
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auto population = Population(m_fitnessMetric, {
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Chromosome("aa"), Chromosome("aa"), Chromosome("aa"),
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Chromosome("ff"), Chromosome("ff"), Chromosome("ff"),
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Chromosome("gg"), Chromosome("gg"), Chromosome("gg"),
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});
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set<string> originalSteps{"aa", "ff", "gg"};
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set<string> crossedSteps{"af", "fa", "fg", "gf", "ga", "ag"};
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m_options.crossoverChance = 0.8;
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ClassicGeneticAlgorithm algorithm(m_options);
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SimulationRNG::reset(1);
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Population newPopulation = algorithm.runNextRound(population);
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size_t totalCrossed = 0;
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size_t totalUnchanged = 0;
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for (auto const& individual: newPopulation.individuals())
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{
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totalCrossed += crossedSteps.count(toString(individual.chromosome));
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totalUnchanged += originalSteps.count(toString(individual.chromosome));
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}
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BOOST_TEST(totalCrossed + totalUnchanged == newPopulation.individuals().size());
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BOOST_TEST(totalCrossed >= 2);
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_mutation, ClassicGeneticAlgorithmFixture)
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{
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m_options.mutationChance = 0.6;
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ClassicGeneticAlgorithm algorithm(m_options);
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constexpr size_t populationSize = 1000;
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constexpr double relativeTolerance = 0.05;
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double const expectedValue = m_options.mutationChance;
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double const variance = m_options.mutationChance * (1 - m_options.mutationChance);
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Chromosome chromosome("aaaaaaaaaa");
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vector<Chromosome> chromosomes(populationSize, chromosome);
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Population population(m_fitnessMetric, chromosomes);
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SimulationRNG::reset(1);
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Population newPopulation = algorithm.runNextRound(population);
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vector<size_t> bernoulliTrials;
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for (auto const& individual: newPopulation.individuals())
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{
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string steps = toString(individual.chromosome);
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for (char step: steps)
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bernoulliTrials.push_back(static_cast<size_t>(step != 'a'));
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}
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BOOST_TEST(abs(mean(bernoulliTrials) - expectedValue) < expectedValue * relativeTolerance);
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BOOST_TEST(abs(meanSquaredError(bernoulliTrials, expectedValue) - variance) < variance * relativeTolerance);
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_deletion, ClassicGeneticAlgorithmFixture)
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{
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m_options.deletionChance = 0.6;
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ClassicGeneticAlgorithm algorithm(m_options);
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constexpr size_t populationSize = 1000;
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constexpr double relativeTolerance = 0.05;
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double const expectedValue = m_options.deletionChance;
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double const variance = m_options.deletionChance * (1 - m_options.deletionChance);
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Chromosome chromosome("aaaaaaaaaa");
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vector<Chromosome> chromosomes(populationSize, chromosome);
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Population population(m_fitnessMetric, chromosomes);
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SimulationRNG::reset(1);
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Population newPopulation = algorithm.runNextRound(population);
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vector<size_t> bernoulliTrials;
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for (auto const& individual: newPopulation.individuals())
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{
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string steps = toString(individual.chromosome);
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for (size_t i = 0; i < chromosome.length(); ++i)
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bernoulliTrials.push_back(static_cast<size_t>(i >= steps.size()));
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}
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BOOST_TEST(abs(mean(bernoulliTrials) - expectedValue) < expectedValue * relativeTolerance);
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BOOST_TEST(abs(meanSquaredError(bernoulliTrials, expectedValue) - variance) < variance * relativeTolerance);
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_do_addition, ClassicGeneticAlgorithmFixture)
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{
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m_options.additionChance = 0.6;
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ClassicGeneticAlgorithm algorithm(m_options);
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constexpr size_t populationSize = 1000;
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constexpr double relativeTolerance = 0.05;
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double const expectedValue = m_options.additionChance;
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double const variance = m_options.additionChance * (1 - m_options.additionChance);
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Chromosome chromosome("aaaaaaaaaa");
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vector<Chromosome> chromosomes(populationSize, chromosome);
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Population population(m_fitnessMetric, chromosomes);
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SimulationRNG::reset(1);
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Population newPopulation = algorithm.runNextRound(population);
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vector<size_t> bernoulliTrials;
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for (auto const& individual: newPopulation.individuals())
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{
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string steps = toString(individual.chromosome);
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for (size_t i = 0; i < chromosome.length() + 1; ++i)
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{
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BOOST_REQUIRE(chromosome.length() <= steps.size() && steps.size() <= 2 * chromosome.length() + 1);
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bernoulliTrials.push_back(static_cast<size_t>(i < steps.size() - chromosome.length()));
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}
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}
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BOOST_TEST(abs(mean(bernoulliTrials) - expectedValue) < expectedValue * relativeTolerance);
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BOOST_TEST(abs(meanSquaredError(bernoulliTrials, expectedValue) - variance) < variance * relativeTolerance);
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}
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BOOST_FIXTURE_TEST_CASE(runNextRound_should_preserve_elite, ClassicGeneticAlgorithmFixture)
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{
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auto population = Population::makeRandom(m_fitnessMetric, 4, 3, 3) + Population::makeRandom(m_fitnessMetric, 6, 5, 5);
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assert((chromosomeLengths(population) == vector<size_t>{3, 3, 3, 3, 5, 5, 5, 5, 5, 5}));
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m_options.elitePoolSize = 0.5;
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m_options.deletionChance = 1.0;
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ClassicGeneticAlgorithm algorithm(m_options);
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Population newPopulation = algorithm.runNextRound(population);
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BOOST_TEST((chromosomeLengths(newPopulation) == vector<size_t>{0, 0, 0, 0, 0, 3, 3, 3, 3, 5}));
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}
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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BOOST_AUTO_TEST_SUITE_END()
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}
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