solidity/tools/yulPhaser/Phaser.cpp

765 lines
27 KiB
C++

/*
This file is part of solidity.
solidity is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
solidity is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with solidity. If not, see <http://www.gnu.org/licenses/>.
*/
#include <tools/yulPhaser/Phaser.h>
#include <tools/yulPhaser/AlgorithmRunner.h>
#include <tools/yulPhaser/Common.h>
#include <tools/yulPhaser/Exceptions.h>
#include <tools/yulPhaser/FitnessMetrics.h>
#include <tools/yulPhaser/GeneticAlgorithms.h>
#include <tools/yulPhaser/Program.h>
#include <tools/yulPhaser/SimulationRNG.h>
#include <liblangutil/CharStream.h>
#include <libsolutil/Assertions.h>
#include <libsolutil/CommonData.h>
#include <libsolutil/CommonIO.h>
#include <boost/filesystem.hpp>
#include <iostream>
using namespace std;
using namespace solidity;
using namespace solidity::langutil;
using namespace solidity::util;
using namespace solidity::phaser;
namespace po = boost::program_options;
namespace
{
map<PhaserMode, string> const PhaserModeToStringMap =
{
{PhaserMode::RunAlgorithm, "run-algorithm"},
{PhaserMode::PrintOptimisedPrograms, "print-optimised-programs"},
{PhaserMode::PrintOptimisedASTs, "print-optimised-asts"},
};
map<string, PhaserMode> const StringToPhaserModeMap = invertMap(PhaserModeToStringMap);
map<Algorithm, string> const AlgorithmToStringMap =
{
{Algorithm::Random, "random"},
{Algorithm::GEWEP, "GEWEP"},
{Algorithm::Classic, "classic"},
};
map<string, Algorithm> const StringToAlgorithmMap = invertMap(AlgorithmToStringMap);
map<MetricChoice, string> MetricChoiceToStringMap =
{
{MetricChoice::CodeSize, "code-size"},
{MetricChoice::RelativeCodeSize, "relative-code-size"},
};
map<string, MetricChoice> const StringToMetricChoiceMap = invertMap(MetricChoiceToStringMap);
map<MetricAggregatorChoice, string> const MetricAggregatorChoiceToStringMap =
{
{MetricAggregatorChoice::Average, "average"},
{MetricAggregatorChoice::Sum, "sum"},
{MetricAggregatorChoice::Maximum, "maximum"},
{MetricAggregatorChoice::Minimum, "minimum"},
};
map<string, MetricAggregatorChoice> const StringToMetricAggregatorChoiceMap = invertMap(MetricAggregatorChoiceToStringMap);
}
istream& phaser::operator>>(istream& _inputStream, PhaserMode& _phaserMode) { return deserializeChoice(_inputStream, _phaserMode, StringToPhaserModeMap); }
ostream& phaser::operator<<(ostream& _outputStream, PhaserMode _phaserMode) { return serializeChoice(_outputStream, _phaserMode, PhaserModeToStringMap); }
istream& phaser::operator>>(istream& _inputStream, Algorithm& _algorithm) { return deserializeChoice(_inputStream, _algorithm, StringToAlgorithmMap); }
ostream& phaser::operator<<(ostream& _outputStream, Algorithm _algorithm) { return serializeChoice(_outputStream, _algorithm, AlgorithmToStringMap); }
istream& phaser::operator>>(istream& _inputStream, MetricChoice& _metric) { return deserializeChoice(_inputStream, _metric, StringToMetricChoiceMap); }
ostream& phaser::operator<<(ostream& _outputStream, MetricChoice _metric) { return serializeChoice(_outputStream, _metric, MetricChoiceToStringMap); }
istream& phaser::operator>>(istream& _inputStream, MetricAggregatorChoice& _aggregator) { return deserializeChoice(_inputStream, _aggregator, StringToMetricAggregatorChoiceMap); }
ostream& phaser::operator<<(ostream& _outputStream, MetricAggregatorChoice _aggregator) { return serializeChoice(_outputStream, _aggregator, MetricAggregatorChoiceToStringMap); }
GeneticAlgorithmFactory::Options GeneticAlgorithmFactory::Options::fromCommandLine(po::variables_map const& _arguments)
{
return {
_arguments["algorithm"].as<Algorithm>(),
_arguments["min-chromosome-length"].as<size_t>(),
_arguments["max-chromosome-length"].as<size_t>(),
_arguments.count("random-elite-pool-size") > 0 ?
_arguments["random-elite-pool-size"].as<double>() :
optional<double>{},
_arguments["gewep-mutation-pool-size"].as<double>(),
_arguments["gewep-crossover-pool-size"].as<double>(),
_arguments["gewep-randomisation-chance"].as<double>(),
_arguments["gewep-deletion-vs-addition-chance"].as<double>(),
_arguments.count("gewep-genes-to-randomise") > 0 ?
_arguments["gewep-genes-to-randomise"].as<double>() :
optional<double>{},
_arguments.count("gewep-genes-to-add-or-delete") > 0 ?
_arguments["gewep-genes-to-add-or-delete"].as<double>() :
optional<double>{},
_arguments["classic-elite-pool-size"].as<double>(),
_arguments["classic-crossover-chance"].as<double>(),
_arguments["classic-mutation-chance"].as<double>(),
_arguments["classic-deletion-chance"].as<double>(),
_arguments["classic-addition-chance"].as<double>(),
};
}
unique_ptr<GeneticAlgorithm> GeneticAlgorithmFactory::build(
Options const& _options,
size_t _populationSize
)
{
assert(_populationSize > 0);
switch (_options.algorithm)
{
case Algorithm::Random:
{
double elitePoolSize = 1.0 / _populationSize;
if (_options.randomElitePoolSize.has_value())
elitePoolSize = _options.randomElitePoolSize.value();
return make_unique<RandomAlgorithm>(RandomAlgorithm::Options{
/* elitePoolSize = */ elitePoolSize,
/* minChromosomeLength = */ _options.minChromosomeLength,
/* maxChromosomeLength = */ _options.maxChromosomeLength,
});
}
case Algorithm::GEWEP:
{
double percentGenesToRandomise = 1.0 / _options.maxChromosomeLength;
double percentGenesToAddOrDelete = percentGenesToRandomise;
if (_options.gewepGenesToRandomise.has_value())
percentGenesToRandomise = _options.gewepGenesToRandomise.value();
if (_options.gewepGenesToAddOrDelete.has_value())
percentGenesToAddOrDelete = _options.gewepGenesToAddOrDelete.value();
return make_unique<GenerationalElitistWithExclusivePools>(GenerationalElitistWithExclusivePools::Options{
/* mutationPoolSize = */ _options.gewepMutationPoolSize,
/* crossoverPoolSize = */ _options.gewepCrossoverPoolSize,
/* randomisationChance = */ _options.gewepRandomisationChance,
/* deletionVsAdditionChance = */ _options.gewepDeletionVsAdditionChance,
/* percentGenesToRandomise = */ percentGenesToRandomise,
/* percentGenesToAddOrDelete = */ percentGenesToAddOrDelete,
});
}
case Algorithm::Classic:
{
return make_unique<ClassicGeneticAlgorithm>(ClassicGeneticAlgorithm::Options{
/* elitePoolSize = */ _options.classicElitePoolSize,
/* crossoverChance = */ _options.classicCrossoverChance,
/* mutationChance = */ _options.classicMutationChance,
/* deletionChance = */ _options.classicDeletionChance,
/* additionChance = */ _options.classicAdditionChance,
});
}
default:
assertThrow(false, solidity::util::Exception, "Invalid Algorithm value.");
}
}
FitnessMetricFactory::Options FitnessMetricFactory::Options::fromCommandLine(po::variables_map const& _arguments)
{
return {
_arguments["metric"].as<MetricChoice>(),
_arguments["metric-aggregator"].as<MetricAggregatorChoice>(),
_arguments["relative-metric-scale"].as<size_t>(),
_arguments["chromosome-repetitions"].as<size_t>(),
};
}
unique_ptr<FitnessMetric> FitnessMetricFactory::build(
Options const& _options,
vector<Program> _programs,
vector<shared_ptr<ProgramCache>> _programCaches
)
{
assert(_programCaches.size() == _programs.size());
assert(_programs.size() > 0 && "Validations should prevent this from being executed with zero files.");
vector<shared_ptr<FitnessMetric>> metrics;
switch (_options.metric)
{
case MetricChoice::CodeSize:
{
for (size_t i = 0; i < _programs.size(); ++i)
metrics.push_back(make_unique<ProgramSize>(
_programCaches[i] != nullptr ? optional<Program>{} : move(_programs[i]),
move(_programCaches[i]),
_options.chromosomeRepetitions
));
break;
}
case MetricChoice::RelativeCodeSize:
{
for (size_t i = 0; i < _programs.size(); ++i)
metrics.push_back(make_unique<RelativeProgramSize>(
_programCaches[i] != nullptr ? optional<Program>{} : move(_programs[i]),
move(_programCaches[i]),
_options.relativeMetricScale,
_options.chromosomeRepetitions
));
break;
}
default:
assertThrow(false, solidity::util::Exception, "Invalid MetricChoice value.");
}
switch (_options.metricAggregator)
{
case MetricAggregatorChoice::Average:
return make_unique<FitnessMetricAverage>(move(metrics));
case MetricAggregatorChoice::Sum:
return make_unique<FitnessMetricSum>(move(metrics));
case MetricAggregatorChoice::Maximum:
return make_unique<FitnessMetricMaximum>(move(metrics));
case MetricAggregatorChoice::Minimum:
return make_unique<FitnessMetricMinimum>(move(metrics));
default:
assertThrow(false, solidity::util::Exception, "Invalid MetricAggregatorChoice value.");
}
}
PopulationFactory::Options PopulationFactory::Options::fromCommandLine(po::variables_map const& _arguments)
{
return {
_arguments["min-chromosome-length"].as<size_t>(),
_arguments["max-chromosome-length"].as<size_t>(),
_arguments.count("population") > 0 ?
_arguments["population"].as<vector<string>>() :
vector<string>{},
_arguments.count("random-population") > 0 ?
_arguments["random-population"].as<vector<size_t>>() :
vector<size_t>{},
_arguments.count("population-from-file") > 0 ?
_arguments["population-from-file"].as<vector<string>>() :
vector<string>{},
};
}
Population PopulationFactory::build(
Options const& _options,
shared_ptr<FitnessMetric> _fitnessMetric
)
{
Population population = buildFromStrings(_options.population, _fitnessMetric);
size_t combinedSize = 0;
for (size_t populationSize: _options.randomPopulation)
combinedSize += populationSize;
population = move(population) + buildRandom(
combinedSize,
_options.minChromosomeLength,
_options.maxChromosomeLength,
_fitnessMetric
);
for (string const& populationFilePath: _options.populationFromFile)
population = move(population) + buildFromFile(populationFilePath, _fitnessMetric);
return population;
}
Population PopulationFactory::buildFromStrings(
vector<string> const& _geneSequences,
shared_ptr<FitnessMetric> _fitnessMetric
)
{
vector<Chromosome> chromosomes;
for (string const& geneSequence: _geneSequences)
chromosomes.emplace_back(geneSequence);
return Population(move(_fitnessMetric), move(chromosomes));
}
Population PopulationFactory::buildRandom(
size_t _populationSize,
size_t _minChromosomeLength,
size_t _maxChromosomeLength,
shared_ptr<FitnessMetric> _fitnessMetric
)
{
return Population::makeRandom(
move(_fitnessMetric),
_populationSize,
_minChromosomeLength,
_maxChromosomeLength
);
}
Population PopulationFactory::buildFromFile(
string const& _filePath,
shared_ptr<FitnessMetric> _fitnessMetric
)
{
return buildFromStrings(readLinesFromFile(_filePath), move(_fitnessMetric));
}
ProgramCacheFactory::Options ProgramCacheFactory::Options::fromCommandLine(po::variables_map const& _arguments)
{
return {
_arguments["program-cache"].as<bool>(),
};
}
vector<shared_ptr<ProgramCache>> ProgramCacheFactory::build(
Options const& _options,
vector<Program> _programs
)
{
vector<shared_ptr<ProgramCache>> programCaches;
for (Program& program: _programs)
programCaches.push_back(_options.programCacheEnabled ? make_shared<ProgramCache>(move(program)) : nullptr);
return programCaches;
}
ProgramFactory::Options ProgramFactory::Options::fromCommandLine(po::variables_map const& _arguments)
{
return {
_arguments["input-files"].as<vector<string>>(),
_arguments["prefix"].as<string>(),
};
}
vector<Program> ProgramFactory::build(Options const& _options)
{
vector<Program> inputPrograms;
for (auto& path: _options.inputFiles)
{
CharStream sourceCode = loadSource(path);
variant<Program, ErrorList> programOrErrors = Program::load(sourceCode);
if (holds_alternative<ErrorList>(programOrErrors))
{
cerr << get<ErrorList>(programOrErrors) << endl;
assertThrow(false, InvalidProgram, "Failed to load program " + path);
}
get<Program>(programOrErrors).optimise(Chromosome(_options.prefix).optimisationSteps());
inputPrograms.push_back(move(get<Program>(programOrErrors)));
}
return inputPrograms;
}
CharStream ProgramFactory::loadSource(string const& _sourcePath)
{
assertThrow(boost::filesystem::exists(_sourcePath), MissingFile, "Source file does not exist: " + _sourcePath);
string sourceCode = readFileAsString(_sourcePath);
return CharStream(sourceCode, _sourcePath);
}
void Phaser::main(int _argc, char** _argv)
{
optional<po::variables_map> arguments = parseCommandLine(_argc, _argv);
if (!arguments.has_value())
return;
initialiseRNG(arguments.value());
runPhaser(arguments.value());
}
Phaser::CommandLineDescription Phaser::buildCommandLineDescription()
{
size_t const lineLength = po::options_description::m_default_line_length;
size_t const minDescriptionLength = lineLength - 23;
po::options_description keywordDescription(
"yul-phaser, a tool for finding the best sequence of Yul optimisation phases.\n"
"\n"
"Usage: yul-phaser [options] <file>\n"
"Reads <file> as Yul code and tries to find the best order in which to run optimisation"
" phases using a genetic algorithm.\n"
"Example:\n"
"yul-phaser program.yul\n"
"\n"
"Allowed options",
lineLength,
minDescriptionLength
);
po::options_description generalDescription("GENERAL", lineLength, minDescriptionLength);
generalDescription.add_options()
("help", "Show help message and exit.")
("input-files", po::value<vector<string>>()->required()->value_name("<PATH>"), "Input files.")
(
"prefix",
po::value<string>()->value_name("<CHROMOSOME>")->default_value(""),
"Initial optimisation steps automatically applied to every input program.\n"
"The result is treated as if it was the actual input, i.e. the steps are not considered "
"a part of the chromosomes and cannot be mutated. The values of relative metric values "
"are also relative to the fitness of a program with these steps applied rather than the "
"fitness of the original program.\n"
"Note that phaser always adds a 'hgo' prefix to ensure that chromosomes can "
"contain arbitrary optimisation steps. This implicit prefix cannot be changed or "
"or removed using this option. The value given here is applied after it."
)
("seed", po::value<uint32_t>()->value_name("<NUM>"), "Seed for the random number generator.")
(
"rounds",
po::value<size_t>()->value_name("<NUM>"),
"The number of rounds after which the algorithm should stop. (default=no limit)."
)
(
"mode",
po::value<PhaserMode>()->value_name("<NAME>")->default_value(PhaserMode::RunAlgorithm),
"Mode of operation. The default is to run the algorithm but you can also tell phaser "
"to do something else with its parameters, e.g. just print the optimised programs and exit."
)
;
keywordDescription.add(generalDescription);
po::options_description algorithmDescription("ALGORITHM", lineLength, minDescriptionLength);
algorithmDescription.add_options()
(
"algorithm",
po::value<Algorithm>()->value_name("<NAME>")->default_value(Algorithm::GEWEP),
"Algorithm"
)
(
"no-randomise-duplicates",
po::bool_switch(),
"By default, after each round of the algorithm duplicate chromosomes are removed from"
"the population and replaced with randomly generated ones. "
"This option disables this postprocessing."
)
(
"min-chromosome-length",
po::value<size_t>()->value_name("<NUM>")->default_value(12),
"Minimum length of randomly generated chromosomes."
)
(
"max-chromosome-length",
po::value<size_t>()->value_name("<NUM>")->default_value(30),
"Maximum length of randomly generated chromosomes."
)
;
keywordDescription.add(algorithmDescription);
po::options_description gewepAlgorithmDescription("GEWEP ALGORITHM", lineLength, minDescriptionLength);
gewepAlgorithmDescription.add_options()
(
"gewep-mutation-pool-size",
po::value<double>()->value_name("<FRACTION>")->default_value(0.25),
"Percentage of population to regenerate using mutations in each round."
)
(
"gewep-crossover-pool-size",
po::value<double>()->value_name("<FRACTION>")->default_value(0.25),
"Percentage of population to regenerate using crossover in each round."
)
(
"gewep-randomisation-chance",
po::value<double>()->value_name("<PROBABILITY>")->default_value(0.9),
"The chance of choosing gene randomisation as the mutation to perform."
)
(
"gewep-deletion-vs-addition-chance",
po::value<double>()->value_name("<PROBABILITY>")->default_value(0.5),
"The chance of choosing gene deletion as the mutation if randomisation was not chosen."
)
(
"gewep-genes-to-randomise",
po::value<double>()->value_name("<PROBABILITY>"),
"The chance of any given gene being mutated in gene randomisation. "
"(default=1/max-chromosome-length)"
)
(
"gewep-genes-to-add-or-delete",
po::value<double>()->value_name("<PROBABILITY>"),
"The chance of a gene being added (or deleted) in gene addition (or deletion). "
"(default=1/max-chromosome-length)"
)
;
keywordDescription.add(gewepAlgorithmDescription);
po::options_description classicGeneticAlgorithmDescription("CLASSIC GENETIC ALGORITHM", lineLength, minDescriptionLength);
classicGeneticAlgorithmDescription.add_options()
(
"classic-elite-pool-size",
po::value<double>()->value_name("<FRACTION>")->default_value(0),
"Percentage of population to regenerate using mutations in each round."
)
(
"classic-crossover-chance",
po::value<double>()->value_name("<FRACTION>")->default_value(0.75),
"Chance of a chromosome being selected for crossover."
)
(
"classic-mutation-chance",
po::value<double>()->value_name("<FRACTION>")->default_value(0.01),
"Chance of a gene being mutated."
)
(
"classic-deletion-chance",
po::value<double>()->value_name("<PROBABILITY>")->default_value(0.01),
"Chance of a gene being deleted."
)
(
"classic-addition-chance",
po::value<double>()->value_name("<PROBABILITY>")->default_value(0.01),
"Chance of a random gene being added."
)
;
keywordDescription.add(classicGeneticAlgorithmDescription);
po::options_description randomAlgorithmDescription("RANDOM ALGORITHM", lineLength, minDescriptionLength);
randomAlgorithmDescription.add_options()
(
"random-elite-pool-size",
po::value<double>()->value_name("<FRACTION>"),
"Percentage of the population preserved in each round. "
"(default=one individual, regardless of population size)"
)
;
keywordDescription.add(randomAlgorithmDescription);
po::options_description populationDescription("POPULATION", lineLength, minDescriptionLength);
populationDescription.add_options()
(
"population",
po::value<vector<string>>()->multitoken()->value_name("<CHROMOSOMES>"),
"List of chromosomes to be included in the initial population. "
"You can specify multiple values separated with spaces or invoke the option multiple times "
"and all the values will be included."
)
(
"random-population",
po::value<vector<size_t>>()->value_name("<SIZE>"),
"The number of randomly generated chromosomes to be included in the initial population."
)
(
"population-from-file",
po::value<vector<string>>()->value_name("<FILE>"),
"A text file with a list of chromosomes (one per line) to be included in the initial population."
)
(
"population-autosave",
po::value<string>()->value_name("<FILE>"),
"If specified, the population is saved in the specified file after each round. (default=autosave disabled)"
)
;
keywordDescription.add(populationDescription);
po::options_description metricsDescription("METRICS", lineLength, minDescriptionLength);
metricsDescription.add_options()
(
"metric",
po::value<MetricChoice>()->value_name("<NAME>")->default_value(MetricChoice::RelativeCodeSize),
"Metric used to evaluate the fitness of a chromosome."
)
(
"metric-aggregator",
po::value<MetricAggregatorChoice>()->value_name("<NAME>")->default_value(MetricAggregatorChoice::Average),
"Operator used to combine multiple fitness metric obtained by evaluating a chromosome "
"separately for each input program."
)
(
"relative-metric-scale",
po::value<size_t>()->value_name("<EXPONENT>")->default_value(3),
"Scaling factor for values produced by relative fitness metrics. \n"
"Since all metrics must produce integer values, the fractional part of the result is discarded. "
"To keep the numbers meaningful, a relative metric multiples its values by a scaling factor "
"and this option specifies the exponent of this factor. "
"For example with value of 3 the factor is 10^3 = 1000 and the metric will return "
"500 to represent 0.5, 1000 for 1.0, 2000 for 2.0 and so on. "
"Using a bigger factor allows discerning smaller relative differences between chromosomes "
"but makes the numbers less readable and may also lose precision if the numbers are very large."
)
(
"chromosome-repetitions",
po::value<size_t>()->value_name("<COUNT>")->default_value(1),
"Number of times to repeat the sequence optimisation steps represented by a chromosome."
)
;
keywordDescription.add(metricsDescription);
po::options_description cacheDescription("CACHE", lineLength, minDescriptionLength);
cacheDescription.add_options()
(
"program-cache",
po::bool_switch(),
"Enables caching of intermediate programs corresponding to chromosome prefixes.\n"
"This speeds up fitness evaluation by a lot but eats tons of memory if the chromosomes are long. "
"Disabled by default since there's currently no way to set an upper limit on memory usage but "
"highly recommended if your computer has enough RAM."
)
;
keywordDescription.add(cacheDescription);
po::options_description outputDescription("OUTPUT", lineLength, minDescriptionLength);
outputDescription.add_options()
(
"show-initial-population",
po::bool_switch(),
"Print the state of the population before the algorithm starts."
)
(
"show-only-top-chromosome",
po::bool_switch(),
"Print only the best chromosome found in each round rather than the whole population."
)
(
"hide-round",
po::bool_switch(),
"Hide information about the current round (round number and elapsed time)."
)
(
"show-cache-stats",
po::bool_switch(),
"Print information about cache size and effectiveness after each round."
)
(
"show-seed",
po::bool_switch(),
"Print the selected random seed."
)
;
keywordDescription.add(outputDescription);
po::positional_options_description positionalDescription;
positionalDescription.add("input-files", -1);
return {keywordDescription, positionalDescription};
}
optional<po::variables_map> Phaser::parseCommandLine(int _argc, char** _argv)
{
auto [keywordDescription, positionalDescription] = buildCommandLineDescription();
po::variables_map arguments;
po::notify(arguments);
po::command_line_parser parser(_argc, _argv);
parser.options(keywordDescription).positional(positionalDescription);
po::store(parser.run(), arguments);
if (arguments.count("help") > 0)
{
cout << keywordDescription << endl;
return nullopt;
}
if (arguments.count("input-files") == 0)
assertThrow(false, NoInputFiles, "Missing argument: input-files.");
return arguments;
}
void Phaser::initialiseRNG(po::variables_map const& _arguments)
{
uint32_t seed;
if (_arguments.count("seed") > 0)
seed = _arguments["seed"].as<uint32_t>();
else
seed = SimulationRNG::generateSeed();
SimulationRNG::reset(seed);
if (_arguments["show-seed"].as<bool>())
cout << "Random seed: " << seed << endl;
}
AlgorithmRunner::Options Phaser::buildAlgorithmRunnerOptions(po::variables_map const& _arguments)
{
return {
_arguments.count("rounds") > 0 ? static_cast<optional<size_t>>(_arguments["rounds"].as<size_t>()) : nullopt,
_arguments.count("population-autosave") > 0 ? static_cast<optional<string>>(_arguments["population-autosave"].as<string>()) : nullopt,
!_arguments["no-randomise-duplicates"].as<bool>(),
_arguments["min-chromosome-length"].as<size_t>(),
_arguments["max-chromosome-length"].as<size_t>(),
_arguments["show-initial-population"].as<bool>(),
_arguments["show-only-top-chromosome"].as<bool>(),
!_arguments["hide-round"].as<bool>(),
_arguments["show-cache-stats"].as<bool>(),
};
}
void Phaser::runPhaser(po::variables_map const& _arguments)
{
auto programOptions = ProgramFactory::Options::fromCommandLine(_arguments);
auto cacheOptions = ProgramCacheFactory::Options::fromCommandLine(_arguments);
auto metricOptions = FitnessMetricFactory::Options::fromCommandLine(_arguments);
auto populationOptions = PopulationFactory::Options::fromCommandLine(_arguments);
vector<Program> programs = ProgramFactory::build(programOptions);
vector<shared_ptr<ProgramCache>> programCaches = ProgramCacheFactory::build(cacheOptions, programs);
unique_ptr<FitnessMetric> fitnessMetric = FitnessMetricFactory::build(metricOptions, programs, programCaches);
Population population = PopulationFactory::build(populationOptions, move(fitnessMetric));
if (_arguments["mode"].as<PhaserMode>() == PhaserMode::RunAlgorithm)
runAlgorithm(_arguments, move(population), move(programCaches));
else
printOptimisedProgramsOrASTs(_arguments, population, move(programs), _arguments["mode"].as<PhaserMode>());
}
void Phaser::runAlgorithm(
po::variables_map const& _arguments,
Population _population,
vector<shared_ptr<ProgramCache>> _programCaches
)
{
auto algorithmOptions = GeneticAlgorithmFactory::Options::fromCommandLine(_arguments);
unique_ptr<GeneticAlgorithm> geneticAlgorithm = GeneticAlgorithmFactory::build(
algorithmOptions,
_population.individuals().size()
);
AlgorithmRunner algorithmRunner(move(_population), move(_programCaches), buildAlgorithmRunnerOptions(_arguments), cout);
algorithmRunner.run(*geneticAlgorithm);
}
void Phaser::printOptimisedProgramsOrASTs(
po::variables_map const& _arguments,
Population const& _population,
vector<Program> _programs,
PhaserMode phaserMode
)
{
assert(phaserMode == PhaserMode::PrintOptimisedPrograms || phaserMode == PhaserMode::PrintOptimisedASTs);
assert(_programs.size() == _arguments["input-files"].as<vector<string>>().size());
if (_population.individuals().size() == 0)
{
cout << "<EMPTY POPULATION>" << endl;
return;
}
vector<string> const& paths = _arguments["input-files"].as<vector<string>>();
for (auto& individual: _population.individuals())
{
cout << "Chromosome: " << individual.chromosome << endl;
for (size_t i = 0; i < _programs.size(); ++i)
{
for (size_t j = 0; j < _arguments["chromosome-repetitions"].as<size_t>(); ++j)
_programs[i].optimise(individual.chromosome.optimisationSteps());
cout << "Program: " << paths[i] << endl;
if (phaserMode == PhaserMode::PrintOptimisedPrograms)
cout << _programs[i] << endl;
else
cout << _programs[i].toJson() << endl;
}
}
}