public override double Evaluate(IChromosome chromosome)
{
try
{
var parameters = Config.Genes.Select(s =>
new MinMaxParameterSpec(min: (double)(s.MinDecimal ?? s.MinInt.Value), max: (double)(s.MaxDecimal ?? s.MaxInt.Value),
transform: Transform.Linear, parameterType: s.Precision > 0 ? ParameterType.Continuous : ParameterType.Discrete)
).ToArray();
IOptimizer optimizer = null;
if (Config.Fitness != null)
{
if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.RandomSearch.ToString())
{
optimizer = new RandomSearchOptimizer(parameters, iterations: Config.Generations, seed: 42, maxDegreeOfParallelism: Config.MaxThreads);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.ParticleSwarm.ToString())
{
optimizer = new ParticleSwarmOptimizer(parameters, maxIterations: Config.Generations, numberOfParticles: Config.PopulationSize,
seed: 42, maxDegreeOfParallelism: Config.MaxThreads);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Bayesian.ToString())
{
optimizer = new BayesianOptimizer(parameters, maxIterations: Config.Generations, numberOfStartingPoints: Config.PopulationSize, seed: 42);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GlobalizedBoundedNelderMead.ToString())
{
optimizer = new GlobalizedBoundedNelderMeadOptimizer(parameters, maxRestarts: Config.Generations,
maxIterationsPrRestart: Config.PopulationSize, seed: 42, maxDegreeOfParallelism: Config.MaxThreads);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Genetic.ToString())
{
throw new Exception("Genetic optimizer cannot be used with Sharpe Maximizer");
}
}
//todo:
// GridSearchOptimizer?
Func <double[], OptimizerResult> minimize = p => Minimize(p, (Chromosome)chromosome);
// run optimizer
var result = optimizer.OptimizeBest(minimize);
Best = ToChromosome(result, chromosome);
return(result.Error);
}
catch (Exception ex)
{
Program.Logger.Error(ex);
return(ErrorFitness);
}
}
public async Task <IterationResult> Start(IOptimizerConfiguration config, CancellationToken cancellationToken)
{
CancellationToken = cancellationToken;
var parameters = config.Genes.Select(s =>
new MinMaxParameterSpec(min: s.Min ?? s.Actual.Value, max: s.Max ?? s.Actual.Value,
transform: Transform.Linear, parameterType: s.Precision > 0 ? ParameterType.Continuous : ParameterType.Discrete)
).ToArray();
Keys = config.Genes.Where(g => g.Key != "id").Select(s => s.Key);
IOptimizer optimizerMethod = null;
if (config.Fitness != null)
{
if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.RandomSearch.ToString())
{
optimizerMethod = new RandomSearchOptimizer(parameters, iterations: config.Generations, seed: 42, runParallel: false);
}
else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.ParticleSwarm.ToString())
{
optimizerMethod = new ParticleSwarmOptimizer(parameters, maxIterations: config.Generations, numberOfParticles: config.PopulationSize,
seed: 42, maxDegreeOfParallelism: 1);
}
else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Bayesian.ToString())
{
optimizerMethod = new BayesianOptimizer(parameters: parameters, iterations: config.Generations, randomStartingPointCount: config.PopulationSize,
functionEvaluationsPerIterationCount: config.PopulationSize, seed: 42, runParallel: false);
}
else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GlobalizedBoundedNelderMead.ToString())
{
optimizerMethod = new GlobalizedBoundedNelderMeadOptimizer(parameters, maxRestarts: config.Generations,
maxIterationsPrRestart: config.PopulationSize, seed: 42, maxDegreeOfParallelism: 1);
}
else if (config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GridSearch.ToString())
{
optimizerMethod = new GridSearchOptimizer(config.Genes.Select(s => new GridParameterSpec(RangeWithPrecision.Range(s.Min.Value, s.Max.Value, s.Precision.Value).ToArray())).ToArray(), runParallel: false);
}
}
else
{
throw new ArgumentException("No optimizer was configured.");
}
var result = await optimizerMethod.OptimizeBest(Minimize);
return(new IterationResult {
ParameterSet = result.ParameterSet, Cost = IsMaximizing ? result.Error * -1 : result.Error
});
}
public void GlobalizedBoundedNelderMeadOptimizer_OptimizeBest()
{
var parameters = new ParameterBounds[]
{
new ParameterBounds(-10.0, 10.0, Transform.Linear),
new ParameterBounds(-10.0, 10.0, Transform.Linear),
new ParameterBounds(-10.0, 10.0, Transform.Linear),
};
var sut = new GlobalizedBoundedNelderMeadOptimizer(parameters, 5, 1e-5, 10);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(actual.Error, -0.99999927563662372, 0.0000001);
Assert.AreEqual(actual.ParameterSet.Length, 3);
Assert.AreEqual(actual.ParameterSet[0], 1.5710337846223761, 0.0000001);
Assert.AreEqual(actual.ParameterSet[1], 3.1421855980282225, 0.0000001);
Assert.AreEqual(actual.ParameterSet[2], 5.203790999662519E-07, 0.0000001);
}
public void GlobalizedBoundedNelderMeadOptimizer_OptimizeBest()
{
var parameters = new ParameterBounds[]
{
new ParameterBounds(-10.0, 10.0, Transform.Linear),
new ParameterBounds(-10.0, 10.0, Transform.Linear),
new ParameterBounds(-10.0, 10.0, Transform.Linear),
};
var sut = new GlobalizedBoundedNelderMeadOptimizer(parameters, 5, 1e-5, 10);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(actual.Error, -0.99999949547279676, 0.0000001);
Assert.AreEqual(actual.ParameterSet.Length, 3);
Assert.AreEqual(actual.ParameterSet[0], -7.8547285710964134, 0.0000001);
Assert.AreEqual(actual.ParameterSet[1], 6.2835515298977995, 0.0000001);
Assert.AreEqual(actual.ParameterSet[2], -1.5851024386788885E-07, 0.0000001);
}
public void GlobalizedBoundedNelderMeadOptimizer_OptimizeBest()
{
var parameters = new double[][]
{
new double[] { -10.0, 10.0 },
new double[] { -10.0, 10.0 },
new double[] { -10.0, 10.0 }
};
var sut = new GlobalizedBoundedNelderMeadOptimizer(parameters, 5, 1e-5, 10);
var actual = sut.OptimizeBest(Minimize);
Assert.AreEqual(actual.Error, -0.99994579068533251, 0.0000001);
Assert.AreEqual(actual.ParameterSet.Length, 3);
Assert.AreEqual(actual.ParameterSet[0], -1.5674665655168316, 0.0000001);
Assert.AreEqual(actual.ParameterSet[1], 6.273371320712446, 0.0000001);
Assert.AreEqual(actual.ParameterSet[2], -5.0918060053651561E-07, 0.0000001);
}
public void GlobalizedBoundedNelderMeadOptimizer_Optimize()
{
var parameters = new double[][] { new double[] { 0.0, 100.0 } };
var sut = new GlobalizedBoundedNelderMeadOptimizer(parameters, 5, 1e-5, 10);
var results = sut.Optimize(Minimize2);
var actual = new OptimizerResult[] { results.First(), results.Last() };
var expected = new OptimizerResult[]
{
new OptimizerResult(new double[] { 37.71314535727786 }, 109.34381396310141),
new OptimizerResult(new double[] { 37.7131485180996 }, 109.34381396350526)
};
Assert.AreEqual(expected.First().Error, actual.First().Error, 0.0001);
Assert.AreEqual(expected.First().ParameterSet.First(), actual.First().ParameterSet.First(), 0.0001);
Assert.AreEqual(expected.Last().Error, actual.Last().Error, 0.0001);
Assert.AreEqual(expected.Last().ParameterSet.First(), actual.Last().ParameterSet.First(), 0.0001);
}
static GlobalizedBoundedNelderMeadOptimizer CreateSut(
int?maybeMaxDegreeOfParallelism,
MinMaxParameterSpec[] parameters)
{
const int DefaultMaxDegreeOfParallelism = -1;
var maxDegreeOfParallelism = maybeMaxDegreeOfParallelism.HasValue ?
maybeMaxDegreeOfParallelism.Value : DefaultMaxDegreeOfParallelism;
var sut = new GlobalizedBoundedNelderMeadOptimizer(parameters,
maxRestarts: 50,
noImprovementThreshold: 1e-5,
maxIterationsWithoutImprovement: 10,
maxIterationsPrRestart: 0,
maxFunctionEvaluations: 0,
alpha: 1,
gamma: 2,
rho: -0.5,
sigma: 0.5,
seed: 324,
maxDegreeOfParallelism: maxDegreeOfParallelism);
return(sut);
}
public override double Evaluate(IChromosome chromosome)
{
try
{
var parameters = Config.Genes.Select(s =>
new MinMaxParameterSpec(min: s.Min ?? s.Actual.Value, max: s.Max ?? s.Actual.Value,
transform: Transform.Linear, parameterType: s.Precision > 0 ? ParameterType.Continuous : ParameterType.Discrete)
).ToArray();
IOptimizer optimizer = null;
if (Config.Fitness != null)
{
if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.RandomSearch.ToString())
{
optimizer = new RandomSearchOptimizer(parameters, iterations: Config.Generations, seed: Seed, maxDegreeOfParallelism: Config.MaxThreads);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.ParticleSwarm.ToString())
{
optimizer = new ParticleSwarmOptimizer(parameters, maxIterations: Config.Generations, numberOfParticles: Config.PopulationSize,
seed: Seed, maxDegreeOfParallelism: Config.MaxThreads);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Bayesian.ToString())
{
optimizer = new BayesianOptimizer(parameters: parameters, iterations: Config.Generations, randomStartingPointCount: Config.PopulationSize,
functionEvaluationsPerIterationCount: Config.PopulationSize, seed: Seed);
//optimizer = new BayesianOptimizer(parameters, iterations: Config.Generations, randomStartingPointCount: Config.PopulationSize,
// functionEvaluationsPerIteration: Config.MaxThreads, seed: 42, maxDegreeOfParallelism: Config.MaxThreads, allowMultipleEvaluations: true);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GlobalizedBoundedNelderMead.ToString())
{
optimizer = new GlobalizedBoundedNelderMeadOptimizer(parameters, maxRestarts: Config.Generations,
maxIterationsPrRestart: Config.PopulationSize, seed: Seed, maxDegreeOfParallelism: Config.MaxThreads);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Smac.ToString())
{
optimizer = new SmacOptimizer(parameters, iterations: Config.Generations, randomStartingPointCount: Config.PopulationSize, seed: 42,
functionEvaluationsPerIterationCount: Config.MaxThreads);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.GridSearch.ToString())
{
optimizer = new GridSearchOptimizer(parameters);
}
else if (Config.Fitness.OptimizerTypeName == Enums.OptimizerTypeOptions.Genetic.ToString())
{
throw new Exception("Genetic optimizer cannot be used with Sharpe Maximizer");
}
}
else
{
throw new ArgumentException("No fitness section was configured.");
}
Func <double[], OptimizerResult> minimize = p => Minimize(p, (Chromosome)chromosome);
var result = optimizer.OptimizeBest(minimize);
Best = MergeFromResult(result, chromosome);
return(result.Error);
}
catch (Exception ex)
{
LogProvider.ErrorLogger.Error(ex);
return(ErrorFitness);
}
}
