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| #include <ensmallen.hpp> | ||
| #include "catch.hpp" | ||
| #include "test_function_tools.hpp" | ||
| #include <ensmallen_bits/cmaes/pop_cmaes.hpp> | ||
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| using namespace ens; | ||
| using namespace ens::test; | ||
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| /** | ||
| * @file ipop_cmaes_impl.hpp | ||
| * @author Marcus Edel | ||
| * @author Benjami Parellada | ||
| * | ||
| * Implementation of the IPOP Covariance Matrix Adaptation Evolution Strategy | ||
| * as proposed by A. Auger and N. Hansen in "A Restart CMA Evolution | ||
| * Strategy With Increasing Population Size" and BIPOP Covariance Matrix | ||
| * Adaptation Evolution Strategy as proposed by N. Hansen in "Benchmarking | ||
| * a BI-population CMA-ES on the BBOB-2009 function testbed". | ||
| * | ||
| * ensmallen is free software; you may redistribute it and/or modify it under | ||
| * the terms of the 3-clause BSD license. You should have received a copy of | ||
| * the 3-clause BSD license along with ensmallen. If not, see | ||
| * http://www.opensource.org/licenses/BSD-3-Clause for more information. | ||
| * Run IPOP-CMA-ES on the Rastrigin function and check whether the optimizer | ||
| * converges to the expected solution within tolerance limits. | ||
| */ | ||
| #ifndef ENSMALLEN_CMAES_POP_CMAES_IMPL_HPP | ||
| #define ENSMALLEN_CMAES_POP_CMAES_IMPL_HPP | ||
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| #include "pop_cmaes.hpp" | ||
| #include <ensmallen_bits/function.hpp> | ||
| #include <random> | ||
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| namespace ens { | ||
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| template<typename SelectionPolicyType, typename TransformationPolicyType, bool UseBIPOPFlag> | ||
| POP_CMAES<SelectionPolicyType, | ||
| TransformationPolicyType, | ||
| UseBIPOPFlag>::POP_CMAES( | ||
| const size_t lambda, | ||
| const TransformationPolicyType& transformationPolicy, | ||
| const size_t batchSize, | ||
| const size_t maxIterations, | ||
| const double tolerance, | ||
| const SelectionPolicyType& selectionPolicy, | ||
| double stepSize, | ||
| const double populationFactor, | ||
| const size_t maxRestarts, | ||
| const size_t maxFunctionEvaluations) : | ||
| CMAES<SelectionPolicyType, TransformationPolicyType>( | ||
| lambda, transformationPolicy, batchSize, maxIterations, | ||
| tolerance, selectionPolicy, stepSize), | ||
| populationFactor(populationFactor), | ||
| maxRestarts(maxRestarts), | ||
| maxFunctionEvaluations(maxFunctionEvaluations) | ||
| { /* Nothing to do. */ } | ||
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| template<typename SelectionPolicyType, | ||
| typename TransformationPolicyType, | ||
| bool UseBIPOPFlag> | ||
| template<typename SeparableFunctionType, typename MatType, typename... CallbackTypes> | ||
| typename MatType::elem_type POP_CMAES<SelectionPolicyType, | ||
| TransformationPolicyType, UseBIPOPFlag>::Optimize( | ||
| SeparableFunctionType& function, | ||
| MatType& iterateIn, | ||
| CallbackTypes&&... callbacks) | ||
| TEST_CASE("IPOPCMAESRastriginFunctionTest", "[POPCMAESTest]") | ||
| { | ||
| // Convenience typedefs. | ||
| typedef typename MatType::elem_type ElemType; | ||
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| StoreBestCoordinates<MatType> sbc; | ||
| StoreBestCoordinates<MatType> overallSBC; | ||
| size_t totalFunctionEvaluations = 0; | ||
| size_t largePopulationBudget = 0; | ||
| size_t smallPopulationBudget = 0; | ||
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| std::random_device rd; | ||
| std::mt19937 gen(rd()); | ||
| std::uniform_real_distribution<> dis(0.0, 1.0); | ||
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| // First single run with default population size | ||
| MatType iterate = iterateIn; | ||
| ElemType overallObjective = CMAES<SelectionPolicyType, | ||
| TransformationPolicyType>::Optimize(function, iterate, sbc, | ||
| callbacks...); | ||
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| overallSBC = sbc; | ||
| ElemType objective; | ||
| size_t evaluations; | ||
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| size_t defaultLambda = this->PopulationSize(); | ||
| size_t currentLargeLambda = defaultLambda; | ||
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| double stepSizeDefault = this->StepSize(); | ||
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| Info << "POP-CMA-ES: default population size (lambda): " << defaultLambda | ||
| << ", default step size (sigma): " << stepSizeDefault << std::endl; | ||
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| size_t restart = 0; | ||
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| while (restart < maxRestarts) | ||
| { | ||
| if (!UseBIPOPFlag || largePopulationBudget <= smallPopulationBudget || | ||
| restart == 0 || restart == maxRestarts - 1) | ||
| { | ||
| // Large population regime (IPOP or BIPOP) | ||
| currentLargeLambda *= populationFactor; | ||
| this->PopulationSize() = currentLargeLambda; | ||
| this->StepSize() = stepSizeDefault; | ||
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| Info << "POP-CMA-ES: restart " << restart << ", large population size" << | ||
| " (lambda): " << this->PopulationSize() << std::endl; | ||
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| iterate = iterateIn; | ||
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| // Optimize using the CMAES object. | ||
| objective = CMAES<SelectionPolicyType, | ||
| TransformationPolicyType>::Optimize(function, iterate, sbc, | ||
| callbacks...); | ||
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| evaluations = this->FunctionEvaluations(); | ||
| largePopulationBudget += evaluations; | ||
| } | ||
| else if (UseBIPOPFlag) | ||
| { | ||
| // Small population regime (BIPOP only) | ||
| double u = dis(gen); | ||
| size_t smallLambda = static_cast<size_t>(defaultLambda * std::pow(0.5 * | ||
| currentLargeLambda / defaultLambda, u * u)); | ||
| double stepSizeSmall = 2 * std::pow(10, -2*dis(gen)); | ||
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| this->PopulationSize() = smallLambda; | ||
| this->StepSize() = stepSizeSmall; | ||
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| Info << "POP-CMA-ES: restart " << restart << ", small population size" | ||
| << " (lambda): " << this->PopulationSize() << ", small step size" | ||
| << " (sigma): " << this->StepSize() << std::endl; | ||
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| iterate = iterateIn; | ||
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| // Optimize using the CMAES object. | ||
| objective = CMAES<SelectionPolicyType, | ||
| TransformationPolicyType>::Optimize(function, iterate, sbc, | ||
| callbacks...); | ||
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| evaluations = this->FunctionEvaluations(); | ||
| smallPopulationBudget += evaluations; | ||
| } | ||
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| if (objective < overallObjective) | ||
| { | ||
| overallObjective = objective; | ||
| overallSBC = sbc; | ||
| Info << "POP-CMA-ES: New best objective: " << overallObjective << | ||
| std::endl; | ||
| } | ||
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| totalFunctionEvaluations += evaluations; | ||
| // Check if the total number of evaluations has exceeded the limit | ||
| if (totalFunctionEvaluations >= maxFunctionEvaluations) { | ||
| Warn << "POP-CMA-ES: Maximum function overall evaluations reached. " | ||
| << "terminating optimization." << std::endl; | ||
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| Callback::EndOptimization(*this, function, iterate, callbacks...); | ||
| iterateIn = overallSBC.BestCoordinates(); | ||
| return overallSBC.BestObjective(); | ||
| } | ||
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| ++restart; | ||
| } | ||
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| Callback::EndOptimization(*this, function, iterate, callbacks...); | ||
| iterateIn = overallSBC.BestCoordinates(); | ||
| return overallSBC.BestObjective(); | ||
| RastriginFunction f(2); | ||
| BoundaryBoxConstraint<> b(-10, 10); | ||
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| IPOP_CMAES<FullSelection, BoundaryBoxConstraint<>> ipopcmaes( | ||
| 15, // lambda | ||
| b, // transformationPolicy | ||
| 32, // batchSize | ||
| 1000, // maxIterations | ||
| 1e-8, // tolerance | ||
| FullSelection(), // selectionPolicy | ||
| 3.72, // stepSize | ||
| 2.0, // populationFactor | ||
| 5, // maxRestarts | ||
| 1e4 // maxFunctionEvaluations | ||
| ); | ||
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| arma::mat initialPoint = f.GetInitialPoint(); | ||
| arma::mat expectedResult = f.GetFinalPoint(); | ||
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| MultipleTrialOptimizerTest(f, ipopcmaes, initialPoint, expectedResult, 0.01, f.GetFinalObjective(), 0.1, 5); | ||
| } | ||
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| } // namespace ens | ||
| /** | ||
| * Run BIPOP-CMA-ES on the Rastrigin function and check whether the optimizer | ||
| * converges to the expected solution within tolerance limits. | ||
| */ | ||
| TEST_CASE("BIPOPCMAESRastriginFunctionTest", "[POPCMAESTest]") | ||
| { | ||
| RastriginFunction f(2); | ||
| BoundaryBoxConstraint<> b(-10, 10); | ||
|
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| IPOP_CMAES<FullSelection, BoundaryBoxConstraint<>> ipopcmaes( | ||
| 15, // lambda | ||
| b, // transformationPolicy | ||
| 32, // batchSize | ||
| 1000, // maxIterations | ||
| 1e-8, // tolerance | ||
| FullSelection(), // selectionPolicy | ||
| 3.72, // stepSize | ||
| 2.0, // populationFactor | ||
| 5, // maxRestarts | ||
| 1e4 // maxFunctionEvaluations | ||
| ); | ||
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| arma::mat initialPoint = f.GetInitialPoint(); | ||
| arma::mat expectedResult = f.GetFinalPoint(); | ||
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| MultipleTrialOptimizerTest(f, ipopcmaes, initialPoint, expectedResult, 0.01, f.GetFinalObjective(), 0.1, 5); | ||
| } | ||
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| #endif | ||
| /** | ||
| * Run IPOP-CMA-ES on the Rosenbrock function and check whether the optimizer | ||
| * converges to the expected solution within tolerance limits. | ||
| */ | ||
| TEST_CASE("IPOPCMAESRosenbrockFunctionTest", "[POPCMAESTest]") | ||
| { | ||
| BoundaryBoxConstraint<> b(0, 2); | ||
|
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| BIPOP_CMAES<FullSelection, BoundaryBoxConstraint<>> bipopcmaes( | ||
| 15, // lambda | ||
| b, // transformationPolicy | ||
| 32, // batchSize | ||
| 1000, // maxIterations | ||
| 1e-8, // tolerance | ||
| FullSelection(), // selectionPolicy | ||
| 0.25, // stepSize | ||
| 1.5, // populationFactor | ||
| 5, // maxRestarts | ||
| 1e4 // maxFunctionEvaluations | ||
| ); | ||
|
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| FunctionTest<RosenbrockFunction>(bipopcmaes, 0.1, 0.1); | ||
| } | ||
|
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| /** | ||
| * Run BIPOP-CMA-ES on the Rosenbrock function and check whether the optimizer | ||
| * converges to the expected solution within tolerance limits. | ||
| */ | ||
| TEST_CASE("BIPOPCMAESRosenbrockFunctionTest", "[POPCMAESTest]") | ||
| { | ||
| BoundaryBoxConstraint<> b(0, 2); | ||
|
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| BIPOP_CMAES<FullSelection, BoundaryBoxConstraint<>> bipopcmaes( | ||
| 15, // lambda | ||
| b, // transformationPolicy | ||
| 32, // batchSize | ||
| 1000, // maxIterations | ||
| 1e-8, // tolerance | ||
| FullSelection(), // selectionPolicy | ||
| 0.25, // stepSize | ||
| 1.5, // populationFactor | ||
| 5, // maxRestarts | ||
| 1e4 // maxFunctionEvaluations | ||
| ); | ||
|
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| FunctionTest<RosenbrockFunction>(bipopcmaes, 0.1, 0.1); | ||
| } | ||
|
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| /** | ||
| * Run IPOP-CMA-ES with the full selection policy on logistic regression and | ||
| * make sure the results are acceptable. | ||
| */ | ||
| TEST_CASE("IPOPCMAESLogisticRegressionTest", "[POPCMAESTest]") | ||
| { | ||
| BoundaryBoxConstraint<> b(-10, 10); | ||
| IPOP_CMAES<FullSelection, BoundaryBoxConstraint<>> cmaes(0, b, 32, 500, 1e-3, FullSelection(), 0.6, 2.0, 5, 1e7); | ||
| LogisticRegressionFunctionTest(cmaes, 0.003, 0.006, 5); | ||
| } | ||
|
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| /** | ||
| * Run BIPOP-CMA-ES with the random selection policy on logistic regression and | ||
| * make sure the results are acceptable. | ||
| */ | ||
| TEST_CASE("BIPOPCMAESLogisticRegressionTest", "[POPCMAESTest]") | ||
| { | ||
| BoundaryBoxConstraint<> b(-10, 10); | ||
| BIPOP_CMAES<FullSelection, BoundaryBoxConstraint<>> cmaes(0, b, 32, 500, 1e-3, FullSelection(), 0.6, 2.0, 5, 1e7); | ||
| LogisticRegressionFunctionTest(cmaes, 0.003, 0.006, 5); | ||
| } |