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fix #41 (issue with ceres optimizer)
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@foolnotion
foolnotion committed Nov 27, 2024
1 parent 6402e86 commit c0de4de
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Showing 3 changed files with 54 additions and 141 deletions.
4 changes: 3 additions & 1 deletion include/operon/optimizer/dynamic_cost_function.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -5,11 +5,13 @@
#define OPERON_OPTIMIZER_COST_FUNCTION_HPP

#ifdef HAVE_CERES
#include <ceres/dynamic_cost_function.h>
#include <ceres/ceres.h>
#ifndef CERES_EXPORT
#define CERES_EXPORT OPERON_EXPORT
#endif

#include <ceres/ceres.h>

#include "operon/core/contracts.hpp"

namespace Operon {
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81 changes: 51 additions & 30 deletions include/operon/optimizer/optimizer.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -151,7 +151,7 @@ struct LevenbergMarquardtOptimizer<DTable, OptimizerType::Eigen> final : public
Operon::Interpreter<Operon::Scalar, DTable> interpreter{dtable, dataset, tree};
Operon::LMCostFunction<Operon::Scalar> cf{interpreter, target, range};
Eigen::LevenbergMarquardt<decltype(cf)> lm(cf);
lm.setMaxfev(static_cast<int>(iterations+1));
lm.setMaxfev(static_cast<int>(iterations));

auto x0 = tree.GetCoefficients();
OptimizerSummary summary;
Expand Down Expand Up @@ -195,50 +195,71 @@ struct LevenbergMarquardtOptimizer<DTable, OptimizerType::Eigen> final : public
};

#if defined(HAVE_CERES)
template <typename T = Operon::Scalar>
struct NonlinearLeastSquaresOptimizer<T, OptimizerType::Ceres> : public OptimizerBase<T> {
explicit NonlinearLeastSquaresOptimizer(InterpreterBase<T>& interpreter)
: OptimizerBase<T>{interpreter}
template <typename DTable>
struct LevenbergMarquardtOptimizer<DTable, OptimizerType::Ceres> final : public OptimizerBase {
explicit LevenbergMarquardtOptimizer(DTable const& dtable, Problem const& problem)
: OptimizerBase{problem}, dtable_{dtable}
{
}

auto Optimize(Operon::Span<Operon::Scalar const> target, Range range, size_t iterations, OptimizerSummary& summary) -> std::vector<Operon::Scalar> final
[[nodiscard]] auto Optimize(Operon::RandomGenerator& /*unused*/, Operon::Tree const& tree) const -> OptimizerSummary final
{
auto const& tree = this->GetTree();
auto const& ds = this->GetDataset();
auto const& dt = this->GetDispatchTable();

auto x0 = tree.GetCoefficients();
auto const& dtable = this->GetDispatchTable();
auto const& problem = this->GetProblem();
auto const& dataset = problem.GetDataset();
auto range = problem.TrainingRange();
auto target = problem.TargetValues(range);
auto iterations = this->Iterations();;

Operon::CostFunction<DTable, Eigen::RowMajor> cf(tree, ds, target, range, dt);
auto costFunction = new Operon::DynamicCostFunction(cf); // NOLINT
auto initialParameters = tree.GetCoefficients();
auto finalParameters = initialParameters;

Operon::Interpreter<Operon::Scalar, DTable> interpreter{dtable, dataset, tree};
Operon::LMCostFunction<Operon::Scalar, Eigen::RowMajor> cf{interpreter, target, range};
auto* dynamicCostFunction = new Operon::DynamicCostFunction{cf};
ceres::Solver::Summary s;
if (!x0.empty()) {
Eigen::Map<Eigen::Matrix<Operon::Scalar, -1, 1>> m0(x0.data(), std::ssize(x0));
auto sz = static_cast<Eigen::Index>(x0.size());
Eigen::VectorXd params = Eigen::Map<Eigen::Matrix<Operon::Scalar, -1, 1>>(x0.data(), sz).template cast<double>();
if (!initialParameters.empty()) {
auto sz = std::ssize(finalParameters);
Eigen::Map<Eigen::Matrix<Operon::Scalar, -1, 1>> m0(finalParameters.data(), sz);
Eigen::VectorXd params = m0.template cast<double>();
ceres::Problem problem;
problem.AddResidualBlock(costFunction, nullptr, params.data());
problem.AddResidualBlock(dynamicCostFunction, nullptr, params.data());
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_QR;
options.logging_type = ceres::LoggingType::SILENT;
options.max_num_iterations = static_cast<int>(iterations - 1); // workaround since for some reason ceres sometimes does 1 more iteration
options.max_num_iterations = static_cast<int>(iterations);
options.minimizer_progress_to_stdout = false;
options.num_threads = 1;
options.trust_region_strategy_type = ceres::LEVENBERG_MARQUARDT;
options.use_inner_iterations = false;
Solve(options, &problem, &s);
m0 = params.cast<Operon::Scalar>();
}
summary.InitialCost = s.initial_cost;
summary.FinalCost = s.final_cost;
summary.Iterations = static_cast<int>(s.iterations.size());
summary.FunctionEvaluations = s.num_residual_evaluations;
summary.JacobianEvaluations = s.num_jacobian_evaluations;
summary.Success = detail::CheckSuccess(summary.InitialCost, summary.FinalCost);
return x0;
return Operon::OptimizerSummary {
.InitialParameters = initialParameters,
.FinalParameters = finalParameters,
.InitialCost = static_cast<Operon::Scalar>(s.initial_cost),
.FinalCost = static_cast<Operon::Scalar>(s.final_cost),
.Iterations = static_cast<int>(s.iterations.size()),
.FunctionEvaluations = s.num_residual_evaluations,
.JacobianEvaluations = s.num_jacobian_evaluations,
.Success = detail::CheckSuccess(s.initial_cost, s.final_cost)
};
}

auto GetDispatchTable() const -> DTable const& { return dtable_.get(); }

[[nodiscard]] auto ComputeLikelihood(Operon::Span<Operon::Scalar const> x, Operon::Span<Operon::Scalar const> y, Operon::Span<Operon::Scalar const> w) const -> Operon::Scalar final
{
return GaussianLikelihood<Operon::Scalar>::ComputeLikelihood(x, y, w);
}

[[nodiscard]] auto ComputeFisherMatrix(Operon::Span<Operon::Scalar const> pred, Operon::Span<Operon::Scalar const> jac, Operon::Span<Operon::Scalar const> sigma) const -> Eigen::Matrix<Operon::Scalar, -1, -1> final {
return GaussianLikelihood<Operon::Scalar>::ComputeFisherMatrix(pred, jac, sigma);
}

private:
std::reference_wrapper<DTable const> dtable_;
};
#endif

Expand Down Expand Up @@ -298,12 +319,12 @@ struct LBFGSOptimizer final : public OptimizerBase {

auto GetDispatchTable() const -> DTable const& { return dtable_.get(); }

[[nodiscard]] virtual auto ComputeLikelihood(Operon::Span<Operon::Scalar const> x, Operon::Span<Operon::Scalar const> y, Operon::Span<Operon::Scalar const> w) const -> Operon::Scalar
[[nodiscard]] auto ComputeLikelihood(Operon::Span<Operon::Scalar const> x, Operon::Span<Operon::Scalar const> y, Operon::Span<Operon::Scalar const> w) const -> Operon::Scalar override
{
return LossFunction::ComputeLikelihood(x, y, w);
}

[[nodiscard]] virtual auto ComputeFisherMatrix(Operon::Span<Operon::Scalar const> pred, Operon::Span<Operon::Scalar const> jac, Operon::Span<Operon::Scalar const> sigma) const -> Eigen::Matrix<Operon::Scalar, -1, -1> final {
[[nodiscard]] auto ComputeFisherMatrix(Operon::Span<Operon::Scalar const> pred, Operon::Span<Operon::Scalar const> jac, Operon::Span<Operon::Scalar const> sigma) const -> Eigen::Matrix<Operon::Scalar, -1, -1> final {
return LossFunction::ComputeFisherMatrix(pred, jac, sigma);
}

Expand Down Expand Up @@ -371,12 +392,12 @@ struct SGDOptimizer final : public OptimizerBase {
return summary;
}

[[nodiscard]] virtual auto ComputeLikelihood(Operon::Span<Operon::Scalar const> x, Operon::Span<Operon::Scalar const> y, Operon::Span<Operon::Scalar const> w) const -> Operon::Scalar
[[nodiscard]] auto ComputeLikelihood(Operon::Span<Operon::Scalar const> x, Operon::Span<Operon::Scalar const> y, Operon::Span<Operon::Scalar const> w) const -> Operon::Scalar override
{
return LossFunction::ComputeLikelihood(x, y, w);
}

[[nodiscard]] virtual auto ComputeFisherMatrix(Operon::Span<Operon::Scalar const> pred, Operon::Span<Operon::Scalar const> jac, Operon::Span<Operon::Scalar const> sigma) const -> Eigen::Matrix<Operon::Scalar, -1, -1> final {
[[nodiscard]] auto ComputeFisherMatrix(Operon::Span<Operon::Scalar const> pred, Operon::Span<Operon::Scalar const> jac, Operon::Span<Operon::Scalar const> sigma) const -> Eigen::Matrix<Operon::Scalar, -1, -1> final {
return LossFunction::ComputeFisherMatrix(pred, jac, sigma);
}

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110 changes: 0 additions & 110 deletions include/operon/optimizer/tiny_cost_function.hpp
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