learn_theta.hpp

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// SPDX-License-Identifier: BSD-2-Clause
 
#ifndef cMHN_TT_LEARN_THETA_H
#define cMHN_TT_LEARN_THETA_H
 
#include <iostream>
#include <map>
#include <string>
#include <tuple>
 
#include <cmhn/common/calculate_pTheta.hpp>
#include <cmhn/tt/als.hpp>
#include <cmhn/tt/mamen.hpp>
#include <cmhn/tt/mhn_operator.hpp>
#include <cmhn/tt/utility.hpp>
#include <cmhn/utility/read_data.hpp>
#include <cmhn/utility/read_header.hpp>
#include <cmhn/utility/write_theta.hpp>
 
#include <prc.hpp>
 
namespace cMHN::TT
{
    template<pRC::Size RP, pRC::Size RQ, class T, pRC::Size D, class S>
    std::tuple<pRC::Tensor<T, D, D>, std::map<std::string, std::string>,
        std::map<std::string, double>>
    learnTheta(pRC::Tensor<T, D, D> const &theta, std::string const &header,
        std::string const &output, std::map<S, T> const &pD,
        cMHN::Score<T> const &Score, cMHN::Regulator<T, D> const &Regulator,
        T const &toleranceOptimizer, T const &toleranceSolverP,
        T const &toleranceSolverQ)
    {
        using ModeSizes = decltype(getModeSizes<D>());
 
        auto tempTheta = theta;
 
        T score = pRC::zero();
 
        pRC::Index at_iter = 0;
 
        pRC::Float<64> startTime = pRC::getTimeInSeconds();
 
        std::map<std::string, double> logInfoNumbers{{"Score", score()},
            {"Iterations", at_iter},
            {"Time", pRC::getTimeInSeconds()() - startTime()},
            {"Lambda", Regulator.lambda()()}};
 
        std::map<std::string, std::string> logInfoNames{
            {"Score Name", Score.name()}, {"Regulator Name", Regulator.name()}};
 
        writeTheta(output, header, tempTheta, logInfoNames, logInfoNumbers);
 
        std::cout << "cMHN learning started (TT):" << std::endl;
        std::cout << "\tScore Name:\t" << logInfoNames["Score Name"]
                  << std::endl;
        std::cout << "\tRegulator Name:\t" << logInfoNames["Regulator Name"]
                  << std::endl
                  << std::endl;
 
        // use a random initial pInit
        pRC::SeedSequence seq(8, 16);
        pRC::DefaultRandomEngine rng(seq);
        pRC::GaussianDistribution<pRC::Float<>> dist;
        auto pInit = round<decltype(getRanks<D, RP>())>(
            pRC::random<pRC::TensorTrain::Tensor<T, ModeSizes,
                decltype(getRanks<D, RP>())>>(rng, dist));
        pInit = pInit /
            scalarProduct(pInit,
                pRC::unit<pRC::TensorTrain::Tensor<T, ModeSizes>>())();
 
        tempTheta = pRC::optimize<>(
            pRC::Optimizer::GradientDescent<>(), tempTheta,
            [&output, &at_iter, &score, &pD, &Score, &Regulator, &pInit,
                &toleranceOptimizer, &toleranceSolverP, &toleranceSolverQ](
                pRC::Tensor<T, D, D> const &tempTheta, pRC::Tensor<T, D, D> &g)
            {
                MHNOperator<T, D> op(tempTheta);
 
                std::tie(score, g) =
                    ::cMHN::calculateScoreAndGradient<RP, RQ>(op, pD, Score,
                        Regulator, pInit, toleranceSolverP, toleranceSolverQ);
 
                return score;
            },
            [&output, &header, &score, &at_iter, &startTime, &logInfoNames,
                &logInfoNumbers](auto const &tempTheta)
            {
                at_iter++;
                logInfoNumbers["Iterations"] = at_iter;
                logInfoNumbers["Score"] = score();
                logInfoNumbers["Time"] =
                    pRC::getTimeInSeconds()() - startTime();
 
                std::cout << "cMHN learning in progress (TT):" << std::endl;
                std::cout << std::defaultfloat;
                std::cout << "\tIteration:\t" << logInfoNumbers["Iterations"]
                          << std::endl;
                std::cout << std::scientific;
                std::cout << "\tLambda:\t\t" << logInfoNumbers["Lambda"]
                          << std::endl;
                std::cout << "\tScore:\t\t" << logInfoNumbers["Score"]
                          << std::endl;
                std::cout << "\tTime:\t\t" << logInfoNumbers["Time"]
                          << std::endl;
                std::cout << std::defaultfloat;
 
                writeTheta(output, header, tempTheta, logInfoNames,
                    logInfoNumbers);
            },
            toleranceOptimizer);
 
        return std::make_tuple(tempTheta, logInfoNames, logInfoNumbers);
    }
}
 
#endif // cMHN_TT_LEARN_THETA_H