EPInferenceMethod.cpp

Go to the documentation of this file.

/*
 * This program 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.
 *
 * Written (W) 2013 Roman Votyakov
 *
 * Based on ideas from GAUSSIAN PROCESS REGRESSION AND CLASSIFICATION Toolbox
 * Copyright (C) 2005-2013 by Carl Edward Rasmussen & Hannes Nickisch under the
 * FreeBSD License
 * http://www.gaussianprocess.org/gpml/code/matlab/doc/
 */

#include <shogun/machine/gp/EPInferenceMethod.h>

#ifdef HAVE_EIGEN3

#include <shogun/mathematics/Math.h>
#include <shogun/labels/RegressionLabels.h>
#include <shogun/features/CombinedFeatures.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/lib/DynamicArray.h>

#include <shogun/mathematics/eigen3.h>

using namespace shogun;
using namespace Eigen;

// try to use previously allocated memory for SGVector
#define CREATE_SGVECTOR(vec, len, sg_type) \
    { \
        if (!vec.vector || vec.vlen!=len) \
            vec=SGVector<sg_type>(len); \
    }

// try to use previously allocated memory for SGMatrix
#define CREATE_SGMATRIX(mat, rows, cols, sg_type) \
    { \
        if (!mat.matrix || mat.num_rows!=rows || mat.num_cols!=cols) \
            mat=SGMatrix<sg_type>(rows, cols); \
    }

CEPInferenceMethod::CEPInferenceMethod()
{
    init();
}

CEPInferenceMethod::CEPInferenceMethod(CKernel* kernel, CFeatures* features,
        CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model)
        : CInferenceMethod(kernel, features, mean, labels, model)
{
    init();
}

CEPInferenceMethod::~CEPInferenceMethod()
{
}

void CEPInferenceMethod::init()
{
    m_max_sweep=8;
    m_min_sweep=2;
    m_tol=1e-4;
}

float64_t CEPInferenceMethod::get_negative_log_marginal_likelihood()
{
    if (update_parameter_hash())
        update();

    return m_nlZ;
}

SGVector<float64_t> CEPInferenceMethod::get_alpha()
{
    if (update_parameter_hash())
        update();

    return SGVector<float64_t>(m_alpha);
}

SGMatrix<float64_t> CEPInferenceMethod::get_cholesky()
{
    if (update_parameter_hash())
        update();

    return SGMatrix<float64_t>(m_L);
}

SGVector<float64_t> CEPInferenceMethod::get_diagonal_vector()
{
    if (update_parameter_hash())
        update();

    return SGVector<float64_t>(m_sttau);
}

SGVector<float64_t> CEPInferenceMethod::get_posterior_mean()
{
    if (update_parameter_hash())
        update();

    return SGVector<float64_t>(m_mu);
}

SGMatrix<float64_t> CEPInferenceMethod::get_posterior_covariance()
{
    if (update_parameter_hash())
        update();

    return SGMatrix<float64_t>(m_Sigma);
}

void CEPInferenceMethod::update()
{
    // update kernel and feature matrix
    CInferenceMethod::update();

    // get number of labels (trainig examples)
    index_t n=m_labels->get_num_labels();

    // try to use tilde values from previous call
    if (m_ttau.vlen==n)
    {
        update_chol();
        update_approx_cov();
        update_approx_mean();
        update_negative_ml();
    }

    // get mean vector
    SGVector<float64_t> mean=m_mean->get_mean_vector(m_features);

    // get and scale diagonal of the kernel matrix
    SGVector<float64_t> ktrtr_diag=m_ktrtr.get_diagonal_vector();
    ktrtr_diag.scale(CMath::sq(m_scale));

    // marginal likelihood for ttau = tnu = 0
    float64_t nlZ0=-SGVector<float64_t>::sum(m_model->get_log_zeroth_moments(
            mean, ktrtr_diag, m_labels));

    // use zero values if we have no better guess or it's better
    if (m_ttau.vlen!=n || m_nlZ>nlZ0)
    {
        CREATE_SGVECTOR(m_ttau, n, float64_t);
        m_ttau.zero();

        CREATE_SGVECTOR(m_sttau, n, float64_t);
        m_sttau.zero();

        CREATE_SGVECTOR(m_tnu, n, float64_t);
        m_tnu.zero();

        CREATE_SGMATRIX(m_Sigma, m_ktrtr.num_rows, m_ktrtr.num_cols, float64_t);

        // copy data manually, since we don't have appropriate method
        for (index_t i=0; i<m_ktrtr.num_rows; i++)
            for (index_t j=0; j<m_ktrtr.num_cols; j++)
                m_Sigma(i,j)=m_ktrtr(i,j)*CMath::sq(m_scale);

        CREATE_SGVECTOR(m_mu, n, float64_t);
        m_mu.zero();

        // set marginal likelihood
        m_nlZ=nlZ0;
    }

    // create vector of the random permutation
    SGVector<index_t> randperm=SGVector<index_t>::randperm_vec(n);

    // cavity tau and nu vectors
    SGVector<float64_t> tau_n(n);
    SGVector<float64_t> nu_n(n);

    // cavity mu and s2 vectors
    SGVector<float64_t> mu_n(n);
    SGVector<float64_t> s2_n(n);

    float64_t nlZ_old=CMath::INFTY;
    uint32_t sweep=0;

    while ((CMath::abs(m_nlZ-nlZ_old)>m_tol && sweep<m_max_sweep) ||
            sweep<m_min_sweep)
    {
        nlZ_old=m_nlZ;
        sweep++;

        // shuffle random permutation
        randperm.permute();

        for (index_t j=0; j<n; j++)
        {
            index_t i=randperm[j];

            // find cavity paramters
            tau_n[i]=1.0/m_Sigma(i,i)-m_ttau[i];
            nu_n[i]=m_mu[i]/m_Sigma(i,i)+mean[i]*tau_n[i]-m_tnu[i];

            // compute cavity mean and variance
            mu_n[i]=nu_n[i]/tau_n[i];
            s2_n[i]=1.0/tau_n[i];

            // get moments
            float64_t mu=m_model->get_first_moment(mu_n, s2_n, m_labels, i);
            float64_t s2=m_model->get_second_moment(mu_n, s2_n, m_labels, i);

            // save old value of ttau
            float64_t ttau_old=m_ttau[i];

            // compute ttau and sqrt(ttau)
            m_ttau[i]=CMath::max(1.0/s2-tau_n[i], 0.0);
            m_sttau[i]=CMath::sqrt(m_ttau[i]);

            // compute tnu
            m_tnu[i]=mu/s2-nu_n[i];

            // compute difference ds2=ttau_new-ttau_old
            float64_t ds2=m_ttau[i]-ttau_old;

            // create eigen representation of Sigma, tnu and mu
            Map<MatrixXd> eigen_Sigma(m_Sigma.matrix, m_Sigma.num_rows,
                    m_Sigma.num_cols);
            Map<VectorXd> eigen_tnu(m_tnu.vector, m_tnu.vlen);
            Map<VectorXd> eigen_mu(m_mu.vector, m_mu.vlen);

            VectorXd eigen_si=eigen_Sigma.col(i);

            // rank-1 update Sigma
            eigen_Sigma=eigen_Sigma-ds2/(1.0+ds2*eigen_si(i))*eigen_si*
                eigen_si.adjoint();

            // update mu
            eigen_mu=eigen_Sigma*eigen_tnu;
        }

        // update upper triangular factor (L^T) of Cholesky decomposition of
        // matrix B, approximate posterior covariance and mean, negative
        // marginal likelihood
        update_chol();
        update_approx_cov();
        update_approx_mean();
        update_negative_ml();
    }

    if (sweep==m_max_sweep)
        SG_WARNING("Maximum number of sweeps reached")

    // update vector alpha
    update_alpha();

    // update matrices to compute derivatives
    update_deriv();
}

void CEPInferenceMethod::update_alpha()
{
    // create eigen representations kernel matrix, L^T, sqrt(ttau) and tnu
    Map<MatrixXd> eigen_K(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);
    Map<VectorXd> eigen_tnu(m_tnu.vector, m_tnu.vlen);
    Map<VectorXd> eigen_sttau(m_sttau.vector, m_sttau.vlen);
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);

    // create shogun and eigen representation of the alpha vector
    CREATE_SGVECTOR(m_alpha, m_tnu.vlen, float64_t);
    Map<VectorXd> eigen_alpha(m_alpha.vector, m_alpha.vlen);

    // solve LL^T * v = tS^(1/2) * K * tnu
    VectorXd eigen_v=eigen_L.triangularView<Upper>().adjoint().solve(
            eigen_sttau.cwiseProduct(eigen_K*CMath::sq(m_scale)*eigen_tnu));
    eigen_v=eigen_L.triangularView<Upper>().solve(eigen_v);

    // compute alpha = (I - tS^(1/2) * B^(-1) * tS(1/2) * K) * tnu =
    // tnu - tS(1/2) * (L^T)^(-1) * L^(-1) * tS^(1/2) * K * tnu =
    // tnu - tS(1/2) * v
    eigen_alpha=eigen_tnu-eigen_sttau.cwiseProduct(eigen_v);
}

void CEPInferenceMethod::update_chol()
{
    // create eigen representations of kernel matrix and sqrt(ttau)
    Map<MatrixXd> eigen_K(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);
    Map<VectorXd> eigen_sttau(m_sttau.vector, m_sttau.vlen);

    // create shogun and eigen representation of the upper triangular factor
    // (L^T) of the Cholesky decomposition of the matrix B
    CREATE_SGMATRIX(m_L, m_ktrtr.num_rows, m_ktrtr.num_cols, float64_t);
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);

    // compute upper triangular factor L^T of the Cholesky decomposion of the
    // matrix: B = tS^(1/2) * K * tS^(1/2) + I
    LLT<MatrixXd> eigen_chol((eigen_sttau*eigen_sttau.adjoint()).cwiseProduct(
            eigen_K*CMath::sq(m_scale))+
            MatrixXd::Identity(m_L.num_rows, m_L.num_cols));

    eigen_L=eigen_chol.matrixU();
}

void CEPInferenceMethod::update_approx_cov()
{
    // create eigen representations of kernel matrix, L^T matrix and sqrt(ttau)
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);
    Map<MatrixXd> eigen_K(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);
    Map<VectorXd> eigen_sttau(m_sttau.vector, m_sttau.vlen);

    // create shogun and eigen representation of the approximate covariance
    // matrix
    CREATE_SGMATRIX(m_Sigma, m_ktrtr.num_rows, m_ktrtr.num_cols, float64_t);
    Map<MatrixXd> eigen_Sigma(m_Sigma.matrix, m_Sigma.num_rows, m_Sigma.num_cols);

    // compute V = L^(-1) * tS^(1/2) * K, using upper triangular factor L^T
    MatrixXd eigen_V=eigen_L.triangularView<Upper>().adjoint().solve(
            eigen_sttau.asDiagonal()*eigen_K*CMath::sq(m_scale));

    // compute covariance matrix of the posterior:
    // Sigma = K - K * tS^(1/2) * (L * L^T)^(-1) * tS^(1/2) * K =
    // K - (K * tS^(1/2)) * (L^T)^(-1) * L^(-1) * tS^(1/2) * K =
    // K - (tS^(1/2) * K)^T * (L^(-1))^T * L^(-1) * tS^(1/2) * K = K - V^T * V
    eigen_Sigma=eigen_K*CMath::sq(m_scale)-eigen_V.adjoint()*eigen_V;
}

void CEPInferenceMethod::update_approx_mean()
{
    // create eigen representation of posterior covariance matrix and tnu
    Map<MatrixXd> eigen_Sigma(m_Sigma.matrix, m_Sigma.num_rows, m_Sigma.num_cols);
    Map<VectorXd> eigen_tnu(m_tnu.vector, m_tnu.vlen);

    // create shogun and eigen representation of the approximate mean vector
    CREATE_SGVECTOR(m_mu, m_tnu.vlen, float64_t);
    Map<VectorXd> eigen_mu(m_mu.vector, m_mu.vlen);

    // compute mean vector of the approximate posterior: mu = Sigma * tnu
    eigen_mu=eigen_Sigma*eigen_tnu;
}

void CEPInferenceMethod::update_negative_ml()
{
    // create eigen representation of Sigma, L, mu, tnu, ttau
    Map<MatrixXd> eigen_Sigma(m_Sigma.matrix, m_Sigma.num_rows, m_Sigma.num_cols);
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);
    Map<VectorXd> eigen_mu(m_mu.vector, m_mu.vlen);
    Map<VectorXd> eigen_tnu(m_tnu.vector, m_tnu.vlen);
    Map<VectorXd> eigen_ttau(m_ttau.vector, m_ttau.vlen);

    // get and create eigen representation of the mean vector
    SGVector<float64_t> m=m_mean->get_mean_vector(m_features);
    Map<VectorXd> eigen_m(m.vector, m.vlen);

    // compute vector of cavity parameter tau_n
    VectorXd eigen_tau_n=(VectorXd::Ones(m_ttau.vlen)).cwiseQuotient(
            eigen_Sigma.diagonal())-eigen_ttau;

    // compute vector of cavity parameter nu_n
    VectorXd eigen_nu_n=eigen_mu.cwiseQuotient(eigen_Sigma.diagonal())-
        eigen_tnu+eigen_m.cwiseProduct(eigen_tau_n);

    // compute cavity mean: mu_n=nu_n/tau_n
    SGVector<float64_t> mu_n(m_ttau.vlen);
    Map<VectorXd> eigen_mu_n(mu_n.vector, mu_n.vlen);

    eigen_mu_n=eigen_nu_n.cwiseQuotient(eigen_tau_n);

    // compute cavity variance: s2_n=1.0/tau_n
    SGVector<float64_t> s2_n(m_ttau.vlen);
    Map<VectorXd> eigen_s2_n(s2_n.vector, s2_n.vlen);

    eigen_s2_n=(VectorXd::Ones(m_ttau.vlen)).cwiseQuotient(eigen_tau_n);

    float64_t lZ=SGVector<float64_t>::sum(
            m_model->get_log_zeroth_moments(mu_n, s2_n, m_labels));

    // compute nlZ_part1=sum(log(diag(L)))-sum(lZ)-tnu'*Sigma*tnu/2
    float64_t nlZ_part1=eigen_L.diagonal().array().log().sum()-lZ-
        (eigen_tnu.adjoint()*eigen_Sigma).dot(eigen_tnu)/2.0;

    // compute nlZ_part2=sum(tnu.^2./(tau_n+ttau))/2-sum(log(1+ttau./tau_n))/2
    float64_t nlZ_part2=(eigen_tnu.array().square()/
        (eigen_tau_n+eigen_ttau).array()).sum()/2.0-(1.0+eigen_ttau.array()/
        eigen_tau_n.array()).log().sum()/2.0;

    // compute nlZ_part3=-(nu_n-m.*tau_n)'*((ttau./tau_n.*(nu_n-m.*tau_n)-2*tnu)
    // ./(ttau+tau_n))/2
    float64_t nlZ_part3=-(eigen_nu_n-eigen_m.cwiseProduct(eigen_tau_n)).dot(
        ((eigen_ttau.array()/eigen_tau_n.array()*(eigen_nu_n.array()-
        eigen_m.array()*eigen_tau_n.array())-2*eigen_tnu.array())/
        (eigen_ttau.array()+eigen_tau_n.array())).matrix())/2.0;

    // compute nlZ=nlZ_part1+nlZ_part2+nlZ_part3
    m_nlZ=nlZ_part1+nlZ_part2+nlZ_part3;
}

void CEPInferenceMethod::update_deriv()
{
    // create eigen representation of L, sstau, alpha
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);
    Map<VectorXd> eigen_sttau(m_sttau.vector, m_sttau.vlen);
    Map<VectorXd> eigen_alpha(m_alpha.vector, m_alpha.vlen);

    // create shogun and eigen representation of F
    m_F=SGMatrix<float64_t>(m_L.num_rows, m_L.num_cols);
    Map<MatrixXd> eigen_F(m_F.matrix, m_F.num_rows, m_F.num_cols);

    // solve L*L^T * V = diag(sqrt(ttau))
    MatrixXd V=eigen_L.triangularView<Upper>().adjoint().solve(
            MatrixXd(eigen_sttau.asDiagonal()));
    V=eigen_L.triangularView<Upper>().solve(V);

    // compute F=alpha*alpha'-repmat(sW,1,n).*solve_chol(L,diag(sW))
    eigen_F=eigen_alpha*eigen_alpha.adjoint()-eigen_sttau.asDiagonal()*V;
}

SGVector<float64_t> CEPInferenceMethod::get_derivative_wrt_inference_method(
        const TParameter* param)
{
    REQUIRE(!strcmp(param->m_name, "scale"), "Can't compute derivative of "
            "the nagative log marginal likelihood wrt %s.%s parameter\n",
            get_name(), param->m_name)

    Map<MatrixXd> eigen_K(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);
    Map<MatrixXd> eigen_F(m_F.matrix, m_F.num_rows, m_F.num_cols);

    SGVector<float64_t> result(1);

    // compute derivative wrt kernel scale: dnlZ=-sum(F.*K*scale*2)/2
    result[0]=-(eigen_F.cwiseProduct(eigen_K)*m_scale*2.0).sum()/2.0;

    return result;
}

SGVector<float64_t> CEPInferenceMethod::get_derivative_wrt_likelihood_model(
        const TParameter* param)
{
    SG_NOTIMPLEMENTED
    return SGVector<float64_t>();
}

SGVector<float64_t> CEPInferenceMethod::get_derivative_wrt_kernel(
        const TParameter* param)
{
    // create eigen representation of the matrix Q
    Map<MatrixXd> eigen_F(m_F.matrix, m_F.num_rows, m_F.num_cols);

    SGVector<float64_t> result;

    if (param->m_datatype.m_ctype==CT_VECTOR ||
            param->m_datatype.m_ctype==CT_SGVECTOR)
    {
        REQUIRE(param->m_datatype.m_length_y,
                "Length of the parameter %s should not be NULL\n", param->m_name)
        result=SGVector<float64_t>(*(param->m_datatype.m_length_y));
    }
    else
    {
        result=SGVector<float64_t>(1);
    }

    for (index_t i=0; i<result.vlen; i++)
    {
        SGMatrix<float64_t> dK;

        if (result.vlen==1)
            dK=m_kernel->get_parameter_gradient(param);
        else
            dK=m_kernel->get_parameter_gradient(param, i);

        Map<MatrixXd> eigen_dK(dK.matrix, dK.num_rows, dK.num_cols);

        // compute derivative wrt kernel parameter: dnlZ=-sum(F.*dK*scale^2)/2.0
        result[i]=-(eigen_F.cwiseProduct(eigen_dK)*CMath::sq(m_scale)).sum()/2.0;
    }

    return result;
}

SGVector<float64_t> CEPInferenceMethod::get_derivative_wrt_mean(
        const TParameter* param)
{
    SG_NOTIMPLEMENTED
    return SGVector<float64_t>();
}

#endif /* HAVE_EIGEN3 */