MultiLaplacianInferenceMethod.cpp

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Wu Lin
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 *    list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 * The views and conclusions contained in the software and documentation are those
 * of the authors and should not be interpreted as representing official policies,
 * either expressed or implied, of the Shogun Development Team.
 *
 *
 * Code adapted from
 * https://gist.github.com/yorkerlin/14ace49f2278f3859614
 * Gaussian Process Machine Learning Toolbox
 * http://www.gaussianprocess.org/gpml/code/matlab/doc/
 * and
 * GPstuff - Gaussian process models for Bayesian analysis
 * http://becs.aalto.fi/en/research/bayes/gpstuff/
 *
 * The reference pseudo code is the algorithm 3.3 of the GPML textbook
 */

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

#ifdef HAVE_EIGEN3
#include <shogun/mathematics/eigen3.h>
#include <shogun/labels/MulticlassLabels.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/external/brent.h>

using namespace shogun;
using namespace Eigen;

namespace shogun
{

#ifndef DOXYGEN_SHOULD_SKIP_THIS

class CMultiPsiLine : public func_base
{
public:
    float64_t log_scale;
    MatrixXd K;
    VectorXd dalpha;
    VectorXd start_alpha;
    Map<VectorXd>* alpha;
    SGVector<float64_t>* dlp;
    SGVector<float64_t>* f;
    SGVector<float64_t>* m;
    CLikelihoodModel* lik;
    CLabels* lab;

    virtual double operator() (double x)
    {
        const index_t C=((CMulticlassLabels*)lab)->get_num_classes();
        const index_t n=((CMulticlassLabels*)lab)->get_num_labels();
        Map<VectorXd> eigen_f(f->vector, f->vlen);
        Map<VectorXd> eigen_m(m->vector, m->vlen);

        // compute alpha=alpha+x*dalpha and f=K*alpha+m
        (*alpha)=start_alpha+x*dalpha;

        float64_t result=0;
        for(index_t bl=0; bl<C; bl++)
        {
            eigen_f.block(bl*n,0,n,1)=K*alpha->block(bl*n,0,n,1)*CMath::exp(log_scale*2.0);
            result+=alpha->block(bl*n,0,n,1).dot(eigen_f.block(bl*n,0,n,1))/2.0;
            eigen_f.block(bl*n,0,n,1)+=eigen_m;
        }

        // get first and second derivatives of log likelihood
        (*dlp)=lik->get_log_probability_derivative_f(lab, (*f), 1);

        result -=SGVector<float64_t>::sum(lik->get_log_probability_f(lab, *f));

        return result;
    }
};

#endif /* DOXYGEN_SHOULD_SKIP_THIS */

CMultiLaplacianInferenceMethod::CMultiLaplacianInferenceMethod() : CLaplacianInferenceBase()
{
    init();
}

CMultiLaplacianInferenceMethod::CMultiLaplacianInferenceMethod(CKernel* kern,
        CFeatures* feat, CMeanFunction* m, CLabels* lab, CLikelihoodModel* mod)
        : CLaplacianInferenceBase(kern, feat, m, lab, mod)
{
    init();
}

void CMultiLaplacianInferenceMethod::init()
{
    m_nlz=0;
    SG_ADD(&m_nlz, "nlz", "negative log marginal likelihood ", MS_NOT_AVAILABLE);
    SG_ADD(&m_U, "U", "the matrix used to compute gradient wrt hyperparameters", MS_NOT_AVAILABLE);
}

CMultiLaplacianInferenceMethod::~CMultiLaplacianInferenceMethod()
{
}

void CMultiLaplacianInferenceMethod::check_members() const
{
    CInferenceMethod::check_members();

    REQUIRE(m_labels->get_label_type()==LT_MULTICLASS,
        "Labels must be type of CMulticlassLabels\n");
    REQUIRE(m_model->supports_multiclass(),
        "likelihood model should support multi-classification\n");
}

SGVector<float64_t> CMultiLaplacianInferenceMethod::get_diagonal_vector()
{
    if (parameter_hash_changed())
        update();

    get_dpi_helper();

    return SGVector<float64_t>(m_W);
}

float64_t CMultiLaplacianInferenceMethod::get_negative_log_marginal_likelihood()
{
    if (parameter_hash_changed())
        update();

    return m_nlz;
}

SGVector<float64_t> CMultiLaplacianInferenceMethod::get_derivative_wrt_likelihood_model(
        const TParameter* param)
{
    //SoftMax likelihood does not have this kind of derivative
    SG_ERROR("Not Implemented!\n");
    return SGVector<float64_t> ();
}

CMultiLaplacianInferenceMethod* CMultiLaplacianInferenceMethod::obtain_from_generic(
        CInferenceMethod* inference)
{
    if (inference==NULL)
        return NULL;

    if (inference->get_inference_type()!=INF_LAPLACIAN_MULTIPLE)
        SG_SERROR("Provided inference is not of type CMultiLaplacianInferenceMethod!\n")

    SG_REF(inference);
    return (CMultiLaplacianInferenceMethod*)inference;
}


void CMultiLaplacianInferenceMethod::update_approx_cov()
{
    //Sigma=K-K*(E-E*R(M*M')^{-1}*R'*E)*K
    const index_t C=((CMulticlassLabels*)m_labels)->get_num_classes();
    const index_t n=m_labels->get_num_labels();
    Map<MatrixXd> eigen_M(m_L.matrix, m_L.num_rows, m_L.num_cols);
    Map<MatrixXd> eigen_E(m_E.matrix, m_E.num_rows, m_E.num_cols);
    Map<MatrixXd> eigen_K(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);

    m_Sigma=SGMatrix<float64_t>(C*n, C*n);
    Map<MatrixXd> eigen_Sigma(m_Sigma.matrix, m_Sigma.num_rows, m_Sigma.num_cols);
    eigen_Sigma.fill(0);

    MatrixXd eigen_U(C*n,n);
    for(index_t bl=0; bl<C; bl++)
    {
        eigen_U.block(bl*n,0,n,n)=eigen_K*CMath::exp(m_log_scale*2.0)*eigen_E.block(0,bl*n,n,n);
        eigen_Sigma.block(bl*n,bl*n,n,n)=(MatrixXd::Identity(n,n)-eigen_U.block(bl*n,0,n,n))*(eigen_K*CMath::exp(m_log_scale*2.0));
    }
    MatrixXd eigen_V=eigen_M.triangularView<Upper>().adjoint().solve(eigen_U.transpose());
    eigen_Sigma+=eigen_V.transpose()*eigen_V;
}

void CMultiLaplacianInferenceMethod::update_chol()
{
}

void CMultiLaplacianInferenceMethod::get_dpi_helper()
{
    const index_t C=((CMulticlassLabels*)m_labels)->get_num_classes();
    const index_t n=m_labels->get_num_labels();
    Map<VectorXd> eigen_dpi(m_W.vector, m_W.vlen);
    Map<MatrixXd> eigen_dpi_matrix(eigen_dpi.data(),n,C);

    Map<VectorXd> eigen_mu(m_mu, m_mu.vlen);
    Map<MatrixXd> eigen_mu_matrix(eigen_mu.data(),n,C);
    // with log_sum_exp trick
    VectorXd max_coeff=eigen_mu_matrix.rowwise().maxCoeff();
    eigen_dpi_matrix=eigen_mu_matrix.array().colwise()-max_coeff.array();
    VectorXd log_sum_exp=((eigen_dpi_matrix.array().exp()).rowwise().sum()).array().log();
    eigen_dpi_matrix=(eigen_dpi_matrix.array().colwise()-log_sum_exp.array()).exp();

    // without log_sum_exp trick
    //eigen_dpi_matrix=eigen_mu_matrix.array().exp();
    //VectorXd tmp_for_dpi=eigen_dpi_matrix.rowwise().sum();
    //eigen_dpi_matrix=eigen_dpi_matrix.array().colwise()/tmp_for_dpi.array();
}

void CMultiLaplacianInferenceMethod::update_alpha()
{
    float64_t Psi_Old = CMath::INFTY;
    float64_t Psi_New;
    float64_t Psi_Def;
    const index_t C=((CMulticlassLabels*)m_labels)->get_num_classes();
    const index_t n=m_labels->get_num_labels();

    // get mean vector and create eigen representation of it
    SGVector<float64_t> mean=m_mean->get_mean_vector(m_features);
    Map<VectorXd> eigen_mean_bl(mean.vector, mean.vlen);
    VectorXd eigen_mean=eigen_mean_bl.replicate(C,1);

    // create eigen representation of kernel matrix
    Map<MatrixXd> eigen_ktrtr(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);

    // create shogun and eigen representation of function vector
    m_mu=SGVector<float64_t>(mean.vlen*C);
    Map<VectorXd> eigen_mu(m_mu, m_mu.vlen);

    // f = mean as default value
    eigen_mu=eigen_mean;

    Psi_Def=-SGVector<float64_t>::sum(m_model->get_log_probability_f(
            m_labels, m_mu));

    if (m_alpha.vlen!=C*n)
    {
        // set alpha a zero vector
        m_alpha=SGVector<float64_t>(C*n);
        m_alpha.zero();
        Psi_New=Psi_Def;
        m_E=SGMatrix<float64_t>(n,C*n);
        m_L=SGMatrix<float64_t>(n,n);
        m_W=SGVector<float64_t>(C*n);
    }
    else
    {
        Map<VectorXd> alpha(m_alpha.vector, m_alpha.vlen);
        for(index_t bl=0; bl<C; bl++)
            eigen_mu.block(bl*n,0,n,1)=eigen_ktrtr*CMath::exp(m_log_scale*2.0)*alpha.block(bl*n,0,n,1);

        //alpha'*(f-m)/2.0
        Psi_New=alpha.dot(eigen_mu)/2.0;
        // compute f = K * alpha + m
        eigen_mu+=eigen_mean;

        Psi_New-=SGVector<float64_t>::sum(m_model->get_log_probability_f(m_labels, m_mu));

        // if default is better, then use it
        if (Psi_Def < Psi_New)
        {
            m_alpha.zero();
            eigen_mu=eigen_mean;
            Psi_New=Psi_Def;
        }
    }

    Map<VectorXd> eigen_alpha(m_alpha.vector, m_alpha.vlen);
    Map<VectorXd> eigen_W(m_W.vector, m_W.vlen);
    Map<MatrixXd> eigen_L(m_L.matrix, m_L.num_rows, m_L.num_cols);
    Map<MatrixXd> eigen_E(m_E.matrix, m_E.num_rows, m_E.num_cols);

    // get first derivative of log probability function
    m_dlp=m_model->get_log_probability_derivative_f(m_labels, m_mu, 1);

    index_t iter=0;
    Map<MatrixXd> & eigen_M=eigen_L;

    while (Psi_Old-Psi_New>m_tolerance && iter<m_iter)
    {
        Map<VectorXd> eigen_dlp(m_dlp.vector, m_dlp.vlen);

        get_dpi_helper();
        Map<VectorXd> eigen_dpi(m_W.vector, m_W.vlen);


        Psi_Old = Psi_New;
        iter++;

        m_nlz=0;

        for(index_t bl=0; bl<C; bl++)
        {
            VectorXd eigen_sD=eigen_dpi.block(bl*n,0,n,1).cwiseSqrt();
            LLT<MatrixXd> chol_tmp((eigen_sD*eigen_sD.transpose()).cwiseProduct(eigen_ktrtr*CMath::exp(m_log_scale*2.0))+
                MatrixXd::Identity(m_ktrtr.num_rows, m_ktrtr.num_cols));
            MatrixXd eigen_L_tmp=chol_tmp.matrixU();
            MatrixXd eigen_E_bl=eigen_L_tmp.triangularView<Upper>().adjoint().solve(MatrixXd(eigen_sD.asDiagonal()));
            eigen_E_bl=eigen_E_bl.transpose()*eigen_E_bl;
            eigen_E.block(0,bl*n,n,n)=eigen_E_bl;
            if (bl==0)
                eigen_M=eigen_E_bl;
            else
                eigen_M+=eigen_E_bl;
            m_nlz+=eigen_L_tmp.diagonal().array().log().sum();
        }

        LLT<MatrixXd> chol_tmp(eigen_M);
        eigen_M = chol_tmp.matrixU();
        m_nlz+=eigen_M.diagonal().array().log().sum();

        VectorXd eigen_b=eigen_dlp;
        Map<VectorXd> & tmp1=eigen_dlp;
        tmp1=eigen_dpi.array()*(eigen_mu-eigen_mean).array();
        Map<MatrixXd> m_tmp(tmp1.data(),n,C);
        VectorXd tmp2=m_tmp.array().rowwise().sum();

        for(index_t bl=0; bl<C; bl++)
            eigen_b.block(bl*n,0,n,1)+=eigen_dpi.block(bl*n,0,n,1).cwiseProduct(eigen_mu.block(bl*n,0,n,1)-eigen_mean_bl-tmp2);

        Map<VectorXd> &eigen_c=eigen_W;
        for(index_t bl=0; bl<C; bl++)
            eigen_c.block(bl*n,0,n,1)=eigen_E.block(0,bl*n,n,n)*(eigen_ktrtr*CMath::exp(m_log_scale*2.0)*eigen_b.block(bl*n,0,n,1));

        Map<MatrixXd> c_tmp(eigen_c.data(),n,C);

        VectorXd tmp3=c_tmp.array().rowwise().sum();
        VectorXd tmp4=eigen_M.triangularView<Upper>().adjoint().solve(tmp3);

        VectorXd &eigen_dalpha=eigen_b;
        eigen_dalpha+=eigen_E.transpose()*(eigen_M.triangularView<Upper>().solve(tmp4))-eigen_c-eigen_alpha;

        // perform Brent's optimization
        CMultiPsiLine func;

        func.log_scale=m_log_scale;
        func.K=eigen_ktrtr;
        func.dalpha=eigen_dalpha;
        func.start_alpha=eigen_alpha;
        func.alpha=&eigen_alpha;
        func.dlp=&m_dlp;
        func.f=&m_mu;
        func.m=&mean;
        func.lik=m_model;
        func.lab=m_labels;

        float64_t x;
        Psi_New=local_min(0, m_opt_max, m_opt_tolerance, func, x);
        m_nlz+=Psi_New;
    }

    if (Psi_Old-Psi_New>m_tolerance && iter>=m_iter)
    {
        SG_WARNING("Max iterations (%d) reached, but convergence level (%f) is not yet below tolerance (%f)\n", m_iter, Psi_Old-Psi_New, m_tolerance);
    }
}

void CMultiLaplacianInferenceMethod::update_deriv()
{
    const index_t C=((CMulticlassLabels*)m_labels)->get_num_classes();
    const index_t n=m_labels->get_num_labels();
    m_U=SGMatrix<float64_t>(n, n*C);
    Map<MatrixXd> eigen_U(m_U.matrix, m_U.num_rows, m_U.num_cols);
    Map<MatrixXd> eigen_M(m_L.matrix, m_L.num_rows, m_L.num_cols);
    Map<MatrixXd> eigen_E(m_E.matrix, m_E.num_rows, m_E.num_cols);
    eigen_U=eigen_M.triangularView<Upper>().adjoint().solve(eigen_E);
}

float64_t CMultiLaplacianInferenceMethod::get_derivative_helper(SGMatrix<float64_t> dK)
{
    Map<MatrixXd> eigen_dK(dK.matrix, dK.num_rows, dK.num_cols);
    //currently only explicit term is computed
    const index_t C=((CMulticlassLabels*)m_labels)->get_num_classes();
    const index_t n=m_labels->get_num_labels();
    Map<MatrixXd> eigen_U(m_U.matrix, m_U.num_rows, m_U.num_cols);
    Map<MatrixXd> eigen_E(m_E.matrix, m_E.num_rows, m_E.num_cols);
    Map<VectorXd> eigen_alpha(m_alpha.vector, m_alpha.vlen);
    float64_t result=0;
    //currently only explicit term is computed
    for(index_t bl=0; bl<C; bl++)
    {
        result+=((eigen_E.block(0,bl*n,n,n)-eigen_U.block(0,bl*n,n,n).transpose()*eigen_U.block(0,bl*n,n,n)).array()
            *eigen_dK.array()).sum();
        result-=(eigen_dK*eigen_alpha.block(bl*n,0,n,1)).dot(eigen_alpha.block(bl*n,0,n,1));
    }

    return result/2.0;
}

SGVector<float64_t> CMultiLaplacianInferenceMethod::get_derivative_wrt_inference_method(
        const TParameter* param)
{
    REQUIRE(!strcmp(param->m_name, "log_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);

    SGVector<float64_t> result(1);

    // compute derivative K wrt scale

    result[0]=get_derivative_helper(m_ktrtr);
    result[0]*=CMath::exp(m_log_scale*2.0)*2.0;

    return result;
}

SGVector<float64_t> CMultiLaplacianInferenceMethod::get_derivative_wrt_kernel(
        const TParameter* param)
{
    // create eigen representation of K, Z, dfhat, dlp and alpha
    Map<MatrixXd> eigen_K(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);

    REQUIRE(param, "Param not set\n");
    SGVector<float64_t> result;
    int64_t len=const_cast<TParameter *>(param)->m_datatype.get_num_elements();
    result=SGVector<float64_t>(len);

    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);

        result[i]=get_derivative_helper(dK);
        result[i]*=CMath::exp(m_log_scale*2.0);
    }

    return result;
}

SGVector<float64_t> CMultiLaplacianInferenceMethod::get_derivative_wrt_mean(
        const TParameter* param)
{
    // create eigen representation of K, Z, dfhat and alpha
    Map<MatrixXd> eigen_K(m_ktrtr.matrix, m_ktrtr.num_rows, m_ktrtr.num_cols);
    Map<VectorXd> eigen_alpha(m_alpha.vector, m_alpha.vlen);
    const index_t C=((CMulticlassLabels*)m_labels)->get_num_classes();
    const index_t n=m_labels->get_num_labels();

    REQUIRE(param, "Param not set\n");
    SGVector<float64_t> result;
    int64_t len=const_cast<TParameter *>(param)->m_datatype.get_num_elements();
    result=SGVector<float64_t>(len);

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

        if (result.vlen==1)
            dmu=m_mean->get_parameter_derivative(m_features, param);
        else
            dmu=m_mean->get_parameter_derivative(m_features, param, i);

        Map<VectorXd> eigen_dmu(dmu.vector, dmu.vlen);

        result[i]=0;
        //currently only compute the explicit term
        for(index_t bl=0; bl<C; bl++)
            result[i]-=eigen_alpha.block(bl*n,0,n,1).dot(eigen_dmu);
    }

    return result;
}

SGVector<float64_t> CMultiLaplacianInferenceMethod::get_posterior_mean()
{
    compute_gradient();

    SGVector<float64_t> res(m_mu.vlen);
    Map<VectorXd> eigen_res(res.vector, res.vlen);
    const index_t C=((CMulticlassLabels*)m_labels)->get_num_classes();

    SGVector<float64_t> mean=m_mean->get_mean_vector(m_features);
    Map<VectorXd> eigen_mean_bl(mean.vector, mean.vlen);
    VectorXd eigen_mean=eigen_mean_bl.replicate(C,1);

    Map<VectorXd> eigen_mu(m_mu, m_mu.vlen);
    eigen_res=eigen_mu-eigen_mean;

    return res;
}


}

#endif /* HAVE_EIGEN3 */