slep_mc_tree_lr.cpp

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/*
 * 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) 2012 Sergey Lisitsyn
 * Copyright (C) 2010-2012 Jun Liu, Jieping Ye
 */

#include <shogun/lib/slep/slep_mc_tree_lr.h>
#ifdef HAVE_EIGEN3
#include <shogun/lib/slep/tree/general_altra.h>
#include <shogun/lib/slep/tree/altra.h>
#include <shogun/lib/slep/q1/eppMatrix.h>
#include <shogun/mathematics/Math.h>
#include <shogun/mathematics/eigen3.h>
#include <shogun/lib/Signal.h>
#include <shogun/lib/Time.h>
#include <iostream>

using namespace shogun;
using namespace Eigen;
using namespace std;

namespace shogun
{

slep_result_t slep_mc_tree_lr(
        CDotFeatures* features,
        CMulticlassLabels* labels,
        float64_t z,
        const slep_options& options)
{
    int i,j;
    // obtain problem parameters
    int n_feats   = features->get_dim_feature_space();
    int n_vecs    = features->get_num_vectors();
    int n_classes = labels->get_num_classes();

    // labels vector containing values in range (0 .. n_classes)
    SGVector<float64_t> labels_vector = labels->get_labels();

    // initialize matrices and vectors to be used
    // weight vector
    MatrixXd w  = MatrixXd::Zero(n_feats, n_classes);
    // intercepts (biases)
    VectorXd c  = VectorXd::Zero(n_classes);

    if (options.last_result)
    {
        SGMatrix<float64_t> last_w = options.last_result->w;
        SGVector<float64_t> last_c = options.last_result->c;
        for (i=0; i<n_classes; i++)
        {
            c[i] = last_c[i];
            for (j=0; j<n_feats; j++)
                w(j,i) = last_w(j,i);
        }
    }
    // iterative process matrices and vectors
    MatrixXd wp = w, wwp = MatrixXd::Zero(n_feats, n_classes);
    VectorXd cp = c, ccp = VectorXd::Zero(n_classes);
    // search point weight vector
    MatrixXd search_w = MatrixXd::Zero(n_feats, n_classes);
    // search point intercepts
    VectorXd search_c = VectorXd::Zero(n_classes);
    // dot products
    MatrixXd Aw  = MatrixXd::Zero(n_vecs, n_classes);
    for (j=0; j<n_classes; j++)
        features->dense_dot_range(Aw.col(j).data(), 0, n_vecs, NULL, w.col(j).data(), n_feats, 0.0);
    MatrixXd As  = MatrixXd::Zero(n_vecs, n_classes);
    MatrixXd Awp = MatrixXd::Zero(n_vecs, n_classes);
    // gradients
    MatrixXd g   = MatrixXd::Zero(n_feats, n_classes);
    VectorXd gc  = VectorXd::Zero(n_classes);
    // projection
    MatrixXd v   = MatrixXd::Zero(n_feats, n_classes);

    // Lipschitz continuous gradient parameter for line search
    double L = 1.0/(n_vecs*n_classes);
    // coefficients for search point computation
    double alphap = 0, alpha = 1;

    // lambda regularization parameter
    double lambda = z;
    // objective values
    double objective = 0.0;
    double objective_p = 0.0;

    int iter = 0;
    bool done = false;
    CTime time;
    //internal::set_is_malloc_allowed(false);
    while ((!done) && (iter<options.max_iter) && (!CSignal::cancel_computations()))
    {
        double beta = (alphap-1)/alpha;
        // compute search points
        search_w = w + beta*wwp;
        search_c = c + beta*ccp;

        // update dot products with search point
        As = Aw + beta*(Aw-Awp);

        // compute objective and gradient at search point
        double fun_s = 0;
        g.setZero();
        gc.setZero();
        // for each vector
        for (i=0; i<n_vecs; i++)
        {
            // class of current vector
            int vec_class = labels_vector[i];
            // for each class
            for (j=0; j<n_classes; j++)
            {
                // compute logistic loss
                double aa = ((vec_class == j) ? -1.0 : 1.0)*(As(i,j) + search_c(j));
                double bb = aa > 0.0 ? aa : 0.0;
                // avoid underflow via log-sum-exp trick
                fun_s += CMath::log(CMath::exp(-bb) + CMath::exp(aa-bb)) + bb;
                double prob = 1.0/(1+CMath::exp(aa));
                double b = ((vec_class == j) ? -1.0 : 1.0)*(1-prob);
                // update gradient of intercepts
                gc[j] += b;
                // update gradient of weight vectors
                features->add_to_dense_vec(b, i, g.col(j).data(), n_feats);
            }
        }
        //fun_s /= (n_vecs*n_classes);

        wp = w;
        Awp = Aw;
        cp = c;

        int inner_iter = 0;
        double fun_x = 0;

        // line search process
        while (inner_iter<5000)
        {
            // compute line search point
            v = search_w - g/L;
            c = search_c - gc/L;

            // compute projection of gradient
            if (options.general)
                general_altra_mt(w.data(),v.data(),n_classes,n_feats,options.G,options.ind_t,options.n_nodes,lambda/L);
            else
                altra_mt(w.data(),v.data(),n_classes,n_feats,options.ind_t,options.n_nodes,lambda/L);
            v = w - search_w;

            // update dot products
            for (j=0; j<n_classes; j++)
                features->dense_dot_range(Aw.col(j).data(), 0, n_vecs, NULL, w.col(j).data(), n_feats, 0.0);

            // compute objective at search point
            fun_x = 0;
            for (i=0; i<n_vecs; i++)
            {
                int vec_class = labels_vector[i];
                for (j=0; j<n_classes; j++)
                {
                    double aa = ((vec_class == j) ? -1.0 : 1.0)*(Aw(i,j) + c(j));
                    double bb = aa > 0.0 ? aa : 0.0;
                    fun_x += CMath::log(CMath::exp(-bb) + CMath::exp(aa-bb)) + bb;
                }
            }
            //fun_x /= (n_vecs*n_classes);

            // check for termination of line search
            double r_sum = (v.squaredNorm() + (c-search_c).squaredNorm())/2;
            double l_sum = fun_x - fun_s - v.cwiseProduct(g).sum() - (c-search_c).dot(gc);

            // stop if projected gradient is less than 1e-20
            if (r_sum <= 1e-20)
            {
                SG_SINFO("Gradient step makes little improvement (%f)\n",r_sum)
                done = true;
                break;
            }

            if (l_sum <= r_sum*L)
                break;
            else
                L = CMath::max(2*L, l_sum/r_sum);

            inner_iter++;
        }

        // update alpha coefficients
        alphap = alpha;
        alpha = (1+CMath::sqrt(4*alpha*alpha+1))/2;

        // update wwp and ccp
        wwp = w - wp;
        ccp = c - cp;

        // update objectives
        objective_p = objective;
        objective = fun_x;

        // compute tree norm
        double tree_norm = 0.0;
        if (options.general)
        {
            for (i=0; i<n_classes; i++)
                tree_norm += general_treeNorm(w.col(i).data(),n_classes,n_feats,options.G,options.ind_t,options.n_nodes);
        }
        else
        {
            for (i=0; i<n_classes; i++)
                tree_norm += treeNorm(w.col(i).data(),n_classes,n_feats,options.ind_t,options.n_nodes);
        }

        // regularize objective with tree norm
        objective += lambda*tree_norm;

        //cout << "Objective = " << objective << endl;

        // check for termination of whole process
        if ((CMath::abs(objective - objective_p) < options.tolerance*CMath::abs(objective_p)) && (iter>2))
        {
            SG_SINFO("Objective changes less than tolerance\n")
            done = true;
        }

        iter++;
    }
    SG_SINFO("%d iterations passed, objective = %f\n",iter,objective)
    //internal::set_is_malloc_allowed(true);

    // output computed weight vectors and intercepts
    SGMatrix<float64_t> r_w(n_feats,n_classes);
    for (j=0; j<n_classes; j++)
    {
        for (i=0; i<n_feats; i++)
            r_w(i,j) = w(i,j);
    }
    //r_w.display_matrix();
    SGVector<float64_t> r_c(n_classes);
    for (j=0; j<n_classes; j++)
        r_c[j] = c[j];
    return slep_result_t(r_w, r_c);
};
};
#endif