MulticlassModel.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 Fernando José Iglesias García
 * Copyright (C) 2012 Fernando José Iglesias García
 */

#include <shogun/features/DotFeatures.h>
#include <shogun/mathematics/Math.h>
#include <shogun/structure/MulticlassModel.h>
#include <shogun/structure/MulticlassSOLabels.h>

using namespace shogun;

CMulticlassModel::CMulticlassModel()
: CStructuredModel()
{
    init();
}

    CMulticlassModel::CMulticlassModel(CFeatures* features, CStructuredLabels* labels)
: CStructuredModel(features, labels)
{
    init();
}

CMulticlassModel::~CMulticlassModel()
{
}

int32_t CMulticlassModel::get_dim() const
{
    // TODO make the casts safe!
    int32_t num_classes = ((CMulticlassSOLabels*) m_labels)->get_num_classes();
    int32_t feats_dim   = ((CDotFeatures*) m_features)->get_dim_feature_space();

    return feats_dim*num_classes;
}

SGVector< float64_t > CMulticlassModel::get_joint_feature_vector(int32_t feat_idx, CStructuredData* y)
{
    SGVector< float64_t > psi( get_dim() );
    psi.zero();

    SGVector< float64_t > x = ((CDotFeatures*) m_features)->
        get_computed_dot_feature_vector(feat_idx);
    CRealNumber* r = CRealNumber::obtain_from_generic(y);
    ASSERT(r != NULL)
    float64_t label_value = r->value;

    for ( index_t i = 0, j = label_value*x.vlen ; i < x.vlen ; ++i, ++j )
        psi[j] = x[i];

    return psi;
}

CResultSet* CMulticlassModel::argmax(
        SGVector< float64_t > w,
        int32_t feat_idx,
        bool const training)
{
    CDotFeatures* df = (CDotFeatures*) m_features;
    int32_t feats_dim   = df->get_dim_feature_space();

    if ( training )
    {
        CMulticlassSOLabels* ml = (CMulticlassSOLabels*) m_labels;
        m_num_classes = ml->get_num_classes();
    }
    else
    {
        REQUIRE(m_num_classes > 0, "The model needs to be trained before "
                "using it for prediction\n");
    }

    int32_t dim = get_dim();
    ASSERT(dim == w.vlen);

    // Find the class that gives the maximum score

    float64_t score = 0, ypred = 0;
    float64_t max_score = -CMath::INFTY;

    for ( int32_t c = 0 ; c < m_num_classes ; ++c )
    {
        score = df->dense_dot(feat_idx, w.vector+c*feats_dim, feats_dim);
        if ( training )
            score += delta_loss(feat_idx, c);

        if ( score > max_score )
        {
            max_score = score;
            ypred = c;
        }
    }

    // Build the CResultSet object to return
    CResultSet* ret = new CResultSet();
    SG_REF(ret);
    CRealNumber* y  = new CRealNumber(ypred);
    SG_REF(y);

    ret->psi_pred = get_joint_feature_vector(feat_idx, y);
    ret->score    = max_score;
    ret->argmax   = y;
    if ( training )
    {
        ret->delta     = CStructuredModel::delta_loss(feat_idx, y);
        ret->psi_truth = CStructuredModel::get_joint_feature_vector(
                    feat_idx, feat_idx);
        ret->score    -= SGVector< float64_t >::dot(w.vector,
                    ret->psi_truth.vector, dim);
    }

    return ret;
}

float64_t CMulticlassModel::delta_loss(CStructuredData* y1, CStructuredData* y2)
{
    CRealNumber* rn1 = CRealNumber::obtain_from_generic(y1);
    CRealNumber* rn2 = CRealNumber::obtain_from_generic(y2);
    ASSERT(rn1 != NULL);
    ASSERT(rn2 != NULL);

    return delta_loss(rn1->value, rn2->value);
}

float64_t CMulticlassModel::delta_loss(int32_t y1_idx, float64_t y2)
{
    REQUIRE(y1_idx >= 0 || y1_idx < m_labels->get_num_labels(),
            "The label index must be inside [0, num_labels-1]\n");

    CRealNumber* rn1 = CRealNumber::obtain_from_generic(m_labels->get_label(y1_idx));
    float64_t ret = delta_loss(rn1->value, y2);
    SG_UNREF(rn1);

    return ret;
}

float64_t CMulticlassModel::delta_loss(float64_t y1, float64_t y2)
{
    return (y1 == y2) ? 0 : 1;
}

void CMulticlassModel::init_opt(
        SGMatrix< float64_t > & A,
        SGVector< float64_t > a,
        SGMatrix< float64_t > B,
        SGVector< float64_t > & b,
        SGVector< float64_t > lb,
        SGVector< float64_t > ub,
        SGMatrix< float64_t > & C)
{
    C = SGMatrix< float64_t >::create_identity_matrix(get_dim(), 1);
}

void CMulticlassModel::init()
{
    SG_ADD(&m_num_classes, "m_num_classes", "The number of classes",
            MS_NOT_AVAILABLE);

    m_num_classes = 0;
}

float64_t CMulticlassModel::risk(float64_t* subgrad, float64_t* W, TMultipleCPinfo* info)
{
    CDotFeatures* X=(CDotFeatures*)m_features;
    CMulticlassSOLabels* y=(CMulticlassSOLabels*)m_labels;
    m_num_classes = y->get_num_classes();
    uint32_t from, to;

    if (info)
    {
        from=info->_from;
        to=(info->N == 0) ? X->get_num_vectors() : from+info->N;
    } else {
        from=0;
        to=X->get_num_vectors();
    }

    uint32_t num_classes=y->get_num_classes();
    uint32_t feats_dim=X->get_dim_feature_space();
    const uint32_t w_dim=get_dim();

    float64_t R=0.0;
    for (uint32_t i=0; i<w_dim; i++)
        subgrad[i] = 0;

    float64_t Rtmp=0.0;
    float64_t Rmax=0.0;
    float64_t loss=0.0;
    uint32_t yhat=0;
    uint32_t GT=0;
    CRealNumber* GT_rn=NULL;

    /* loop through examples */
    for(uint32_t i=from; i<to; ++i)
    {
        Rmax=-CMath::INFTY;
        GT_rn=CRealNumber::obtain_from_generic(y->get_label(i));
        GT=(uint32_t)GT_rn->value;

        for (uint32_t c = 0; c < num_classes; ++c)
        {
            loss=(c == GT) ? 0.0 : 1.0;
            Rtmp=loss+X->dense_dot(i, W+c*feats_dim, feats_dim)
                -X->dense_dot(i, W+GT*feats_dim, feats_dim);

            if (Rtmp > Rmax)
            {
                Rmax=Rtmp;
                yhat=c;
            }
        }
        R += Rmax;

        X->add_to_dense_vec(1.0, i, subgrad+yhat*feats_dim, feats_dim);
        X->add_to_dense_vec(-1.0, i, subgrad+GT*feats_dim, feats_dim);

        SG_UNREF(GT_rn);
    }

    return R;
}