BinaryLabels.cpp

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#include <shogun/labels/DenseLabels.h>
#include <shogun/labels/BinaryLabels.h>

using namespace shogun;

CBinaryLabels::CBinaryLabels() : CDenseLabels()
{
}

CBinaryLabels::CBinaryLabels(int32_t num_labels) : CDenseLabels(num_labels)
{
}

CBinaryLabels::CBinaryLabels(SGVector<float64_t> src, float64_t threshold) : CDenseLabels()
{
    SGVector<float64_t> labels(src.vlen);
    for (int32_t i=0; i<labels.vlen; i++)
        labels[i] = src[i]+threshold>=0 ? +1.0 : -1.0;
    set_labels(labels);
    set_values(src);
}

CBinaryLabels::CBinaryLabels(CFile* loader) : CDenseLabels(loader)
{
}

CBinaryLabels* CBinaryLabels::obtain_from_generic(CLabels* base_labels)
{
    if ( base_labels->get_label_type() == LT_BINARY )
        return (CBinaryLabels*) base_labels;
    else
        SG_SERROR("base_labels must be of dynamic type CBinaryLabels");

    return NULL;
}


void CBinaryLabels::ensure_valid(const char* context)
{
    CDenseLabels::ensure_valid(context);
    bool found_plus_one=false;
    bool found_minus_one=false;

    int32_t subset_size=get_num_labels();
    for (int32_t i=0; i<subset_size; i++)
    {
        int32_t real_i=m_subset_stack->subset_idx_conversion(i);
        if (m_labels[real_i]==+1.0)
            found_plus_one=true;
        else if (m_labels[real_i]==-1.0)
            found_minus_one=true;
        else
        {
            SG_ERROR("%s%sNot a two class labeling label[%d]=%f (only +1/-1 "
                    "allowed)\n", context?context:"", context?": ":"", i, m_labels[real_i]);
        }
    }
    
    if (!found_plus_one)
    {
        SG_ERROR("%s%sNot a two class labeling - no positively labeled examples found\n",
                context?context:"", context?": ":"");
    }

    if (!found_minus_one)
    {
        SG_ERROR("%s%sNot a two class labeling - no negatively labeled examples found\n",
                context?context:"", context?": ":"");
    }
}

ELabelType CBinaryLabels::get_label_type()
{
    return LT_BINARY;
}

void CBinaryLabels::scores_to_probabilities()
{
    SG_DEBUG("entering CBinaryLabels::scores_to_probabilities()\n");

    REQUIRE(m_current_values.vector, "%s::scores_to_probabilities() requires "
            "values vector!\n", get_name());

    /* count prior0 and prior1 if needed */
    int32_t prior0=0;
    int32_t prior1=0;
    SG_DEBUG("counting number of positive and negative labels\n");
    {
        for (index_t i=0; i<m_current_values.vlen; ++i)
        {
            if (m_current_values[i]>0)
                prior1++;
            else
                prior0++;
        }
    }
    SG_DEBUG("%d pos; %d neg\n", prior1, prior0);

    /* parameter setting */
    /* maximum number of iterations */
    index_t maxiter=100;

    /* minimum step taken in line search */
    float64_t minstep=1E-10;

    /* for numerically strict pd of hessian */
    float64_t sigma=1E-12;
    float64_t eps=1E-5;

    /* construct target support */
    float64_t hiTarget=(prior1+1.0)/(prior1+2.0);
    float64_t loTarget=1/(prior0+2.0);
    index_t length=prior1+prior0;

    SGVector<float64_t> t(length);
    for (index_t i=0; i<length; ++i)
    {
        if (m_current_values[i]>0)
            t[i]=hiTarget;
        else
            t[i]=loTarget;
    }

    /* initial Point and Initial Fun Value */
    /* result parameters of sigmoid */
    float64_t a=0;
    float64_t b=CMath::log((prior0+1.0)/(prior1+1.0));
    float64_t fval=0.0;

    for (index_t i=0; i<length; ++i)
    {
        float64_t fApB=m_current_values[i]*a+b;
        if (fApB>=0)
            fval+=t[i]*fApB+CMath::log(1+CMath::exp(-fApB));
        else
            fval+=(t[i]-1)*fApB+CMath::log(1+CMath::exp(fApB));
    }

    index_t it;
    float64_t g1;
    float64_t g2;
    for (it=0; it<maxiter; ++it)
    {
        SG_DEBUG("Iteration %d, a=%f, b=%f, fval=%f\n", it, a, b, fval);

        /* Update Gradient and Hessian (use H' = H + sigma I) */
        float64_t h11=sigma; //Numerically ensures strict PD
        float64_t h22=h11;
        float64_t h21=0;
        g1=0;
        g2=0;

        for (index_t i=0; i<length; ++i)
        {
            float64_t fApB=m_current_values[i]*a+b;
            float64_t p;
            float64_t q;
            if (fApB>=0)
            {
                p=CMath::exp(-fApB)/(1.0+CMath::exp(-fApB));
                q=1.0/(1.0+CMath::exp(-fApB));
            }
            else
            {
                p=1.0/(1.0+CMath::exp(fApB));
                q=CMath::exp(fApB)/(1.0+CMath::exp(fApB));
            }

            float64_t d2=p*q;
            h11+=m_current_values[i]*m_current_values[i]*d2;
            h22+=d2;
            h21+=m_current_values[i]*d2;
            float64_t d1=t[i]-p;
            g1+=m_current_values[i]*d1;
            g2+=d1;
        }

        /* Stopping Criteria */
        if (CMath::abs(g1)<eps && CMath::abs(g2)<eps)
            break;

        /* Finding Newton direction: -inv(H') * g */
        float64_t det=h11*h22-h21*h21;
        float64_t dA=-(h22*g1-h21*g2)/det;
        float64_t dB=-(-h21*g1+h11*g2)/det;
        float64_t gd=g1*dA+g2*dB;

        /* Line Search */
        float64_t stepsize=1;

        while (stepsize>=minstep)
        {
            float64_t newA=a+stepsize*dA;
            float64_t newB=b+stepsize*dB;

            /* New function value */
            float64_t newf=0.0;
            for (index_t i=0; i<length; ++i)
            {
                float64_t fApB=m_current_values[i]*newA+newB;
                if (fApB>=0)
                    newf+=t[i]*fApB+CMath::log(1+CMath::exp(-fApB));
                else
                    newf+=(t[i]-1)*fApB+CMath::log(1+CMath::exp(fApB));
            }

            /* Check sufficient decrease */
            if (newf<fval+0.0001*stepsize*gd)
            {
                a=newA;
                b=newB;
                fval=newf;
                break;
            }
            else
                stepsize=stepsize/2.0;
        }

        if (stepsize<minstep)
        {
            SG_WARNING("%s::scores_to_probabilities(): line search fails, A=%f, "
                    "B=%f, g1=%f, g2=%f, dA=%f, dB=%f, gd=%f\n",
                    get_name(), a, b, g1, g2, dA, dB, gd);
        }
    }

    if (it>=maxiter-1)
    {
        SG_WARNING("%s::scores_to_probabilities(): reaching maximal iterations,"
                " g1=%f, g2=%f\n", get_name(), g1, g2);
    }

    SG_DEBUG("fitted sigmoid: a=%f, b=%f\n", a, b);

    /* now the sigmoid is fitted, convert all values to probabilities */
    for (index_t i=0; i<m_current_values.vlen; ++i)
    {
        float64_t fApB=m_current_values[i]*a+b;
        m_current_values[i]=fApB>=0 ? CMath::exp(-fApB)/(1.0+exp(-fApB)) :
                1.0/(1+CMath::exp(fApB));
    }

    SG_DEBUG("leaving CBinaryLabels::scores_to_probabilities()\n");
}