SVMSGD.cpp

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/*
   SVM with stochastic gradient
   Copyright (C) 2007- Leon Bottou

   This program is free software; you can redistribute it and/or
   modify it under the terms of the GNU Lesser General Public
   License as published by the Free Software Foundation; either
   version 2.1 of the License, or (at your option) any later version.

   This program is distributed in the hope that it will be useful,
   but WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   GNU General Public License for more details.

   You should have received a copy of the GNU General Public License
   along with this program; if not, write to the Free Software
   Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111, USA
   $Id: svmsgd.cpp,v 1.13 2007/10/02 20:40:06 cvs Exp $

   Shogun adjustments (w) 2008-2009 Soeren Sonnenburg
*/

#include <shogun/classifier/svm/SVMSGD.h>
#include <shogun/base/Parameter.h>
#include <shogun/lib/Signal.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/loss/HingeLoss.h>

using namespace shogun;

CSVMSGD::CSVMSGD()
: CLinearMachine()
{
    init();
}

CSVMSGD::CSVMSGD(float64_t C)
: CLinearMachine()
{
    init();

    C1=C;
    C2=C;
}

CSVMSGD::CSVMSGD(float64_t C, CDotFeatures* traindat, CLabels* trainlab)
: CLinearMachine()
{
    init();
    C1=C;
    C2=C;

    set_features(traindat);
    set_labels(trainlab);
}

CSVMSGD::~CSVMSGD()
{
    SG_UNREF(loss);
}

void CSVMSGD::set_loss_function(CLossFunction* loss_func)
{
    if (loss)
        SG_UNREF(loss);
    loss=loss_func;
    SG_REF(loss);
}

bool CSVMSGD::train_machine(CFeatures* data)
{
    // allocate memory for w and initialize everyting w and bias with 0
    ASSERT(m_labels);
    ASSERT(m_labels->get_label_type() == LT_BINARY);

    if (data)
    {
        if (!data->has_property(FP_DOT))
            SG_ERROR("Specified features are not of type CDotFeatures\n");
        set_features((CDotFeatures*) data);
    }

    ASSERT(features);

    int32_t num_train_labels=m_labels->get_num_labels();
    int32_t num_vec=features->get_num_vectors();

    ASSERT(num_vec==num_train_labels);
    ASSERT(num_vec>0);

    w=SGVector<float64_t>(features->get_dim_feature_space());
    w.zero();
    bias=0;

    float64_t lambda= 1.0/(C1*num_vec);

    // Shift t in order to have a
    // reasonable initial learning rate.
    // This assumes |x| \approx 1.
    float64_t maxw = 1.0 / sqrt(lambda);
    float64_t typw = sqrt(maxw);
    float64_t eta0 = typw / CMath::max(1.0,-loss->first_derivative(-typw,1));
    t = 1 / (eta0 * lambda);

    SG_INFO("lambda=%f, epochs=%d, eta0=%f\n", lambda, epochs, eta0);


    //do the sgd
    calibrate();

    SG_INFO("Training on %d vectors\n", num_vec);
    CSignal::clear_cancel();

    ELossType loss_type = loss->get_loss_type();
    bool is_log_loss = false;
    if ((loss_type == L_LOGLOSS) || (loss_type == L_LOGLOSSMARGIN))
        is_log_loss = true;

    for(int32_t e=0; e<epochs && (!CSignal::cancel_computations()); e++)
    {
        count = skip;
        for (int32_t i=0; i<num_vec; i++)
        {
            float64_t eta = 1.0 / (lambda * t);
            float64_t y = ((CBinaryLabels*) m_labels)->get_label(i);
            float64_t z = y * (features->dense_dot(i, w.vector, w.vlen) + bias);

            if (z < 1 || is_log_loss)
            {
                float64_t etd = -eta * loss->first_derivative(z,1);
                features->add_to_dense_vec(etd * y / wscale, i, w.vector, w.vlen);

                if (use_bias)
                {
                    if (use_regularized_bias)
                        bias *= 1 - eta * lambda * bscale;
                    bias += etd * y * bscale;
                }
            }

            if (--count <= 0)
            {
                float64_t r = 1 - eta * lambda * skip;
                if (r < 0.8)
                    r = pow(1 - eta * lambda, skip);
                SGVector<float64_t>::scale_vector(r, w.vector, w.vlen);
                count = skip;
            }
            t++;
        }
    }

    float64_t wnorm =  SGVector<float64_t>::dot(w.vector,w.vector, w.vlen);
    SG_INFO("Norm: %.6f, Bias: %.6f\n", wnorm, bias);

    return true;
}

void CSVMSGD::calibrate()
{
    ASSERT(features);
    int32_t num_vec=features->get_num_vectors();
    int32_t c_dim=features->get_dim_feature_space();

    ASSERT(num_vec>0);
    ASSERT(c_dim>0);

    float64_t* c=SG_MALLOC(float64_t, c_dim);
    memset(c, 0, c_dim*sizeof(float64_t));

    SG_INFO("Estimating sparsity and bscale num_vec=%d num_feat=%d.\n", num_vec, c_dim);

    // compute average gradient size
    int32_t n = 0;
    float64_t m = 0;
    float64_t r = 0;

    for (int32_t j=0; j<num_vec && m<=1000; j++, n++)
    {
        r += features->get_nnz_features_for_vector(j);
        features->add_to_dense_vec(1, j, c, c_dim, true);

        //waste cpu cycles for readability
        //(only changed dims need checking)
        m=SGVector<float64_t>::max(c, c_dim);
    }

    // bias update scaling
    bscale = 0.5*m/n;

    // compute weight decay skip
    skip = (int32_t) ((16 * n * c_dim) / r);
    SG_INFO("using %d examples. skip=%d  bscale=%.6f\n", n, skip, bscale);

    SG_FREE(c);
}

void CSVMSGD::init()
{
    t=1;
    C1=1;
    C2=1;
    wscale=1;
    bscale=1;
    epochs=5;
    skip=1000;
    count=1000;
    use_bias=true;

    use_regularized_bias=false;

    loss=new CHingeLoss();
    SG_REF(loss);

    m_parameters->add(&C1, "C1",  "Cost constant 1.");
    m_parameters->add(&C2, "C2",  "Cost constant 2.");
    m_parameters->add(&wscale, "wscale",  "W scale");
    m_parameters->add(&bscale, "bscale",  "b scale");
    m_parameters->add(&epochs, "epochs",  "epochs");
    m_parameters->add(&skip, "skip",  "skip");
    m_parameters->add(&count, "count",  "count");
    m_parameters->add(&use_bias, "use_bias",  "Indicates if bias is used.");
    m_parameters->add(&use_regularized_bias, "use_regularized_bias",  "Indicates if bias is regularized.");
}