DomainAdaptationSVMLinear.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 2 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2007-2010 Christian Widmer
 * Copyright (C) 2007-2010 Max-Planck-Society
 */

#include "lib/config.h"

#ifdef HAVE_LAPACK

#include "classifier/svm/DomainAdaptationSVMLinear.h"
#include "lib/io.h"
#include "base/Parameter.h"
#include <iostream>
#include <vector>

using namespace shogun;


CDomainAdaptationSVMLinear::CDomainAdaptationSVMLinear() : CLibLinear(L2R_L1LOSS_SVC_DUAL)
{
    init(NULL, 0.0);
}


CDomainAdaptationSVMLinear::CDomainAdaptationSVMLinear(float64_t C, CDotFeatures* f, CLabels* lab, CLinearClassifier* pre_svm, float64_t B_param) : CLibLinear(C, f, lab)
{
    init(pre_svm, B_param);

}


CDomainAdaptationSVMLinear::~CDomainAdaptationSVMLinear()
{

    SG_UNREF(presvm);
    SG_DEBUG("deleting DomainAdaptationSVMLinear\n");
}


void CDomainAdaptationSVMLinear::init(CLinearClassifier* pre_svm, float64_t B_param)
{

    if (pre_svm)
    {
        // increase reference counts
        SG_REF(pre_svm);

        // set bias of parent svm to zero
        pre_svm->set_bias(0.0);
    }

    this->presvm=pre_svm;
    this->B=B_param;
    this->train_factor=1.0;

    set_liblinear_solver_type(L2R_L1LOSS_SVC_DUAL);

    // invoke sanity check
    is_presvm_sane();

    // serialization code
    m_parameters->add((CSGObject**) &presvm, "presvm", "SVM to regularize against");
    m_parameters->add(&B, "B",  "Regularization strenth B.");
    m_parameters->add(&train_factor, "train_factor",  "train_factor");

}


bool CDomainAdaptationSVMLinear::is_presvm_sane()
{

    if (!presvm) {

        SG_WARNING("presvm is null");

    } else {

        if (presvm->get_bias() != 0) {
            SG_ERROR("presvm bias not set to zero");
        }

        if (presvm->get_features()->get_feature_type() != this->get_features()->get_feature_type()) {
            SG_ERROR("feature types do not agree");
        }
    }

    return true;

}


bool CDomainAdaptationSVMLinear::train(CDotFeatures* train_data)
{

    CDotFeatures* tmp_data;

    if (train_data)
    {
        if (labels->get_num_labels() != train_data->get_num_vectors())
            SG_ERROR("Number of training vectors does not match number of labels\n");
        tmp_data = train_data;

    } else {

        tmp_data = features;
    }

    int32_t num_training_points = get_labels()->get_num_labels();

    std::vector<float64_t> lin_term = std::vector<float64_t>(num_training_points);

    if (presvm)
    {
        // bias of parent SVM was set to zero in constructor, already contains B
        CLabels* parent_svm_out = presvm->classify(tmp_data);

        SG_DEBUG("pre-computing linear term from presvm\n");

        // pre-compute linear term
        for (int32_t i=0; i!=num_training_points; i++)
        {
            lin_term[i] = (- B*(get_label(i) * parent_svm_out->get_label(i)))*train_factor - 1.0;
        }

        // set linear term for QP
        this->set_linear_term(&lin_term[0], lin_term.size());

    }

    /*
    //TODO test this code, measure speed-ups
    //presvm w stored in presvm
    float64_t* tmp_w;
    presvm->get_w(tmp_w, w_dim);

    //copy vector
    float64_t* tmp_w_copy = new float64_t[w_dim];
    std::copy(tmp_w, tmp_w + w_dim, tmp_w_copy);

    for (int32_t i=0; i!=w_dim; i++)
    {
        tmp_w_copy[i] = B * tmp_w_copy[i];
    }

    //set w (copied in setter)
    set_w(tmp_w_copy, w_dim);
    delete[] tmp_w_copy;
    */

    bool success = false;

    //train SVM
    if (train_data)
    {
        success = CLibLinear::train(train_data);
    } else {
        success = CLibLinear::train();
    }

    //ASSERT(presvm)

    return success;

}


CLinearClassifier* CDomainAdaptationSVMLinear::get_presvm()
{
    return presvm;
}


float64_t CDomainAdaptationSVMLinear::get_B()
{
    return B;
}


float64_t CDomainAdaptationSVMLinear::get_train_factor()
{
    return train_factor;
}


void CDomainAdaptationSVMLinear::set_train_factor(float64_t factor)
{
    train_factor = factor;
}


CLabels* CDomainAdaptationSVMLinear::classify(CDotFeatures* data)
{

    ASSERT(presvm->get_bias()==0.0);

    int32_t num_examples = data->get_num_vectors();

    CLabels* out_current = CLibLinear::classify(data);

    if (presvm)
    {

        // recursive call if used on DomainAdaptationSVM object
        CLabels* out_presvm = presvm->classify(data);


        // combine outputs
        for (int32_t i=0; i!=num_examples; i++)
        {
            float64_t out_combined = out_current->get_label(i) + B*out_presvm->get_label(i);
            out_current->set_label(i, out_combined);
        }

    }


    return out_current;

}

#endif //HAVE_LAPACK