MultitaskL12LogisticRegression.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.
 *
 * Copyright (C) 2012 Sergey Lisitsyn
 */


#include <shogun/transfer/multitask/MultitaskL12LogisticRegression.h>
#ifdef USE_GPL_SHOGUN
#include <shogun/lib/malsar/malsar_joint_feature_learning.h>
#include <shogun/lib/malsar/malsar_options.h>
#include <shogun/lib/SGVector.h>
#include <shogun/features/DotFeatures.h>

namespace shogun
{

class CMultitaskL12LogisticRegression::Self
{
public:

    float64_t m_rho1;

    float64_t m_rho2;
};

CMultitaskL12LogisticRegression::CMultitaskL12LogisticRegression() :
    CMultitaskLogisticRegression(), self()
{
    set_rho1(0.0);
    set_rho2(0.0);
    init();
}

CMultitaskL12LogisticRegression::CMultitaskL12LogisticRegression(
     float64_t rho1, float64_t rho2, CDotFeatures* train_features,
     CBinaryLabels* train_labels, CTaskGroup* task_group) :
    CMultitaskLogisticRegression(0.0,train_features,train_labels,(CTaskRelation*)task_group)
{
    set_rho1(rho1);
    set_rho2(rho2);
    init();
}

void CMultitaskL12LogisticRegression::init()
{
    SG_ADD(&self->m_rho1,"rho1","rho L1/L2 regularization parameter",MS_AVAILABLE);
    SG_ADD(&self->m_rho2,"rho2","rho L2 regularization parameter",MS_AVAILABLE);
}

void CMultitaskL12LogisticRegression::set_rho1(float64_t rho1)
{
    self->m_rho1 = rho1;
}

void CMultitaskL12LogisticRegression::set_rho2(float64_t rho2)
{
    self->m_rho2 = rho2;
}

float64_t CMultitaskL12LogisticRegression::get_rho1() const
{
    return self->m_rho1;
}

float64_t CMultitaskL12LogisticRegression::get_rho2() const
{
    return self->m_rho2;
}

CMultitaskL12LogisticRegression::~CMultitaskL12LogisticRegression()
{
}

bool CMultitaskL12LogisticRegression::train_locked_implementation(SGVector<index_t>* tasks)
{
    SGVector<float64_t> y(m_labels->get_num_labels());
    for (int32_t i=0; i<y.vlen; i++)
        y[i] = ((CBinaryLabels*)m_labels)->get_label(i);

    malsar_options options = malsar_options::default_options();
    options.termination = m_termination;
    options.tolerance = m_tolerance;
    options.max_iter = m_max_iter;
    options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
    options.tasks_indices = tasks;
    malsar_result_t model = malsar_joint_feature_learning(
        features, y.vector, self->m_rho1, self->m_rho2, options);

    m_tasks_w = model.w;
    m_tasks_c = model.c;

    return true;
}

bool CMultitaskL12LogisticRegression::train_machine(CFeatures* data)
{
    if (data && (CDotFeatures*)data)
        set_features((CDotFeatures*)data);

    ASSERT(features)
    ASSERT(m_labels)
    ASSERT(m_task_relation)

    SGVector<float64_t> y(m_labels->get_num_labels());
    for (int32_t i=0; i<y.vlen; i++)
        y[i] = ((CBinaryLabels*)m_labels)->get_label(i);

    malsar_options options = malsar_options::default_options();
    options.termination = m_termination;
    options.tolerance = m_tolerance;
    options.max_iter = m_max_iter;
    options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
    options.tasks_indices = ((CTaskGroup*)m_task_relation)->get_tasks_indices();

    malsar_result_t model = malsar_joint_feature_learning(
        features, y.vector, self->m_rho1, self->m_rho2, options);

    m_tasks_w = model.w;
    m_tasks_c = model.c;

    SG_FREE(options.tasks_indices);

    return true;
}

}

#endif //USE_GPL_SHOGUN