BaggingMachine.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) 2013 Viktor Gal
 * Copyright (C) 2013 Viktor Gal
 */

#include <shogun/machine/BaggingMachine.h>
#include <shogun/ensemble/CombinationRule.h>
#include <shogun/evaluation/Evaluation.h>

using namespace shogun;

CBaggingMachine::CBaggingMachine()
    : CMachine()
{
    init();
    register_parameters();
}

CBaggingMachine::CBaggingMachine(CFeatures* features, CLabels* labels)
    : CMachine()
{
    init();
    register_parameters();

    set_labels(labels);

    SG_REF(features);
    m_features = features;
}

CBaggingMachine::~CBaggingMachine()
{
    SG_UNREF(m_machine);
    SG_UNREF(m_features);
    SG_UNREF(m_combination_rule);
    SG_UNREF(m_bags);
    SG_UNREF(m_oob_indices);
}

CBinaryLabels* CBaggingMachine::apply_binary(CFeatures* data)
{
    SGVector<float64_t> combined_vector = apply_get_outputs(data);

    CBinaryLabels* pred = new CBinaryLabels(combined_vector);
    return pred;
}

CMulticlassLabels* CBaggingMachine::apply_multiclass(CFeatures* data)
{
    SGVector<float64_t> combined_vector = apply_get_outputs(data);

    CMulticlassLabels* pred = new CMulticlassLabels(combined_vector);
    return pred;
}

CRegressionLabels* CBaggingMachine::apply_regression(CFeatures* data)
{
    SGVector<float64_t> combined_vector = apply_get_outputs(data);

    CRegressionLabels* pred = new CRegressionLabels(combined_vector);

    return pred;
}

SGVector<float64_t> CBaggingMachine::apply_get_outputs(CFeatures* data)
{
    ASSERT(data != NULL);
    REQUIRE(m_combination_rule != NULL, "Combination rule is not set!");
    ASSERT(m_num_bags == m_bags->get_num_elements());

    SGMatrix<float64_t> output(data->get_num_vectors(), m_num_bags);
    output.zero();

    /*
    #pragma omp parallel for num_threads(parallel->get_num_threads())
    */
    for (int32_t i = 0; i < m_num_bags; ++i)
    {
        CMachine* m = dynamic_cast<CMachine*>(m_bags->get_element(i));
        CLabels* l = m->apply(data);
        SGVector<float64_t> lv;
        if (l!=NULL)
            lv = dynamic_cast<CDenseLabels*>(l)->get_labels();
        else
            SG_ERROR("NULL returned by apply method\n");

        float64_t* bag_results = output.get_column_vector(i);
        memcpy(bag_results, lv.vector, lv.vlen*sizeof(float64_t));

        SG_UNREF(l);
        SG_UNREF(m);
    }

    SGVector<float64_t> combined = m_combination_rule->combine(output);

    return combined;
}

bool CBaggingMachine::train_machine(CFeatures* data)
{
    REQUIRE(m_machine != NULL, "Machine is not set!");
    REQUIRE(m_num_bags > 0, "Number of bag is not set!");

    if (data)
    {
        SG_REF(data);
        SG_UNREF(m_features);
        m_features = data;

        ASSERT(m_features->get_num_vectors() == m_labels->get_num_labels());
    }

    // if bag size is not provided, set it equal to number of training vectors
    if (m_bag_size==0)
        m_bag_size = m_features->get_num_vectors();

    // clear the array, if previously trained
    m_bags->reset_array();

    // reset the oob index vector
    m_all_oob_idx = SGVector<bool>(m_features->get_num_vectors());
    m_all_oob_idx.zero();

    SG_UNREF(m_oob_indices);
    m_oob_indices = new CDynamicObjectArray();

    /*
      TODO: enable multi-threaded learning. This requires views support
        on CFeatures
    #pragma omp parallel for num_threads(parallel->get_num_threads())
    */
    for (int32_t i = 0; i < m_num_bags; ++i)
    {
        CMachine* c=dynamic_cast<CMachine*>(m_machine->clone());
        ASSERT(c != NULL);
        SGVector<index_t> idx(get_bag_size());
        idx.random(0, m_features->get_num_vectors()-1);
        m_labels->add_subset(idx);
        /* TODO:
           if it's a binary labeling ensure that
           there's always samples of both classes
        if ((m_labels->get_label_type() == LT_BINARY))
        {
            while (true) {
                if (!m_labels->ensure_valid()) {
                    m_labels->remove_subset();
                    idx.random(0, m_features->get_num_vectors());
                    m_labels->add_subset(idx);
                    continue;
                }
                break;
            }
        }
        */
        m_features->add_subset(idx);
        set_machine_parameters(c,idx);
        c->set_labels(m_labels);
        c->train(m_features);
        m_features->remove_subset();
        m_labels->remove_subset();

        // get out of bag indexes
        CDynamicArray<index_t>* oob = get_oob_indices(idx);
        m_oob_indices->push_back(oob);

        // add trained machine to bag array
        m_bags->push_back(c);

        SG_UNREF(c);
    }

    return true;
}

void CBaggingMachine::set_machine_parameters(CMachine* m, SGVector<index_t> idx)
{
}

void CBaggingMachine::register_parameters()
{
    SG_ADD((CSGObject**)&m_features, "features", "Train features for bagging",
        MS_NOT_AVAILABLE);
    SG_ADD(&m_num_bags, "num_bags", "Number of bags", MS_AVAILABLE);
    SG_ADD(&m_bag_size, "bag_size", "Number of vectors per bag", MS_AVAILABLE);
    SG_ADD((CSGObject**)&m_bags, "bags", "Bags array", MS_NOT_AVAILABLE);
    SG_ADD((CSGObject**)&m_combination_rule, "combination_rule",
        "Combination rule to use for aggregating", MS_AVAILABLE);
    SG_ADD(&m_all_oob_idx, "all_oob_idx", "Indices of all oob vectors",
            MS_NOT_AVAILABLE);
    SG_ADD((CSGObject**)&m_oob_indices, "oob_indices",
            "OOB indices for each machine", MS_NOT_AVAILABLE);
}

void CBaggingMachine::set_num_bags(int32_t num_bags)
{
    m_num_bags = num_bags;
}

int32_t CBaggingMachine::get_num_bags() const
{
    return m_num_bags;
}

void CBaggingMachine::set_bag_size(int32_t bag_size)
{
    m_bag_size = bag_size;
}

int32_t CBaggingMachine::get_bag_size() const
{
    return m_bag_size;
}

CMachine* CBaggingMachine::get_machine() const
{
    SG_REF(m_machine);
    return m_machine;
}

void CBaggingMachine::set_machine(CMachine* machine)
{
    SG_REF(machine);
    SG_UNREF(m_machine);
    m_machine = machine;
}

void CBaggingMachine::init()
{
    m_bags = new CDynamicObjectArray();
    m_machine = NULL;
    m_features = NULL;
    m_combination_rule = NULL;
    m_labels = NULL;
    m_num_bags = 0;
    m_bag_size = 0;
    m_all_oob_idx = SGVector<bool>();
    m_oob_indices = NULL;
}

void CBaggingMachine::set_combination_rule(CCombinationRule* rule)
{
    SG_REF(rule);
    SG_UNREF(m_combination_rule);
    m_combination_rule = rule;
}

CCombinationRule* CBaggingMachine::get_combination_rule() const
{
    SG_REF(m_combination_rule);
    return m_combination_rule;
}

float64_t CBaggingMachine::get_oob_error(CEvaluation* eval) const
{
    REQUIRE(m_combination_rule != NULL, "Combination rule is not set!");
    REQUIRE(m_bags->get_num_elements() > 0, "BaggingMachine is not trained!");

    SGMatrix<float64_t> output(m_features->get_num_vectors(), m_bags->get_num_elements());
    if (m_labels->get_label_type() == LT_REGRESSION)
        output.zero();
    else
        output.set_const(NAN);

    /* TODO: add parallel support of applying the OOBs
      only possible when add_subset is thread-safe
    #pragma omp parallel for num_threads(parallel->get_num_threads())
    */
    for (index_t i = 0; i < m_bags->get_num_elements(); i++)
    {
        CMachine* m = dynamic_cast<CMachine*>(m_bags->get_element(i));
        CDynamicArray<index_t>* current_oob
            = dynamic_cast<CDynamicArray<index_t>*>(m_oob_indices->get_element(i));

        SGVector<index_t> oob(current_oob->get_array(), current_oob->get_num_elements(), false);
        m_features->add_subset(oob);

        CLabels* l = m->apply(m_features);
        SGVector<float64_t> lv;
        if (l!=NULL)
            lv = dynamic_cast<CDenseLabels*>(l)->get_labels();
        else
            SG_ERROR("NULL returned by apply method\n");

        // assign the values in the matrix (NAN) that are in-bag!
        for (index_t j = 0; j < oob.vlen; j++)
            output(oob[j], i) = lv[j];

        m_features->remove_subset();
        SG_UNREF(current_oob);
        SG_UNREF(m);
        SG_UNREF(l);
    }

    DynArray<index_t> idx;
    for (index_t i = 0; i < m_features->get_num_vectors(); i++)
    {
        if (m_all_oob_idx[i])
            idx.push_back(i);
    }

    SGVector<float64_t> combined = m_combination_rule->combine(output);
    SGVector<float64_t> lab(idx.get_num_elements());
    for (int32_t i=0;i<lab.vlen;i++)
        lab[i]=combined[idx.get_element(i)];

    CLabels* predicted = NULL;
    switch (m_labels->get_label_type())
    {
        case LT_BINARY:
            predicted = new CBinaryLabels(lab);
            break;

        case LT_MULTICLASS:
            predicted = new CMulticlassLabels(lab);
            break;

        case LT_REGRESSION:
            predicted = new CRegressionLabels(lab);
            break;

        default:
            SG_ERROR("Unsupported label type\n");
    }

    m_labels->add_subset(SGVector<index_t>(idx.get_array(), idx.get_num_elements(), false));
    float64_t res = eval->evaluate(predicted, m_labels);
    m_labels->remove_subset();

    SG_UNREF(predicted);
    return res;
}

CDynamicArray<index_t>* CBaggingMachine::get_oob_indices(const SGVector<index_t>& in_bag)
{
    SGVector<bool> out_of_bag(m_features->get_num_vectors());
    out_of_bag.set_const(true);

    // mark the ones that are in_bag
    index_t oob_count = m_features->get_num_vectors();
    for (index_t i = 0; i < in_bag.vlen; i++)
    {
        if (out_of_bag[in_bag[i]])
        {
            out_of_bag[in_bag[i]] = false;
            oob_count--;
        }
    }

    CDynamicArray<index_t>* oob = new CDynamicArray<index_t>();
    // store the indicies of vectors that are out of the bag
    for (index_t i = 0; i < out_of_bag.vlen; i++)
    {
        if (out_of_bag[i])
        {
            oob->push_back(i);
            m_all_oob_idx[i] = true;
        }
    }

    return oob;
}