StratifiedCrossValidationSplitting.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) 2011-2012 Heiko Strathmann
 * Copyright (C) 2011 Berlin Institute of Technology and Max-Planck-Society
 */

#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
#include <shogun/labels/Labels.h>
#include <shogun/labels/BinaryLabels.h>
#include <shogun/labels/MulticlassLabels.h>

using namespace shogun;

CStratifiedCrossValidationSplitting::CStratifiedCrossValidationSplitting() :
    CSplittingStrategy(0, 0)
{
}

CStratifiedCrossValidationSplitting::CStratifiedCrossValidationSplitting(
        CLabels* labels, index_t num_subsets) :
    CSplittingStrategy(labels, num_subsets)
{
    /* check for "stupid" combinations of label numbers and num_subsets.
     * if there are of a class less labels than num_subsets, the class will not
     * appear in every subset, leading to subsets of only one class in the
     * extreme case of a two class labeling. */
    SGVector<float64_t> classes;

    int32_t num_classes=2;
    if (labels->get_label_type() == LT_MULTICLASS)
    {
        num_classes=((CMulticlassLabels*) labels)->get_num_classes();
        classes=((CMulticlassLabels*) labels)->get_unique_labels();
    }
    else if (labels->get_label_type() == LT_BINARY)
    {
        classes=SGVector<float64_t>(2);
        classes[0]=-1;
        classes[1]=+1;
    }
    else
    {
        SG_ERROR("Multiclass or binary labels required for stratified crossvalidation\n");
    }

    SGVector<index_t> labels_per_class(num_classes);

    for (index_t i=0; i<num_classes; ++i)
    {
        labels_per_class.vector[i]=0;
        for (index_t j=0; j<labels->get_num_labels(); ++j)
        {
            if (classes.vector[i]==((CDenseLabels*) labels)->get_label(j))
                labels_per_class.vector[i]++;
        }
    }

    for (index_t i=0; i<num_classes; ++i)
    {
        if (labels_per_class.vector[i]<num_subsets)
        {
            SG_WARNING("There are only %d labels of class %.18g, but %d "
                    "subsets. Labels of that class will not appear in every "
                    "subset!\n", labels_per_class.vector[i], classes.vector[i], num_subsets);
        }
    }
}

void CStratifiedCrossValidationSplitting::build_subsets()
{
    /* ensure that subsets are empty and set flag to filled */
    reset_subsets();
    m_is_filled=true;

    SGVector<float64_t> unique_labels;

    if (m_labels->get_label_type() == LT_MULTICLASS)
    {
        unique_labels=((CMulticlassLabels*) m_labels)->get_unique_labels();
    }
    else if (m_labels->get_label_type() == LT_BINARY)
    {
        unique_labels=SGVector<float64_t>(2);
        unique_labels[0]=-1;
        unique_labels[1]=+1;
    }
    else
    {
        SG_ERROR("Multiclass or binary labels required for stratified crossvalidation\n");
    }

    /* for every label, build set for indices */
    CDynamicObjectArray label_indices;
    for (index_t i=0; i<unique_labels.vlen; ++i)
        label_indices.append_element(new CDynamicArray<index_t> ());

    /* fill set with indices, for each label type ... */
    for (index_t i=0; i<unique_labels.vlen; ++i)
    {
        /* ... iterate over all labels and add indices with same label to set */
        for (index_t j=0; j<m_labels->get_num_labels(); ++j)
        {
            if (((CDenseLabels*) m_labels)->get_label(j)==unique_labels.vector[i])
            {
                CDynamicArray<index_t>* current=(CDynamicArray<index_t>*)
                        label_indices.get_element(i);
                current->append_element(j);
                SG_UNREF(current);
            }
        }
    }

    /* shuffle created label sets */
    for (index_t i=0; i<label_indices.get_num_elements(); ++i)
    {
        CDynamicArray<index_t>* current=(CDynamicArray<index_t>*)
                label_indices.get_element(i);
        current->shuffle();
        SG_UNREF(current);
    }

    /* distribute labels to subsets for all label types */
    index_t target_set=0;
    for (index_t i=0; i<unique_labels.vlen; ++i)
    {
        /* current index set for current label */
        CDynamicArray<index_t>* current=(CDynamicArray<index_t>*)
                label_indices.get_element(i);

        for (index_t j=0; j<current->get_num_elements(); ++j)
        {
            CDynamicArray<index_t>* next=(CDynamicArray<index_t>*)
                    m_subset_indices->get_element(target_set++);
            next->append_element(current->get_element(j));
            target_set%=m_subset_indices->get_num_elements();
            SG_UNREF(next);
        }

        SG_UNREF(current);
    }

    /* finally shuffle to avoid that subsets with low indices have more
     * elements, which happens if the number of class labels is not equal to
     * the number of subsets */
    m_subset_indices->shuffle();
}