LabelOrder.h

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//===========================================================================
/*!
 *
 *
 * \brief       This will relabel a given dataset to have labels 0..N-1 (and vice versa)
 *
 *
 *
 * \author      Aydin Demircioglu
 * \date        2014
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 *
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 *
 * Shark 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 3 of the License, or
 * (at your option) any later version.
 *
 * Shark 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 Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
 
 
#ifndef SHARK_LABELORDER_H
#define SHARK_LABELORDER_H
 
#include <shark/Core/INameable.h>
#include <shark/Core/ISerializable.h>
 
#include <shark/Data/Dataset.h>
 
 
 
 
namespace shark
{
 
 
/// \brief This will normalize the labels of a given dataset to 0..N-1
///
/// \par This will normalize the labels of a given dataset to 0..N-1
/// and store the ordering in a member variable.
/// After processing, the dataset will afterwards have labels ranging
/// from 0 to N-1, with N the number of classes, so usual Shark
/// trainers can work with it.
/// One can then revert the original labeling just by calling restoreOriginalLabels
class LabelOrder : public INameable
{
private:
 
public:
 
 
    LabelOrder() {};
 
 
    virtual ~LabelOrder() {};
 
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "LabelOrder"; }
 
 
    /// \brief This will normalize the labels and store the ordering in the
    /// member variables. The dataset will afterwards have labels ranging
    /// from 0 to N-1, with N the number of classes.
    /// This will overwrite any previously stored label ordering in the object.
    ///
    /// \param[in,out]  dataset     dataset that will be relabeled
 
    void normalizeLabels(LabeledData<RealVector, unsigned int> &dataset)
    {
        // determine the min and max labels of the given dataset
        unsigned int minLabel = std::numeric_limits<unsigned int>::max();
        unsigned int maxLabel = 0;
        for(std::size_t i = 0; i < dataset.numberOfElements(); ++i)
        {
            unsigned int label = dataset.labels().element(i);
 
            if(label < minLabel)
                minLabel = label;
            if(label > maxLabel)
                maxLabel = label;
        }
 
        // now we create an vector that can hold the label ordering
        m_labelOrder.clear();
 
        // and one array that tracks what we already encountered
        unsigned int maxval = std::numeric_limits<unsigned int>::max();
        std::vector<unsigned int> foundLabels(maxLabel - minLabel + 1, maxval);
 
        // and insert all labels we encounter
        unsigned int currentPosition = 0;
        for(std::size_t i = 0; i < dataset.numberOfElements(); i++)
        {
            // is it a new label?
            unsigned int label = dataset.labels().element(i);
            if(foundLabels[label - minLabel] == maxval)
            {
                foundLabels[label - minLabel] = currentPosition;
                m_labelOrder.push_back(label);
                currentPosition++;
            }
        }
 
        // now map every label
        for(std::size_t i = 0; i < dataset.numberOfElements(); i++)
        {
            unsigned int label = dataset.labels().element(i);
            dataset.labels().element(i) = foundLabels[label - minLabel];
        }
    }
 
 
 
    /// \brief This will restore the original labels of the dataset. This
    /// must be called with data compatible the original dataset, so that the labels will
    /// fit. The label ordering will not be destroyed after calling this function, so
    /// it can be called multiple times, e.g. to testsets or similar data.
    ///
    /// \param[in,out]  dataset     dataset to relabel (restore labels)
 
    void restoreOriginalLabels(LabeledData<RealVector, unsigned int> &dataset)
    {
        // now map every label
        for(std::size_t i = 0; i < dataset.numberOfElements(); ++i)
        {
            unsigned int label = dataset.labels().element(i);
 
            // check if the reordering fit the data
            SHARK_RUNTIME_CHECK(label < m_labelOrder.size(),"Dataset labels does not fit to the stored ordering!");
 
            // relabel
            label = m_labelOrder[label];
            dataset.labels().element(i) = label;
        }
    }
 
 
 
    /// \brief Get label ordering directly
    ///
    /// \param[out] labelOrder      vector to store the current label order.
 
    void getLabelOrder(std::vector<unsigned int>& labelOrder)
    {
        labelOrder = m_labelOrder;
    }
 
 
    /// \brief Set label ordering directly
    ///
    /// \param[in] labelOrder      vector with the new label order
 
    void setLabelOrder(std::vector<unsigned int> const& labelOrder)
    {
        m_labelOrder = labelOrder;
    }
 
 
protected:
 
    std::vector<unsigned int> m_labelOrder;
};
 
}
 
#endif