ZeroOneLoss.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Error measure for classication tasks, typically used for evaluation of results
 * 
 * 
 *
 * \author      T. Glasmachers
 * \date        2010-2011
 *
 *
 * \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_OBJECTIVEFUNCTIONS_LOSS_ZEROONELOSS_H
#define SHARK_OBJECTIVEFUNCTIONS_LOSS_ZEROONELOSS_H
 
 
#include <shark/ObjectiveFunctions/Loss/AbstractLoss.h>
 
namespace shark {
 
///
/// \brief 0-1-loss for classification.
///
/// The ZeroOneLoss requires the existence of the comparison
/// operator == for its LabelType template parameter. The
/// loss function returns zero of the predictions exactly
/// matches the label, and one otherwise.
/// \ingroup lossfunctions
template<class LabelType = unsigned int, class OutputType = LabelType>
class ZeroOneLoss : public AbstractLoss<LabelType, LabelType>
{
public:
    typedef AbstractLoss<LabelType, LabelType> base_type;
    typedef typename base_type::BatchLabelType BatchLabelType;
    typedef typename base_type::BatchOutputType BatchOutputType;
 
    /// constructor
    ZeroOneLoss()
    { }
 
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "ZeroOneLoss"; }
 
    using base_type::eval;
 
    ///\brief Return zero if labels == predictions and one otherwise.
    double eval(BatchLabelType const& labels, BatchOutputType const& predictions) const{
        std::size_t numInputs = labels.size();
        SIZE_CHECK(numInputs == predictions.size());
 
        double error = 0;
        for(std::size_t i = 0; i != numInputs; ++i){
            error += (predictions(i) != labels(i))?1.0:0.0;
        }
        return error;
    }
};
 
 
/// \brief 0-1-loss for classification.
template <class Float>
class ZeroOneLoss<unsigned int, blas::vector<Float> > : public AbstractLoss<unsigned int, blas::vector<Float> >
{
public:
    typedef AbstractLoss<unsigned int, blas::vector<Float> > base_type;
    typedef typename base_type::BatchLabelType BatchLabelType;
    typedef typename base_type::BatchOutputType BatchOutputType;
 
    /// constructor
    ///
    /// \param threshold: in the case dim(predictions) == 1, predictions strictly larger than this parameter are regarded as belonging to the positive class
    ZeroOneLoss(double threshold = 0.0)
    {
        m_threshold = threshold;
    }
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "ZeroOneLoss"; }
 
 
    // annoyingness of C++ templates
    using base_type::eval;
 
    /// Return zero if labels == arg max { predictions_i } and one otherwise,
    /// where the index i runs over the components of the predictions vector.
    /// A special version of dim(predictions) == 1 computes the predicted
    /// labels by thresholding at zero. Shark's label convention is used,
    /// saying that a positive value encodes class 0, a negative value
    /// encodes class 1.
    double eval(BatchLabelType const& labels, BatchOutputType const& predictions) const{
        std::size_t numInputs = labels.size();
        SIZE_CHECK(numInputs == predictions.size1());
 
        double error = 0;
        for(std::size_t i = 0; i != numInputs; ++i){
            error+=evalSingle(labels(i),row(predictions,i));
        }
        return error;
    }
    
    double eval(Data<unsigned int> const& targets, Data< blas::vector<Float>> const& predictions, RealVector const& weights) const{
        SIZE_CHECK(predictions.numberOfElements() == weights.size());
        SIZE_CHECK(targets.numberOfElements() == weights.size());
        SIZE_CHECK(predictions.numberOfBatches() == targets.numberOfBatches());
        double error = 0;
        for(std::size_t i = 0; i != predictions.numberOfBatches(); ++i){
            for(std::size_t j = 0; j != targets.batch(i).size(); ++j){
                error+= weights(i) * evalSingle(targets.batch(i)(j),row(predictions.batch(i),j));
            }
        }
        return error / weights.size();
    }
    
private:
    template<class VectorType>
    double evalSingle(unsigned int label, VectorType const& predictions) const{
        std::size_t size = predictions.size();
        if (size == 1){
            // binary case, single real-valued predictions
            unsigned int t = (predictions(0) > m_threshold);
            if (t == label) return 0.0;
            else return 1.0;
        }
        else{
            // multi-class case, one prediction component per class
            RANGE_CHECK(label < size);
            double p = predictions(label);
            for (std::size_t i = 0; i<size; i++)
            {
                if (i == label) continue;
                if (predictions(i) >= p) return 1.0;
            }
            return 0.0;
        }
    }
 
    double m_threshold; ///< in the case dim(predictions) == 1, predictions strictly larger tha this parameter are regarded as belonging to the positive class
};
 
 
}
#endif