ExportKernelMatrix.h

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//===========================================================================
/*!
 *
 *
 * \brief       export precomputed kernel matrices (using libsvm format)
 *
 *
 *
 * \author      M. Tuma
 * \date        2012
 *
 *
 * \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_DATA_PRECOMPUTEDMATRIX_H
#define SHARK_DATA_PRECOMPUTEDMATRIX_H
 
 
 
#include <fstream>
#include <shark/Data/Dataset.h>
#include <shark/Data/DataView.h>
#include <shark/Models/Kernels/AbstractKernelFunction.h>
#include <shark/Models/Kernels/ScaledKernel.h>
#include <shark/Algorithms/Trainers/NormalizeKernelUnitVariance.h>
 
 
namespace shark
{
 
/**
 * \ingroup shark_globals
 *
 * @{
 */
 
enum KernelMatrixNormalizationType
{
    NONE,                               // no normalization. output regular Gram kernel matrix
    MULTIPLICATIVE_TRACE_ONE,           // determine the trace, and devide each entry by it
    MULTIPLICATIVE_TRACE_N,             // determine the trace, devide each entry by it, then multiply by the number of samples
    MULTIPLICATIVE_VARIANCE_ONE,        // normalize to unit variance in feature space. see kloft in jmlr 2012.
    CENTER_ONLY,                        // center the kernel in feature space. see cortes in jmlr 2012 and in icml 2010.
    CENTER_AND_MULTIPLICATIVE_TRACE_ONE // first center the kernel in featrue space. then devide each entry by the centered kernel's trace.
};
 
/// \brief Write a kernel Gram matrix to stream.
///
/// \param  dataset    data basis for the Gram matrix
/// \param  kernel     pointer to kernel function to be used
/// \param  out         The stream to be written to
/// \param  normalizer what kind of normalization to apply. see enum declaration for details.
/// \param  scientific        should the output be in scientific notation?
/// \param  fieldwidth      field width for pretty printing
template<typename InputType, typename LabelType>
void exportKernelMatrix(
    LabeledData<InputType, LabelType> const &dataset,
    AbstractKernelFunction<InputType> &kernel,           // kernel function (can't be const b/c of ScaledKernel later)
    std::ostream &out,                                     // The stream to be written to
    KernelMatrixNormalizationType normalizer = NONE, // what kind of normalization to apply. see enum declaration for details.
    bool scientific = false,                         // scientific notation?
    unsigned int fieldwidth = 0                      // for pretty-printing
)
{
    //get access to the range of elements
    DataView<LabeledData<InputType, LabelType> const> points(dataset);
    std::size_t size = points.size();
 
    SIZE_CHECK(size != 0);
    // check outstream status
    if(!out)
    {
        throw(std::invalid_argument("[export_kernel_matrix] Can't write to stream."));
    }
 
    // COMPUTE MODIFIERS
 
    // if multiplicative trace normalization: determine trace
    double trace = 0.0;
    double trace_factor = 1.0;
    if(normalizer == MULTIPLICATIVE_TRACE_ONE || normalizer == MULTIPLICATIVE_TRACE_N)
    {
        for(auto point: points)
        {
            trace += kernel.eval(point.input, point.input);
        }
        SHARK_ASSERT(trace > 0);
        trace_factor = 1.0 / trace;
        if(normalizer == MULTIPLICATIVE_TRACE_N)
        {
            trace_factor *= size;
        }
    }
 
    // if multiplicative variance normalization: determine factor
    double variance_factor = 0.0;
    if(normalizer == MULTIPLICATIVE_VARIANCE_ONE)
    {
        ScaledKernel<InputType> scaled(&kernel);
        NormalizeKernelUnitVariance<InputType> normalizer;
        normalizer.train(scaled, dataset.inputs());
        variance_factor = scaled.factor();
    }
 
    // if centering: determine matrix- and row-wise means;
    double mean = 0;
    RealVector rowmeans(size, 0.0);
    if(normalizer == CENTER_ONLY || normalizer == CENTER_AND_MULTIPLICATIVE_TRACE_ONE)
    {
        // initialization: calculate mean and rowmeans
        for(std::size_t i = 0; i < size; i++)
        {
            double k = kernel.eval(points[i].input, points[i].input);
            mean += k; //add diagonal value to mean once
            rowmeans(i) += k; //add diagonal to its rowmean
            for(std::size_t j = 0; j < i; j++)
            {
                double k = kernel.eval(points[i].input, points[j].input);
                mean += 2.0 * k; //add off-diagonals to mean twice
                rowmeans(i) += k; //add to mean of row
                rowmeans(j) += k; //add to mean of transposed row
            }
        }
        mean = mean / (double) size / (double) size;
        rowmeans /= size;
        // get trace if necessary
        if(normalizer == CENTER_AND_MULTIPLICATIVE_TRACE_ONE)
        {
            trace = 0.0;
            for(std::size_t i = 0; i < size; i++)
            {
                trace += kernel.eval(points[i].input, points[i].input) - 2 * rowmeans(i) + mean;
            }
            SHARK_ASSERT(trace > 0);
            trace_factor = 1.0 / trace;
        }
    }
 
    // FIX OUTPUT FORMAT
 
    // set output format
    if(scientific)
        out.setf(std::ios_base::scientific);
    std::streamsize ss = out.precision();
    out.precision(10);
 
    // determine dataset type
    double max_label = -1e100;
    double min_label = -max_label;
    bool binary = false;
    bool regression = false;
    for(double cur_label: dataset.labels().elements())
    {
        if(cur_label > max_label)
            max_label = cur_label;
        if(cur_label < min_label)
            min_label = cur_label;
        if((cur_label != (int)cur_label) || cur_label < 0)
            regression = true;
    }
    if(!regression && (min_label == 0) && (max_label == 1))
        binary = true;
 
    // WRITE OUT
 
    // write to file:
    // loop through examples (rows)
    for(std::size_t i = 0; i < size; i++)
    {
 
        // write label
        if(regression)
        {
            out << std::setw(fieldwidth) << std::left << points[i].label << " ";
        }
        else if(binary)
        {
            out << std::setw(fieldwidth) << std::left << (int)(points[i].label * 2 - 1) << " ";
        }
        else
        {
            out << std::setw(fieldwidth) << std::left << (unsigned int)(points[i].label + 1) << " ";
        }
 
        out << "0:" << std::setw(fieldwidth) << std::left << i + 1; //write index
 
        // loop through examples (columns)
        // CASE DISTINCTION:
        if(normalizer == NONE)
        {
            for(std::size_t j = 0; j < size; j++)
            {
                out  << " " << j + 1 << ":" << std::setw(fieldwidth) << std::left << kernel.eval(points[i].input, points[j].input);
            }
            out << "\n";
        }
        else if(normalizer == MULTIPLICATIVE_TRACE_ONE || normalizer == MULTIPLICATIVE_TRACE_N)
        {
            for(std::size_t j = 0; j < size; j++)
            {
                out  << " " << j + 1 << ":" << std::setw(fieldwidth) << std::left << trace_factor * kernel.eval(points[i].input, points[j].input);
            }
            out << "\n";
        }
        else if(normalizer == MULTIPLICATIVE_VARIANCE_ONE)
        {
            for(std::size_t j = 0; j < size; j++)
            {
                out  << " " << j + 1 << ":" << std::setw(fieldwidth) << std::left <<  variance_factor *kernel.eval(points[i].input, points[j].input);
            }
            out << "\n";
        }
        else if(normalizer == CENTER_ONLY)
        {
            for(std::size_t j = 0; j < size; j++)
            {
                double tmp = kernel.eval(points[i].input, points[j].input) - rowmeans(i) - rowmeans(j) + mean;
                out  << " " << j + 1 << ":" << std::setw(fieldwidth) << std::left << tmp;
            }
            out << "\n";
        }
        else if(normalizer == CENTER_AND_MULTIPLICATIVE_TRACE_ONE)
        {
            for(std::size_t j = 0; j < size; j++)
            {
                double tmp = kernel.eval(points[i].input, points[j].input) - rowmeans(i) - rowmeans(j) + mean;
                out  << " " << j + 1 << ":" << std::setw(fieldwidth) << std::left << trace_factor *tmp;
            }
            out << "\n";
        }
        else
        {
            throw SHARKEXCEPTION("[detail::export_kernel_matrix] Unknown normalization type.");
        }
 
    }
 
    // clean up
    out.precision(ss);
}
 
 
 
/// \brief Write a kernel Gram matrix to file.
///
/// \param  dataset    data basis for the Gram matrix
/// \param  kernel     pointer to kernel function to be used
/// \param  fn         The filename of the file to be written to
/// \param  normalizer what kind of normalization to apply. see enum declaration for details.
/// \param  sci        should the output be in scientific notation?
/// \param  width      field width for pretty printing
template<typename InputType, typename LabelType>
void exportKernelMatrix(
    LabeledData<InputType, LabelType> const &dataset,
    AbstractKernelFunction<InputType> &kernel,
    std::string fn,
    KernelMatrixNormalizationType normalizer = NONE,
    bool sci = false,
    unsigned int width = 0
)
{
    std::ofstream ofs(fn.c_str());
    if(ofs)
    {
        exportKernelMatrix(dataset, kernel, ofs, normalizer, sci, width);
    }
    else
        throw(std::invalid_argument("[detail::export_kernel_matrix] Stream cannot be opened for writing."));
 
}
 
 
 
 
// deprecated wrapper
template<typename InputType, typename LabelType>
void export_kernel_matrix(
    LabeledData<InputType, LabelType> const &dataset,
    AbstractKernelFunction<InputType> &kernel,           // kernel function (can't be const b/c of ScaledKernel later)
    std::ostream &out,                                     // The stream to be written to
    KernelMatrixNormalizationType normalizer = NONE, // what kind of normalization to apply. see enum declaration for details.
    bool scientific = false,                         // scientific notation?
    unsigned int fieldwidth = 0                      // for pretty-printing
)
{
    exportKernelMatrix(dataset, kernel, out, normalizer, scientific, fieldwidth);
}
 
 
// deprecated wrapper
template<typename InputType, typename LabelType>
void export_kernel_matrix(
    LabeledData<InputType, LabelType> const &dataset,
    AbstractKernelFunction<InputType> &kernel,
    std::string fn,
    KernelMatrixNormalizationType normalizer = NONE,
    bool sci = false,
    unsigned int width = 0
)
{
    exportKernelMatrix(dataset, kernel, fn, normalizer,  sci, width);
}
 
 
 
// TODO: import functionality is still missing.
//       when that is done, add tutorial
 
 
/** @}*/
 
} // namespace shark
 
 
 
#endif // SHARK_DATA_PRECOMPUTEDMATRIX_H