QpBoxLinear.h

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//===========================================================================
/*!
 * 
 *
 * \brief       Quadratic programming solver linear SVM training without bias
 * 
 * 
 * 
 *
 * \author      T. Glasmachers
 * \date        -
 *
 *
 * \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_ALGORITHMS_QP_QPBOXLINEAR_H
#define SHARK_ALGORITHMS_QP_QPBOXLINEAR_H
 
#include <shark/Core/Timer.h>
#include <shark/Algorithms/QP/QuadraticProgram.h>
#include <shark/Data/Dataset.h>
#include <shark/Data/DataView.h>
#include <shark/LinAlg/Base.h>
#include <cmath>
#include <iostream>
 
 
namespace shark {
 
 
///
/// \brief Quadratic program solver for box-constrained problems with linear kernel
///
/// \par
/// The QpBoxLinear class is a decomposition-based solver for linear
/// support vector machine classifiers trained with the hinge loss.
/// Its optimization is largely based on the paper<br>
///   "A Dual Coordinate Descent Method for Large-scale Linear SVM"
///   by Hsieh, Chang, and Lin, ICML, 2007.
/// However, the present algorithm differs quite a bit, since it
/// learns variable preferences as a replacement for the missing
/// working set selection. At the same time, this method replaces
/// the shrinking heuristic.
///
template <class InputT>
class QpBoxLinear
{
public:
    typedef LabeledData<InputT, unsigned int> DatasetType;
    typedef typename LabeledData<InputT, unsigned int>::const_element_reference ElementType;
 
    ///
    /// \brief Constructor
    ///
    /// \param  dataset  training data
    /// \param  dim      problem dimension
    ///
    QpBoxLinear(const DatasetType& dataset, std::size_t dim)
    : m_data(dataset)
    , m_dim(dim)
    , m_xSquared(m_data.size())
    , m_alpha(m_data.size(),0.0)
    , m_weights(m_dim,0.0)
    , m_pref(m_data.size(),1.0)
    , m_offset(0)
    
    {
        SHARK_ASSERT(dim > 0);
 
        // pre-compute squared norms
        for (std::size_t i=0; i<m_data.size(); i++)
        {
            auto const& x_i = m_data[i];
            m_xSquared(i) = norm_sqr(x_i.input);
        }
    }
    
    void setOffset(double newOffset){
        m_offset = newOffset;
    }
    
    double offsetGradient()const{
        double result = 0;
        for(std::size_t i = 0; i != m_data.size(); ++i){
            double y_i = (m_data[i].label > 0) ? +1.0 : -1.0;
            result += m_alpha(i) * y_i;
        }
        return result;
    }
    
    RealVector const& solutionWeightVector()const{
        return m_weights;
    }
 
    ///
    /// \brief Solve the SVM training problem.
    ///
    /// \param  bound    upper bound for m_alpha-components, complexity parameter of the hinge loss SVM
    /// \param  reg      coefficient of the penalty term \f$-\frac{reg}{2} \cdot \|\m_alpha\|^2\f$, reg=1/C where C is the complexity parameter of the squared hinge loss SVM
    /// \param  stop     stopping condition(s)
    /// \param  prop     solution properties
    /// \param  verbose  if true, the solver prints status information and solution statistics
    ///
    void solve(
        double bound,
        double reg,
        QpStoppingCondition& stop,
        QpSolutionProperties* prop = NULL,
        bool verbose = false
    ){
        // sanity checks
        SHARK_ASSERT(bound > 0.0);
        SHARK_ASSERT(reg >= 0.0);
 
        // measure training time
        Timer timer;
 
        // prepare dimensions and vectors
        std::size_t ell = m_data.size();
        double prefsum = sum(m_pref);               // normalization constant for m_pref
        std::vector<std::size_t> schedule(ell);
 
        // prepare counters
        std::size_t epoch = 0;
        std::size_t steps = 0;
 
        // prepare performance monitoring for self-adaptation
        double max_violation = 0.0;
        const double gain_learning_rate = 1.0 / ell;
        double average_gain = 0.0;
        bool canstop = true;
 
        // outer optimization loop
        while (true)
        {
            // define schedule
            double psum = prefsum;
            prefsum = 0.0;
            std::size_t pos = 0;
            for (std::size_t i=0; i<ell; i++)
            {
                double p = m_pref[i];
                double num = (psum < 1e-6) ? ell - pos : std::min((double)(ell - pos), (ell - pos) * p / psum);
                std::size_t n = (std::size_t)std::floor(num);
                double prob = num - n;
                if (random::coinToss(random::globalRng,prob)) n++;
                for (std::size_t j=0; j<n; j++)
                {
                    schedule[pos] = i;
                    pos++;
                }
                psum -= p;
                prefsum += p;
            }
            SHARK_ASSERT(pos == ell);
            std::shuffle(schedule.begin(),schedule.end(),random::globalRng);
 
            // inner loop
            max_violation = 0.0;
            for (std::size_t j=0; j<ell; j++)
            {
                // active variable
                std::size_t i = schedule[j];
                auto const& e_i = m_data[i];
                double y_i = (e_i.label > 0) ? +1.0 : -1.0;
 
                // compute gradient and projected gradient
                double a = m_alpha(i);
                double wyx = y_i * inner_prod(m_weights, e_i.input);
                double g = 1.0 - m_offset * y_i - wyx - reg * a;
                double pg = (a == 0.0 && g < 0.0) ? 0.0 : (a == bound && g > 0.0 ? 0.0 : g);
 
                // update maximal KKT violation over the epoch
                max_violation = std::max(max_violation, std::abs(pg));
                double gain = 0.0;
 
                // perform the step
                if (pg != 0.0)
                {
                    // SMO-style coordinate descent step
                    double q = m_xSquared(i) + reg;
                    double mu = g / q;
                    double new_a = a + mu;
 
                    // numerically stable update
                    if (new_a <= 0.0)
                    {
                        mu = -a;
                        new_a = 0.0;
                    }
                    else if (new_a >= bound)
                    {
                        mu = bound - a;
                        new_a = bound;
                    }
 
                    // update both representations of the weight vector: m_alpha and m_weights
                    m_alpha(i) = new_a;
                    noalias(m_weights) += (mu * y_i) * e_i.input;
                    gain = mu * (g - 0.5 * q * mu);
 
                    steps++;
                }
 
                // update gain-based preferences
                {
                    if (epoch == 0) average_gain += gain / (double)ell;
                    else
                    {
                        // strategy constants
                        constexpr double CHANGE_RATE = 0.2;
                        constexpr double PREF_MIN = 0.05;
                        constexpr double PREF_MAX = 20.0;
 
                        double change = CHANGE_RATE * (gain / average_gain - 1.0);
                        double newpref = std::min(PREF_MAX, std::max(PREF_MIN, m_pref(i) * std::exp(change)));
                        prefsum += newpref - m_pref(i);
                        m_pref[i] = newpref;
                        average_gain = (1.0 - gain_learning_rate) * average_gain + gain_learning_rate * gain;
                    }
                }
            }
 
            epoch++;
 
            // stopping criteria
            if (stop.maxIterations > 0 && ell * epoch >= stop.maxIterations)
            {
                if (prop != NULL) prop->type = QpMaxIterationsReached;
                break;
            }
 
            if (timer.stop() >= stop.maxSeconds)
            {
                if (prop != NULL) prop->type = QpTimeout;
                break;
            }
 
            if (max_violation < stop.minAccuracy)
            {
                if (verbose) std::cout << "#" << std::flush;
                if (canstop)
                {
                    if (prop != NULL) prop->type = QpAccuracyReached;
                    break;
                }
                else
                {
                    // prepare full sweep for a reliable checking of the stopping criterion
                    canstop = true;
                    for (std::size_t i=0; i<ell; i++) m_pref[i] = 1.0;
                    prefsum = (double)ell;
                }
            }
            else
            {
                if (verbose) std::cout << "." << std::flush;
                canstop = false;
            }
        }
 
        timer.stop();
 
        // compute solution statistics
        std::size_t free_SV = 0;
        std::size_t bounded_SV = 0;
        double objective = -0.5 * norm_sqr(m_weights);
        for (std::size_t i=0; i<ell; i++)
        {
            double a = m_alpha(i);
            if (a > 0.0)
            {
                objective += a;
                objective -= reg/2.0 * a * a;
                if (a == bound) bounded_SV++;
                else free_SV++;
            }
        }
 
        // return solution statistics
        if (prop != NULL)
        {
            prop->accuracy = max_violation;       // this is approximate, but a good guess
            prop->iterations = ell * epoch;
            prop->value = objective;
            prop->seconds = timer.lastLap();
        }
 
        // output solution statistics
        if (verbose)
        {
            std::cout << std::endl;
            std::cout << "training time (seconds): " << timer.lastLap() << std::endl;
            std::cout << "number of epochs: " << epoch << std::endl;
            std::cout << "number of iterations: " << (ell * epoch) << std::endl;
            std::cout << "number of non-zero steps: " << steps << std::endl;
            std::cout << "dual accuracy: " << max_violation << std::endl;
            std::cout << "dual objective value: " << objective << std::endl;
            std::cout << "number of free support vectors: " << free_SV << std::endl;
            std::cout << "number of bounded support vectors: " << bounded_SV << std::endl;
        }
    }
 
protected:
    DataView<const DatasetType> m_data;               ///< view on training data
    std::size_t m_dim;                                ///< input space dimension
    RealVector m_xSquared;                            ///< diagonal entries of the quadratic matrix
    RealVector m_alpha;                               ///< storage of the m_alpha values for warm start
    RealVector m_weights;                                   ///< storage of weight vector for warm start
    RealVector m_pref;                ///< measure of success of individual steps
    double m_offset;
};
 
 
//~ template < >
//~ class QpBoxLinear<CompressedRealVector>
//~ {
//~ public:
    //~ typedef LabeledData<CompressedRealVector, unsigned int> DatasetType;
 
    //~ ///
    //~ /// \brief Constructor
    //~ ///
    //~ /// \param  dataset  training data
    //~ /// \param  dim      problem dimension
    //~ ///
    //~ QpBoxLinear(const DatasetType& dataset, std::size_t dim)
    //~ : x(dataset.numberOfElements())
    //~ , y(dataset.numberOfElements())
    //~ , diagonal(dataset.numberOfElements())
    //~ , m_dim(dim)
    //~ {
        //~ SHARK_ASSERT(dim > 0);
 
        //~ // transform ublas sparse vectors into a fast format
        //~ // (yes, ublas is slow...), and compute the diagonal
        //~ // elements of the quadratic matrix
        //~ SparseVector sparse;
        //~ for (std::size_t b=0, j=0; b<dataset.numberOfBatches(); b++)
        //~ {
            //~ DatasetType::const_batch_reference batch = dataset.batch(b);
            //~ for (std::size_t i=0; i<batch.size(); i++)
            //~ {
                //~ auto const& x_i = shark::get(batch, i).input;
                //~ // if (x_i.nnz() == 0) continue;
 
                //~ unsigned int y_i = shark::get(batch, i).label;
                //~ y[j] = 2.0 * y_i - 1.0;
                //~ double d = 0.0;
                //~ for (auto it=x_i.begin(); it != x_i.end(); ++it)
                //~ {
                    //~ double v = *it;
                    //~ sparse.index = it.index();
                    //~ sparse.value = v;
                    //~ storage.push_back(sparse);
                    //~ d += v * v;
                //~ }
                //~ sparse.index = (std::size_t)-1;
                //~ storage.push_back(sparse);
                //~ diagonal(j) = d;
                //~ j++;
            //~ }
        //~ }
        //~ for (std::size_t b=0, j=0, k=0; b<dataset.numberOfBatches(); b++)
        //~ {
            //~ DatasetType::const_batch_reference batch = dataset.batch(b);
            //~ for (std::size_t i=0; i<batch.size(); i++)
            //~ {
                //~ auto const& x_i = shark::get(batch, i).input;
                //~ // if (x_i.nnz() == 0) continue;
 
                //~ x[j] = &storage[k];   // cannot be done in the first loop because of vector reallocation
                //~ for (; storage[k].index != (std::size_t)-1; k++);
                //~ k++;
                //~ j++;
            //~ }
        //~ }
    //~ }
 
    //~ ///
    //~ /// \brief Solve the SVM training problem.
    //~ ///
    //~ /// \param  bound    upper bound for m_alpha-components, complexity parameter of the hinge loss SVM
    //~ /// \param  reg      coefficient of the penalty term \f$-\frac{reg}{2} \cdot \|\m_alpha\|^2\f$, reg=1/C where C is the complexity parameter of the squared hinge loss SVM
    //~ /// \param  stop     stopping condition(s)
    //~ /// \param  prop     solution properties
    //~ /// \param  verbose  if true, the solver prints status information and solution statistics
    //~ ///
    //~ RealVector solve(
            //~ double bound,
            //~ double reg,
            //~ QpStoppingCondition& stop,
            //~ QpSolutionProperties* prop = NULL,
            //~ bool verbose = false)
    //~ {
        //~ // sanity checks
        //~ SHARK_ASSERT(bound > 0.0);
        //~ SHARK_ASSERT(reg >= 0.0);
 
        //~ // measure training time
        //~ Timer timer;
        //~ timer.start();
 
        //~ // prepare dimensions and vectors
        //~ std::size_t ell = x.size();
        //~ RealVector m_alpha(ell, 0.0);
        //~ RealVector m_weights(m_dim, 0.0);
        //~ RealVector m_pref(ell, 1.0);          // measure of success of individual steps
        //~ double prefsum = ell;               // normalization constant
        //~ std::vector<std::size_t> schedule(ell);
 
        //~ // prepare counters
        //~ std::size_t epoch = 0;
        //~ std::size_t steps = 0;
 
        //~ // prepare performance monitoring for self-adaptation
        //~ double max_violation = 0.0;
        //~ const double gain_learning_rate = 1.0 / ell;
        //~ double average_gain = 0.0;
        //~ bool canstop = true;
 
        //~ // outer optimization loop
        //~ while (true)
        //~ {
            //~ // define schedule
            //~ double psum = prefsum;
            //~ prefsum = 0.0;
            //~ std::size_t pos = 0;
            //~ for (std::size_t i=0; i<ell; i++)
            //~ {
                //~ double p = m_pref[i];
                //~ double num = (psum < 1e-6) ? ell - pos : std::min((double)(ell - pos), (ell - pos) * p / psum);
                //~ std::size_t n = (std::size_t)std::floor(num);
                //~ double prob = num - n;
                //~ if (random::uni() < prob) n++;
                //~ for (std::size_t j=0; j<n; j++)
                //~ {
                    //~ schedule[pos] = i;
                    //~ pos++;
                //~ }
                //~ psum -= p;
                //~ prefsum += p;
            //~ }
            //~ SHARK_ASSERT(pos == ell);
            //~ for (std::size_t i=0; i<ell; i++) std::swap(schedule[i], schedule[random::discrete(0, ell - 1)]);
 
            //~ // inner loop
            //~ max_violation = 0.0;
            //~ for (std::size_t j=0; j<ell; j++)
            //~ {
                //~ // active variable
                //~ std::size_t i = schedule[j];
                //~ const SparseVector* x_i = x[i];
 
                //~ // compute gradient and projected gradient
                //~ double a = m_alpha(i);
                //~ double wyx = y(i) * inner_prod(m_weights, x_i);
                //~ double g = 1.0 - wyx - reg * a;
                //~ double pg = (a == 0.0 && g < 0.0) ? 0.0 : (a == bound && g > 0.0 ? 0.0 : g);
 
                //~ // update maximal KKT violation over the epoch
                //~ max_violation = std::max(max_violation, std::abs(pg));
                //~ double gain = 0.0;
 
                //~ // perform the step
                //~ if (pg != 0.0)
                //~ {
                    //~ // SMO-style coordinate descent step
                    //~ double q = diagonal(i) + reg;
                    //~ double mu = g / q;
                    //~ double new_a = a + mu;
 
                    //~ // numerically stable update
                    //~ if (new_a <= 0.0)
                    //~ {
                        //~ mu = -a;
                        //~ new_a = 0.0;
                    //~ }
                    //~ else if (new_a >= bound)
                    //~ {
                        //~ mu = bound - a;
                        //~ new_a = bound;
                    //~ }
 
                    //~ // update both representations of the weight vector: m_alpha and m_weights
                    //~ m_alpha(i) = new_a;
                    //~ // m_weights += (mu * y(i)) * x_i;
                    //~ axpy(m_weights, mu * y(i), x_i);
                    //~ gain = mu * (g - 0.5 * q * mu);
 
                    //~ steps++;
                //~ }
 
                //~ // update gain-based preferences
                //~ {
                    //~ if (epoch == 0) average_gain += gain / (double)ell;
                    //~ else
                    //~ {
                        //~ // strategy constants
                        //~ constexpr double CHANGE_RATE = 0.2;
                        //~ constexpr double PREF_MIN = 0.05;
                        //~ constexpr double PREF_MAX = 20.0;
 
                        //~ double change = CHANGE_RATE * (gain / average_gain - 1.0);
                        //~ double newpref = std::min(PREF_MAX, std::max(PREF_MIN, m_pref(i) * std::exp(change)));
                        //~ prefsum += newpref - m_pref(i);
                        //~ m_pref[i] = newpref;
                        //~ average_gain = (1.0 - gain_learning_rate) * average_gain + gain_learning_rate * gain;
                    //~ }
                //~ }
            //~ }
 
            //~ epoch++;
 
            //~ // stopping criteria
            //~ if (stop.maxIterations > 0 && ell * epoch >= stop.maxIterations)
            //~ {
                //~ if (prop != NULL) prop->type = QpMaxIterationsReached;
                //~ break;
            //~ }
 
            //~ if (timer.stop() >= stop.maxSeconds)
            //~ {
                //~ if (prop != NULL) prop->type = QpTimeout;
                //~ break;
            //~ }
 
            //~ if (max_violation < stop.minAccuracy)
            //~ {
                //~ if (verbose) std::cout << "#" << std::flush;
                //~ if (canstop)
                //~ {
                    //~ if (prop != NULL) prop->type = QpAccuracyReached;
                    //~ break;
                //~ }
                //~ else
                //~ {
                    //~ // prepare full sweep for a reliable checking of the stopping criterion
                    //~ canstop = true;
                    //~ for (std::size_t i=0; i<ell; i++) m_pref[i] = 1.0;
                    //~ prefsum = ell;
                //~ }
            //~ }
            //~ else
            //~ {
                //~ if (verbose) std::cout << "." << std::flush;
                //~ canstop = false;
            //~ }
        //~ }
 
        //~ timer.stop();
 
        //~ // compute solution statistics
        //~ std::size_t free_SV = 0;
        //~ std::size_t bounded_SV = 0;
        //~ double objective = -0.5 * shark::blas::inner_prod(m_weights, m_weights);
        //~ for (std::size_t i=0; i<ell; i++)
        //~ {
            //~ double a = m_alpha(i);
            //~ if (a > 0.0)
            //~ {
                //~ objective += a;
                //~ objective -= reg/2.0 * a * a;
                //~ if (a == bound) bounded_SV++;
                //~ else free_SV++;
            //~ }
        //~ }
 
        //~ // return solution statistics
        //~ if (prop != NULL)
        //~ {
            //~ prop->accuracy = max_violation;       // this is approximate, but a good guess
            //~ prop->iterations = ell * epoch;
            //~ prop->value = objective;
            //~ prop->seconds = timer.lastLap();
        //~ }
 
        //~ // output solution statistics
        //~ if (verbose)
        //~ {
            //~ std::cout << std::endl;
            //~ std::cout << "training time (seconds): " << timer.lastLap() << std::endl;
            //~ std::cout << "number of epochs: " << epoch << std::endl;
            //~ std::cout << "number of iterations: " << (ell * epoch) << std::endl;
            //~ std::cout << "number of non-zero steps: " << steps << std::endl;
            //~ std::cout << "dual accuracy: " << max_violation << std::endl;
            //~ std::cout << "dual objective value: " << objective << std::endl;
            //~ std::cout << "number of free support vectors: " << free_SV << std::endl;
            //~ std::cout << "number of bounded support vectors: " << bounded_SV << std::endl;
        //~ }
 
        //~ // return the solution
        //~ return m_weights;
    //~ }
 
//~ protected:
    //~ /// \brief Data structure for sparse vectors.
    //~ struct SparseVector
    //~ {
        //~ std::size_t index;
        //~ double value;
    //~ };
 
    //~ /// \brief Famous "axpy" product, here adding a multiple of a sparse vector to a dense one.
    //~ static inline void axpy(RealVector& m_weights, double m_alpha, const SparseVector* xi)
    //~ {
        //~ while (true)
        //~ {
            //~ if (xi->index == (std::size_t)-1) return;
            //~ m_weights[xi->index] += m_alpha * xi->value;
            //~ xi++;
        //~ }
    //~ }
 
    //~ /// \brief Inner product between a dense and a sparse vector.
    //~ static inline double inner_prod(RealVector const& m_weights, const SparseVector* xi)
    //~ {
        //~ double ret = 0.0;
        //~ while (true)
        //~ {
            //~ if (xi->index == (std::size_t)-1) return ret;
            //~ ret += m_weights[xi->index] * xi->value;
            //~ xi++;
        //~ }
    //~ }
 
    //~ std::vector<SparseVector> storage;                ///< storage for sparse vectors
    //~ std::vector<SparseVector*> x;                     ///< sparse vectors
    //~ RealVector y;                                     ///< +1/-1 labels
    //~ RealVector diagonal;                              ///< diagonal entries of the quadratic matrix
    //~ std::size_t m_dim;                                ///< input space dimension
//~ };
 
 
}
#endif