dlib C++ Library - svm_threaded.h

    // Copyright (C) 2008  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_SVm_THREADED_
    #define DLIB_SVm_THREADED_

    #include <cmath>
    #include <iostream>
    #include <limits>
    #include <sstream>
    #include <vector>

    #include "svm_threaded_abstract.h"
    #include "svm.h"
    #include "../matrix.h"
    #include "../algs.h"
    #include "../serialize.h"
    #include "function.h"
    #include "kernel.h"
    #include "../threads.h"
    #include "../pipe.h"

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        namespace cvtti_helpers
        {
            template <typename trainer_type, typename in_sample_vector_type>
            struct job
            {
                typedef typename trainer_type::scalar_type scalar_type;
                typedef typename trainer_type::sample_type sample_type;
                typedef typename trainer_type::mem_manager_type mem_manager_type;
                typedef matrix<sample_type,0,1,mem_manager_type> sample_vector_type;
                typedef matrix<scalar_type,0,1,mem_manager_type> scalar_vector_type;

                job() : x(0) {}

                trainer_type trainer;
                matrix<long,0,1> x_test, x_train;
                scalar_vector_type y_test, y_train;
                const in_sample_vector_type* x;
            };

            struct task
            {
                template <
                    typename trainer_type,
                    typename mem_manager_type,
                    typename in_sample_vector_type
                    >
                void operator()(
                    job<trainer_type,in_sample_vector_type>& j,
                    matrix<double,1,2,mem_manager_type>& result
                )
                {
                    try
                    {
                        result = test_binary_decision_function(j.trainer.train(rowm(*j.x,j.x_train), j.y_train), rowm(*j.x,j.x_test), j.y_test);

                        // Do this just to make j release it's memory since people might run threaded cross validation
                        // on very large datasets.  Every bit of freed memory helps out.
                        j = job<trainer_type,in_sample_vector_type>();
                    }
                    catch (invalid_nu_error&)
                    {
                        // If this is a svm_nu_trainer then we might get this exception if the nu is
                        // invalid.  In this case just return a cross validation score of 0.
                        result = 0;
                    }
                    catch (std::bad_alloc&)
                    {
                        std::cerr << "\nstd::bad_alloc thrown while running cross_validate_trainer_threaded().  Not enough memory.\n" << std::endl;
                        throw;
                    }
                }
            };
        }

        template <
            typename trainer_type,
            typename in_sample_vector_type,
            typename in_scalar_vector_type
            >
        const matrix<double, 1, 2, typename trainer_type::mem_manager_type>
        cross_validate_trainer_threaded_impl (
            const trainer_type& trainer,
            const in_sample_vector_type& x,
            const in_scalar_vector_type& y,
            const long folds,
            const long num_threads
        )
        {
            using namespace dlib::cvtti_helpers;
            typedef typename trainer_type::mem_manager_type mem_manager_type;

            // make sure requires clause is not broken
            DLIB_ASSERT(is_binary_classification_problem(x,y) == true &&
                        1 < folds && folds <= std::min(sum(y>0),sum(y<0)) &&
                        num_threads > 0,
                "\tmatrix cross_validate_trainer()"
                << "\n\t invalid inputs were given to this function"
                << "\n\t std::min(sum(y>0),sum(y<0)): " << std::min(sum(y>0),sum(y<0))
                << "\n\t folds:  " << folds
                << "\n\t num_threads:  " << num_threads
                << "\n\t is_binary_classification_problem(x,y): " << ((is_binary_classification_problem(x,y))? "true":"false")
                );

            task mytask;
            thread_pool tp(num_threads);

            // count the number of positive and negative examples
            long num_pos = 0;
            long num_neg = 0;
            for (long r = 0; r < y.nr(); ++r)
            {
                if (y(r) == +1.0)
                    ++num_pos;
                else
                    ++num_neg;
            }

            // figure out how many positive and negative examples we will have in each fold
            const long num_pos_test_samples = num_pos/folds;
            const long num_pos_train_samples = num_pos - num_pos_test_samples;
            const long num_neg_test_samples = num_neg/folds;
            const long num_neg_train_samples = num_neg - num_neg_test_samples;

            long pos_idx = 0;
            long neg_idx = 0;

            std::vector<future<job<trainer_type,in_sample_vector_type> > > jobs(folds);
            std::vector<future<matrix<double, 1, 2, mem_manager_type> > > results(folds);

            for (long i = 0; i < folds; ++i)
            {
                job<trainer_type,in_sample_vector_type>& j = jobs[i].get();

                j.x = &x;
                j.x_test.set_size (num_pos_test_samples  + num_neg_test_samples);
                j.y_test.set_size (num_pos_test_samples  + num_neg_test_samples);
                j.x_train.set_size(num_pos_train_samples + num_neg_train_samples);
                j.y_train.set_size(num_pos_train_samples + num_neg_train_samples);
                j.trainer = trainer;

                long cur = 0;

                // load up our positive test samples
                while (cur < num_pos_test_samples)
                {
                    if (y(pos_idx) == +1.0)
                    {
                        j.x_test(cur) = pos_idx;
                        j.y_test(cur) = +1.0;
                        ++cur;
                    }
                    pos_idx = (pos_idx+1)%x.nr();
                }

                // load up our negative test samples
                while (cur < j.x_test.nr())
                {
                    if (y(neg_idx) == -1.0)
                    {
                        j.x_test(cur) = neg_idx;
                        j.y_test(cur) = -1.0;
                        ++cur;
                    }
                    neg_idx = (neg_idx+1)%x.nr();
                }

                // load the training data from the data following whatever we loaded
                // as the testing data
                long train_pos_idx = pos_idx;
                long train_neg_idx = neg_idx;
                cur = 0;

                // load up our positive train samples
                while (cur < num_pos_train_samples)
                {
                    if (y(train_pos_idx) == +1.0)
                    {
                        j.x_train(cur) = train_pos_idx;
                        j.y_train(cur) = +1.0;
                        ++cur;
                    }
                    train_pos_idx = (train_pos_idx+1)%x.nr();
                }

                // load up our negative train samples
                while (cur < j.x_train.nr())
                {
                    if (y(train_neg_idx) == -1.0)
                    {
                        j.x_train(cur) = train_neg_idx;
                        j.y_train(cur) = -1.0;
                        ++cur;
                    }
                    train_neg_idx = (train_neg_idx+1)%x.nr();
                }

                // finally spawn a task to process this job
                tp.add_task(mytask, jobs[i], results[i]);

            } // for (long i = 0; i < folds; ++i)

            matrix<double, 1, 2, mem_manager_type> res;
            set_all_elements(res,0);

            // now compute the total results
            for (long i = 0; i < folds; ++i)
            {
                res += results[i].get();
            }

            return res/(double)folds;
        }

        template <
            typename trainer_type,
            typename in_sample_vector_type,
            typename in_scalar_vector_type
            >
        const matrix<double, 1, 2, typename trainer_type::mem_manager_type>
        cross_validate_trainer_threaded (
            const trainer_type& trainer,
            const in_sample_vector_type& x,
            const in_scalar_vector_type& y,
            const long folds,
            const long num_threads
        )
        {
            return cross_validate_trainer_threaded_impl(trainer,
                                               mat(x),
                                               mat(y),
                                               folds,
                                               num_threads);
        }

    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_SVm_THREADED_