dlib C++ Library - cross_validate_sequence_segmenter.h

    // Copyright (C) 2013  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_CROSS_VALIDATE_SEQUENCE_sEGMENTER_Hh_
    #define DLIB_CROSS_VALIDATE_SEQUENCE_sEGMENTER_Hh_

    #include "cross_validate_sequence_segmenter_abstract.h"
    #include "sequence_segmenter.h"

    namespace dlib
    {

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

        namespace impl
        {
            template <
                typename sequence_segmenter_type,
                typename sequence_type
                >
            const matrix<double,1,3> raw_metrics_test_sequence_segmenter (
                const sequence_segmenter_type& segmenter,
                const std::vector<sequence_type>& samples,
                const std::vector<std::vector<std::pair<unsigned long,unsigned long> > >& segments
            )
            {
                std::vector<std::pair<unsigned long,unsigned long> > truth;
                std::vector<std::pair<unsigned long,unsigned long> > pred;

                double true_hits = 0;
                double total_detections = 0;
                double total_true_segments = 0;

                for (unsigned long i = 0; i < samples.size(); ++i)
                {
                    segmenter.segment_sequence(samples[i], pred);
                    truth = segments[i];
                    // sort the segments so they will be in the same orders
                    std::sort(truth.begin(), truth.end());
                    std::sort(pred.begin(), pred.end());

                    total_true_segments += truth.size();
                    total_detections += pred.size();

                    unsigned long j=0,k=0;
                    while (j < pred.size() && k < truth.size())
                    {
                        if (pred[j].first == truth[k].first &&
                            pred[j].second == truth[k].second)
                        {
                            ++true_hits;
                            ++j;
                            ++k;
                        }
                        else if (pred[j].first < truth[k].first)
                        {
                            ++j;
                        }
                        else
                        {
                            ++k;
                        }
                    }
                }

                matrix<double,1,3> res;
                res = total_detections, total_true_segments, true_hits;
                return res;
            }
        }

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

        template <
            typename sequence_segmenter_type,
            typename sequence_type
            >
        const matrix<double,1,3> test_sequence_segmenter (
            const sequence_segmenter_type& segmenter,
            const std::vector<sequence_type>& samples,
            const std::vector<std::vector<std::pair<unsigned long,unsigned long> > >& segments
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT( is_sequence_segmentation_problem(samples, segments) == true,
                        "\tmatrix test_sequence_segmenter()"
                        << "\n\t invalid inputs were given to this function"
                        << "\n\t is_sequence_segmentation_problem(samples, segments): "
                        << is_sequence_segmentation_problem(samples, segments));

            const matrix<double,1,3> metrics = impl::raw_metrics_test_sequence_segmenter(segmenter, samples, segments);

            const double total_detections    = metrics(0);
            const double total_true_segments = metrics(1);
            const double true_hits           = metrics(2);

            const double precision = (total_detections   ==0) ? 1 : true_hits/total_detections;
            const double recall    = (total_true_segments==0) ? 1 : true_hits/total_true_segments;
            const double f1        = (precision+recall   ==0) ? 0 : 2*precision*recall/(precision+recall);

            matrix<double,1,3> res;
            res = precision, recall, f1;
            return res;
        }

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

        template <
            typename trainer_type,
            typename sequence_type
            >
        const matrix<double,1,3> cross_validate_sequence_segmenter (
            const trainer_type& trainer,
            const std::vector<sequence_type>& samples,
            const std::vector<std::vector<std::pair<unsigned long,unsigned long> > >& segments,
            const long folds
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT( is_sequence_segmentation_problem(samples, segments) == true &&
                        1 < folds && folds <= static_cast<long>(samples.size()),
                        "\tmatrix cross_validate_sequence_segmenter()"
                        << "\n\t invalid inputs were given to this function"
                        << "\n\t folds:  " << folds
                        << "\n\t is_sequence_segmentation_problem(samples, segments): "
                        << is_sequence_segmentation_problem(samples, segments));

            const long num_in_test = samples.size()/folds;
            const long num_in_train = samples.size() - num_in_test;

            std::vector<sequence_type> x_test, x_train;
            std::vector<std::vector<std::pair<unsigned long,unsigned long> > > y_test, y_train;

            long next_test_idx = 0;

            matrix<double,1,3> metrics;
            metrics = 0;

            for (long i = 0; i < folds; ++i)
            {
                x_test.clear();
                y_test.clear();
                x_train.clear();
                y_train.clear();

                // load up the test samples
                for (long cnt = 0; cnt < num_in_test; ++cnt)
                {
                    x_test.push_back(samples[next_test_idx]);
                    y_test.push_back(segments[next_test_idx]);
                    next_test_idx = (next_test_idx + 1)%samples.size();
                }

                // load up the training samples
                long next = next_test_idx;
                for (long cnt = 0; cnt < num_in_train; ++cnt)
                {
                    x_train.push_back(samples[next]);
                    y_train.push_back(segments[next]);
                    next = (next + 1)%samples.size();
                }

                metrics += impl::raw_metrics_test_sequence_segmenter(trainer.train(x_train,y_train), x_test, y_test);
            } // for (long i = 0; i < folds; ++i)

            const double total_detections    = metrics(0);
            const double total_true_segments = metrics(1);
            const double true_hits           = metrics(2);

            const double precision = (total_detections   ==0) ? 1 : true_hits/total_detections;
            const double recall    = (total_true_segments==0) ? 1 : true_hits/total_true_segments;
            const double f1        = (precision+recall   ==0) ? 0 : 2*precision*recall/(precision+recall);

            matrix<double,1,3> res;
            res = precision, recall, f1;
            return res;
        }

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

    }

    #endif // DLIB_CROSS_VALIDATE_SEQUENCE_sEGMENTER_Hh_