Refactored the inputvector part of the som implementation
This commit is contained in:
emeric
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#include <stdlib.h>
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#include <stdexcept>
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#include <iostream>
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#include <string>
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#include <chrono>
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#include <random>
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#include "database/DatabaseHandler.hpp"
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#include "database/Track.hpp"
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#include "database/Artist.hpp"
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#include "database/Cluster.hpp"
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#include "database/Release.hpp"
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#include "database/TrackFeatures.hpp"
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#include "utils/Config.hpp"
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#include "similarity/features/som/DataNormalizer.hpp"
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#include "similarity/features/som/Network.hpp"
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static
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std::ostream& operator<<(std::ostream& os, const Database::Track::pointer& track)
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{
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auto genreClusterType = Database::ClusterType::getByName(*track->session(), "GENRE");
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os << "[";
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auto genreClusters = track->getClusterGroups({genreClusterType}, 1);
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for (auto genreCluster : genreClusters)
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os << genreCluster.front()->getName() << " - ";
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if (track->getArtist())
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os << track->getArtist()->getName() << " - ";
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if (track->getRelease())
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os << track->getRelease()->getName() << " - ";
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os << track->getName() << "]";
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return os;
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}
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static
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bool
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getTrackFeatures(Wt::Dbo::Session &session, const Database::Track::pointer& track, const std::map<std::string, std::size_t>& featuresSettings, SOM::InputVector& res)
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{
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std::map<std::string, std::vector<double>> features;
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for (const auto& featureSettings : featuresSettings)
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features[featureSettings.first] = {};
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if (!track->getTrackFeatures()->getFeatures(features))
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{
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std::cout << "Skipping track '" << track->getMBID() << "': missing item" << std::endl;
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return false;
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};
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std::size_t index {};
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for (const auto& feature : features)
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{
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auto it = featuresSettings.find(feature.first);
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if (it == featuresSettings.end() || (feature.second.size() != it->second))
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return false;
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for (double value : feature.second)
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res[index++] = value;
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}
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return true;
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}
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int main(int argc, char *argv[])
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{
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try
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{
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const std::size_t width = 10;
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const std::size_t height = 10;
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const std::size_t nbIterations = 20;
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std::size_t nbTracks = 5000;
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const std::map<std::string, std::size_t> featuresSettings =
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{
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// { "lowlevel.average_loudness", 1 },
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// { "lowlevel.dynamic_complexity", 1 },
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{ "lowlevel.spectral_contrast_coeffs.median", 6 },
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{ "lowlevel.erbbands.median", 40 },
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{ "tonal.hpcp.median", 36 },
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{ "lowlevel.melbands.median", 40 },
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{ "lowlevel.barkbands.median", 27 },
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{ "lowlevel.mfcc.mean", 13 },
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{ "lowlevel.gfcc.mean", 13 },
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};
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std::size_t nbDims = 0;
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for (const auto& featureSettings : featuresSettings)
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nbDims += featureSettings.second;
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boost::filesystem::path configFilePath = "/etc/lms.conf";
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if (argc >= 2)
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configFilePath = std::string(argv[1], 0, 256);
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Config::instance().setFile(configFilePath);
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Database::Handler::configureAuth();
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auto connectionPool = Database::Handler::createConnectionPool(Config::instance().getPath("working-dir") / "lms.db");
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Database::Handler db(*connectionPool);
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std::cout << "Getting all features..." << std::endl;
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Wt::Dbo::Transaction transaction(db.getSession());
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auto tracks = Database::Track::getAllWithFeatures(db.getSession(), nbTracks);
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nbTracks = tracks.size();
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std::cout << "Getting features DONE (" << nbTracks << " tracks)" << std::endl;
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std::cout << "Reading features..." << std::endl;
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std::vector<SOM::InputVector> tracksFeatures;
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for (const auto& track : tracks)
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{
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SOM::InputVector features {nbDims};
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if (!getTrackFeatures(db.getSession(), track, featuresSettings, features))
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continue;
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tracksFeatures.emplace_back(std::move(features));
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}
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std::cout << "Reading features DONE" << std::endl;
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SOM::Network network {width, height, nbDims};
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SOM::DataNormalizer normalizer {nbDims};
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SOM::InputVector weights {nbDims};
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for (const auto& featureSettings : featuresSettings)
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{
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for (std::size_t i {}; i < featureSettings.second; ++i)
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weights[i] = SOM::InputVector::value_type{1. / featureSettings.second};
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}
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network.setDataWeights(weights);
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std::cout << "Normalizing..." << std::endl;
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normalizer.computeNormalizationFactors(tracksFeatures);
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std::cout << "Dumping normalizer: " << std::endl;
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normalizer.dump(std::cout);
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std::cout << "Dumping normalizer DONE" << std::endl;
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for (SOM::InputVector& features : tracksFeatures)
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normalizer.normalizeData(features);
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std::cout << "Normalizing DONE" << std::endl;
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auto progress {[](const SOM::Network::CurrentIteration& iteration)
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{
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std::cout << "Iteration " << iteration.idIteration + 1 << " of " << iteration.iterationCount << std::endl;;
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}};
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std::cout << "Training..." << std::endl;
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network.train(tracksFeatures, nbIterations, progress);
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std::cout << "Training DONE" << std::endl;
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auto meanDistance = network.computeRefVectorsDistanceMean();
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std::cout << "MEAN distance = " << meanDistance << std::endl;
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auto medianDistance = network.computeRefVectorsDistanceMedian();
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std::cout << "MEDIAN distance = " << medianDistance << std::endl;
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std::cout << "Classifying tracks..." << std::endl;
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SOM::Matrix< std::vector<Database::Track::pointer> > tracksMap(width, height);
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for (auto track : tracks)
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{
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SOM::InputVector features {nbDims};
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if (!getTrackFeatures(db.getSession(), track, featuresSettings, features))
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continue;
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normalizer.normalizeData(features);
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SOM::Position position = network.getClosestRefVectorPosition(features);
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tracksMap[position].push_back(track);
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}
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std::cout << "Classifying tracks DONE" << std::endl;
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// Dump tracks
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for (SOM::Coordinate y = 0; y < tracksMap.getHeight(); ++y)
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{
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for (SOM::Coordinate x = 0; x < tracksMap.getWidth(); ++x)
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{
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std::cout << "{" << x << ", " << y << "}" << std::endl;
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const auto& tracks = tracksMap[{x, y}];
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for (const auto& track : tracks)
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{
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std::cout << " - " << track << std::endl;
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}
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}
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}
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// For each track, get the nearest tracks
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for (const auto& track : tracks)
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{
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SOM::InputVector features {nbDims};
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if (!getTrackFeatures(db.getSession(), track, featuresSettings, features))
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continue;
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normalizer.normalizeData(features);
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SOM::Position refVectorPosition {network.getClosestRefVectorPosition(features)};
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std::cout << "Getting nearest songs for track " << track << " in {" << refVectorPosition.x << ", " << refVectorPosition.y << "}:" << std::endl;
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for (auto similarTrack : tracksMap[refVectorPosition])
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std::cout << " - " << similarTrack << std::endl;
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std::set<SOM::Position> neighbourPosition {refVectorPosition};
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for (std::size_t i {}; i < 3; ++i)
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{
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auto position = network.getClosestRefVectorPosition(neighbourPosition, medianDistance);
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if (!position)
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break;
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std::cout << " - in {" << position->x << ", " << position->y << "}, dist = " << network.getRefVectorsDistance(*position, refVectorPosition) << std::endl;
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for (const auto& similarTrack : tracksMap[*position])
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std::cout << " - " << similarTrack << std::endl;
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neighbourPosition.insert(*position);
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}
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}
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}
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catch( std::exception& e)
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{
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std::cerr << "Caught exception: " << e.what() << std::endl;
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}
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return EXIT_SUCCESS;
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}
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