Reorganized sources, better accuracy for similarities based on features
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emeric
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/*
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* Copyright (C) 2018 Emeric Poupon
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*
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* This file is part of LMS.
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*
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* LMS is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* LMS is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with LMS. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include "SimilarityFeaturesSearcher.hpp"
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#include <random>
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#include "database/Artist.hpp"
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#include "database/SimilaritySettings.hpp"
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#include "database/Release.hpp"
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#include "database/Track.hpp"
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#include "database/TrackFeatures.hpp"
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#include "som/DataNormalizer.hpp"
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#include "utils/Logger.hpp"
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#include "utils/Utils.hpp"
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namespace Similarity {
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FeaturesSearcher::FeaturesSearcher(Wt::Dbo::Session& session)
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{
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Wt::Dbo::Transaction transaction(session);
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auto settings = Database::SimilaritySettings::get(session);
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struct FeatureInfo
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{
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std::size_t nbDimensions;
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double weight;
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};
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std::map<std::string, FeatureInfo> featuresInfo;
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std::size_t nbDimensions = 0;
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for (auto feature : settings->getFeatures())
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{
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featuresInfo[feature->getName()] = { feature->getNbDimensions(), feature->getWeight() };
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nbDimensions += feature->getNbDimensions();
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Getting Tracks with features...";
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auto tracks = Database::Track::getAllWithFeatures(session);
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LMS_LOG(SIMILARITY, DEBUG) << "Getting Tracks with features DONE";
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std::vector<SOM::InputVector> samples;
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std::vector<Database::IdType> tracksIds;
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LMS_LOG(SIMILARITY, DEBUG) << "Extracting features...";
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for (auto track : tracks)
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{
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SOM::InputVector sample;
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std::map<std::string, std::vector<double>> features;
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for (const auto& featureInfo : featuresInfo)
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features[featureInfo.first] = {};
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if (!track->getTrackFeatures()->getFeatures(features))
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continue;
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// Check dimensions for each feature
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bool ok = true;
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for (const auto& feature : features)
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{
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auto it = featuresInfo.find(feature.first);
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if (it == featuresInfo.end() || it->second.nbDimensions != feature.second.size())
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{
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LMS_LOG(SIMILARITY, WARNING) << "Dimension mismatch for feature '" << feature.first << "'. Expected " << it->second.nbDimensions << ", got " << feature.second.size();
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ok = false;
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break;
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}
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sample.insert( sample.end(), feature.second.begin(), feature.second.end() );
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}
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if (!ok)
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continue;
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samples.emplace_back(std::move(sample));
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tracksIds.emplace_back(track.id());
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Extracting features DONE";
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transaction.commit();
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if (tracksIds.empty())
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{
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LMS_LOG(SIMILARITY, INFO) << "Nothing to classify!";
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return;
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Normalizing data...";
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SOM::DataNormalizer normalizer(nbDimensions);
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normalizer.computeNormalizationFactors(samples);
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for (auto& sample : samples)
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normalizer.normalizeData(sample);
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std::size_t size = std::sqrt(samples.size()/2);
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LMS_LOG(SIMILARITY, INFO) << "Found " << samples.size() << " tracks, constructing a " << size << "*" << size << " network";
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_network = std::make_unique<SOM::Network>(size, size, nbDimensions);
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_artistsMap = SOM::Matrix<std::set<Database::IdType>>(size, size);
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_releasesMap = SOM::Matrix<std::set<Database::IdType>>(size, size);
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_tracksMap = SOM::Matrix<std::set<Database::IdType>>(size, size);
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std::vector<double> weights;
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for (const auto& featureInfo : featuresInfo)
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{
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for (std::size_t i = 0; i < featureInfo.second.nbDimensions; ++i)
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weights.push_back(1. / featureInfo.second.nbDimensions * featureInfo.second.weight);
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Training network...";
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_network->train(samples, 20);
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LMS_LOG(SIMILARITY, DEBUG) << "Training network DONE";
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LMS_LOG(SIMILARITY, DEBUG) << "Classifying tracks...";
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for (std::size_t i = 0; i < samples.size(); ++i)
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{
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Wt::Dbo::Transaction transaction(session);
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const auto& sample = samples[i];
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auto trackId = tracksIds[i];
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auto coords = _network->getClosestRefVectorCoords(sample);
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_trackCoords[trackId].insert(coords);
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_tracksMap[coords].insert(trackId);
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auto track = Database::Track::getById(session, trackId);
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if (track->getRelease())
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{
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_releaseCoords[track->getRelease().id()].insert(coords);
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_releasesMap[coords].insert(track->getRelease().id());
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}
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if (track->getArtist())
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{
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_artistCoords[track->getArtist().id()].insert(coords);
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_artistsMap[coords].insert(track->getArtist().id());
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}
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}
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LMS_LOG(SIMILARITY, DEBUG) << "Classifying tracks DONE";
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}
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std::vector<Database::IdType>
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FeaturesSearcher::getSimilarTracks(const std::set<Database::IdType>& tracksIds, std::size_t maxCount) const
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{
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return getSimilarObjects(tracksIds, _tracksMap, _trackCoords, maxCount);
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}
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std::vector<Database::IdType>
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FeaturesSearcher::getSimilarReleases(Database::IdType releaseId, std::size_t maxCount) const
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{
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return getSimilarObjects({releaseId}, _releasesMap, _releaseCoords, maxCount);
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}
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std::vector<Database::IdType>
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FeaturesSearcher::getSimilarArtists(Database::IdType artistId, std::size_t maxCount) const
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{
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return getSimilarObjects({artistId}, _artistsMap, _artistCoords, maxCount);
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}
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#if 0
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void
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FeaturesSearcher::dump(Wt::Dbo::Session& session, std::ostream& os) const
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{
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os << "Number of tracks classified: " << _trackIdsCoords.size() << std::endl;
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os << "Network size: " << _network.getWidth() << " * " << _network.getHeight() << std::endl;
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Wt::Dbo::Transaction transaction(session);
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for (std::size_t y = 0; y < _network.getHeight(); ++y)
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{
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for (std::size_t x = 0; x < _network.getWidth(); ++x)
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{
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const auto& trackIds = _tracksMap[{x, y}];
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for (auto trackId : trackIds)
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{
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auto track = Database::Track::getById(session, trackId);
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if (!track)
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continue;
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os << "{";
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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 << "} ";
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}
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os << "; ";
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}
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os << std::endl;
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}
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}
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#endif
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static
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std::set<SOM::Coords>
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getMatchingRefVectorsCoords(const std::set<Database::IdType>& ids, const std::map<Database::IdType, std::set<SOM::Coords>>& objectCoords)
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{
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std::set<SOM::Coords> res;
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if (ids.empty())
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return res;
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for (auto id : ids)
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{
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auto it = objectCoords.find(id);
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if (it == objectCoords.end())
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continue;
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for (const auto& coords : it->second)
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res.insert(coords);
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}
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return res;
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}
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static
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std::set<Database::IdType>
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getObjectsIds(const std::set<SOM::Coords>& coordsSet, const SOM::Matrix<std::set<Database::IdType>>& objectsMap )
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{
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std::set<Database::IdType> res;
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for (const auto& coords : coordsSet)
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{
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for (auto id : objectsMap.get(coords))
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res.insert(id);
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}
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return res;
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}
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std::vector<Database::IdType>
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FeaturesSearcher::getSimilarObjects(const std::set<Database::IdType>& ids,
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const SOM::Matrix<std::set<Database::IdType>>& objectsMap,
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const std::map<Database::IdType, std::set<SOM::Coords>>& objectCoords,
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std::size_t maxCount) const
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{
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std::vector<Database::IdType> res;
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auto now = std::chrono::system_clock::now();
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std::mt19937 randGenerator(std::chrono::duration_cast<std::chrono::milliseconds>(now.time_since_epoch()).count());
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std::set<SOM::Coords> searchedRefVectorsCoords = getMatchingRefVectorsCoords(ids, objectCoords);
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if (searchedRefVectorsCoords.empty())
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return res;
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while (1)
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{
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std::set<Database::IdType> closestObjectIds = getObjectsIds(searchedRefVectorsCoords, objectsMap);
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// Remove objects that are already in input
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for (auto id : ids)
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closestObjectIds.erase(id);
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{
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std::vector<Database::IdType> objectIdsToAdd(closestObjectIds.begin(), closestObjectIds.end());
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std::shuffle(objectIdsToAdd.begin(), objectIdsToAdd.end(), randGenerator);
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std::copy(objectIdsToAdd.begin(), objectIdsToAdd.end(), std::back_inserter(res));
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}
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if (res.size() > maxCount)
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res.resize(maxCount);
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if (res.size() == maxCount)
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break;
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// If there is not enough objects, try again with closest neighbour until there is too much distance
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auto closestRefVectorCoords = _network->getClosestRefVectorCoords(searchedRefVectorsCoords, _networkRefVectorsDistanceMedian * 0.75);
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if (!closestRefVectorCoords)
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break;
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searchedRefVectorsCoords.insert(*closestRefVectorCoords);
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}
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return res;
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}
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} // ns Similarity
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