Migrated scrobbling stuff
This commit is contained in:
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 "FeaturesEngine.hpp"
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#include <numeric>
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#include "database/Artist.hpp"
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#include "database/Db.hpp"
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#include "database/Release.hpp"
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#include "database/Session.hpp"
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#include "database/Track.hpp"
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#include "database/TrackArtistLink.hpp"
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#include "database/TrackFeatures.hpp"
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#include "database/TrackList.hpp"
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#include "som/DataNormalizer.hpp"
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#include "utils/Logger.hpp"
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#include "utils/Random.hpp"
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namespace Recommendation {
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std::unique_ptr<IEngine> createFeaturesEngine(Database::Db& db)
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{
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return std::make_unique<FeaturesEngine>(db);
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}
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const FeatureSettingsMap&
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FeaturesEngine::getDefaultTrainFeatureSettings()
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{
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static const FeatureSettingsMap defaultTrainFeatureSettings
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{
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{ "lowlevel.spectral_energyband_high.mean", {1}},
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{ "lowlevel.spectral_rolloff.median", {1}},
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{ "lowlevel.spectral_contrast_valleys.var", {1}},
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{ "lowlevel.erbbands.mean", {1}},
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{ "lowlevel.gfcc.mean", {1}},
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};
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return defaultTrainFeatureSettings;
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}
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static
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std::optional<FeatureValuesMap>
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getTrackFeatureValues(FeaturesEngine::FeaturesFetchFunc func, Database::TrackId trackId, const std::unordered_set<FeatureName>& featureNames)
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{
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return func(trackId, featureNames);
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}
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static
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std::optional<FeatureValuesMap>
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getTrackFeatureValuesFromDb(Database::Session& session, Database::TrackId trackId, const std::unordered_set<FeatureName>& featureNames)
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{
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auto func = [&](Database::TrackId trackId, const std::unordered_set<FeatureName>& featureNames)
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{
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std::optional<FeatureValuesMap> res;
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auto transaction {session.createSharedTransaction()};
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Database::Track::pointer track {Database::Track::getById(session, trackId)};
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if (!track)
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return res;
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res = track->getTrackFeatures()->getFeatureValuesMap(featureNames);
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if (res->empty())
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res.reset();
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return res;
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};
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return getTrackFeatureValues(func, trackId, featureNames);
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}
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static
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std::optional<SOM::InputVector>
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convertFeatureValuesMapToInputVector(const FeatureValuesMap& featureValuesMap, std::size_t nbDimensions)
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{
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std::size_t i {};
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std::optional<SOM::InputVector> res {SOM::InputVector {nbDimensions}};
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for (const auto& [featureName, values] : featureValuesMap)
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{
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if (values.size() != getFeatureDef(featureName).nbDimensions)
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{
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LMS_LOG(RECOMMENDATION, WARNING) << "Dimension mismatch for feature '" << featureName << "'. Expected " << getFeatureDef(featureName).nbDimensions << ", got " << values.size();
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res.reset();
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break;
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}
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for (double val : values)
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(*res)[i++] = val;
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}
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return res;
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}
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static
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SOM::InputVector
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getInputVectorWeights(const FeatureSettingsMap& featureSettingsMap, std::size_t nbDimensions)
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{
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SOM::InputVector weights {nbDimensions};
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std::size_t index {};
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for (const auto& [featureName, featureSettings] : featureSettingsMap)
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{
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const std::size_t featureNbDimensions {getFeatureDef(featureName).nbDimensions};
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for (std::size_t i {}; i < featureNbDimensions; ++i)
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weights[index++] = (1. / featureNbDimensions * featureSettings.weight);
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}
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assert(index == nbDimensions);
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return weights;
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}
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void
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FeaturesEngine::loadFromTraining(const TrainSettings& trainSettings, const ProgressCallback& progressCallback)
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{
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LMS_LOG(RECOMMENDATION, INFO) << "Constructing features classifier...";
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std::unordered_set<FeatureName> featureNames;
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std::transform(std::cbegin(trainSettings.featureSettingsMap), std::cend(trainSettings.featureSettingsMap), std::inserter(featureNames, std::begin(featureNames)),
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[](const auto& itFeatureSetting) { return itFeatureSetting.first; });
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const std::size_t nbDimensions {std::accumulate(std::cbegin(featureNames), std::cend(featureNames), std::size_t {0},
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[](std::size_t sum, const FeatureName& featureName) { return sum + getFeatureDef(featureName).nbDimensions; })};
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LMS_LOG(RECOMMENDATION, DEBUG) << "Features dimension = " << nbDimensions;
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Database::Session& session {_db.getTLSSession()};
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std::vector<Database::TrackId> trackIds;
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{
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auto transaction {session.createSharedTransaction()};
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LMS_LOG(RECOMMENDATION, DEBUG) << "Getting Tracks with features...";
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trackIds = Database::Track::getAllIdsWithFeatures(session);
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LMS_LOG(RECOMMENDATION, DEBUG) << "Getting Tracks with features DONE (found " << trackIds.size() << " tracks)";
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}
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std::vector<SOM::InputVector> samples;
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std::vector<Database::TrackId> samplesTrackIds;
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samples.reserve(trackIds.size());
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samplesTrackIds.reserve(trackIds.size());
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LMS_LOG(RECOMMENDATION, DEBUG) << "Extracting features...";
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for (Database::TrackId trackId : trackIds)
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{
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if (_loadCancelled)
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return;
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std::optional<FeatureValuesMap> featureValuesMap;
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if (_featuresFetchFunc)
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featureValuesMap = getTrackFeatureValues(_featuresFetchFunc, trackId, featureNames);
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else
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featureValuesMap = getTrackFeatureValuesFromDb(session, trackId, featureNames);
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if (!featureValuesMap)
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continue;
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std::optional<SOM::InputVector> inputVector {convertFeatureValuesMapToInputVector(*featureValuesMap, nbDimensions)};
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if (!inputVector)
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continue;
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samples.emplace_back(std::move(*inputVector));
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samplesTrackIds.emplace_back(trackId);
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}
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LMS_LOG(RECOMMENDATION, DEBUG) << "Extracting features DONE";
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if (samples.empty())
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{
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LMS_LOG(RECOMMENDATION, INFO) << "Nothing to classify!";
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return;
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}
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LMS_LOG(RECOMMENDATION, DEBUG) << "Normalizing data...";
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SOM::DataNormalizer dataNormalizer {nbDimensions};
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dataNormalizer.computeNormalizationFactors(samples);
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for (auto& sample : samples)
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dataNormalizer.normalizeData(sample);
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SOM::Coordinate size {static_cast<SOM::Coordinate>(std::sqrt(samples.size() / trainSettings.sampleCountPerNeuron))};
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if (size < 2)
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{
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LMS_LOG(RECOMMENDATION, WARNING) << "Very few tracks (" << samples.size() << ") are being used by the features engine, expect bad behaviors";
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size = 2;
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}
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LMS_LOG(RECOMMENDATION, INFO) << "Found " << samples.size() << " tracks, constructing a " << size << "*" << size << " network";
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SOM::Network network {size, size, nbDimensions};
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SOM::InputVector weights {getInputVectorWeights(trainSettings.featureSettingsMap, nbDimensions)};
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network.setDataWeights(weights);
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auto somProgressCallback{[&](const SOM::Network::CurrentIteration& iter)
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{
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LMS_LOG(RECOMMENDATION, DEBUG) << "Current pass = " << iter.idIteration << " / " << iter.iterationCount;
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progressCallback(Progress {iter.idIteration, iter.iterationCount});
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}};
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LMS_LOG(RECOMMENDATION, DEBUG) << "Training network...";
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network.train(samples, trainSettings.iterationCount,
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progressCallback ? somProgressCallback : SOM::Network::ProgressCallback {},
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[this] { return _loadCancelled; });
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LMS_LOG(RECOMMENDATION, DEBUG) << "Training network DONE";
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LMS_LOG(RECOMMENDATION, DEBUG) << "Classifying tracks...";
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TrackPositions trackPositions;
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for (std::size_t i {}; i < samples.size(); ++i)
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{
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if (_loadCancelled)
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return;
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const SOM::Position position {network.getClosestRefVectorPosition(samples[i])};
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trackPositions[samplesTrackIds[i]].push_back(position);
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}
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LMS_LOG(RECOMMENDATION, DEBUG) << "Classifying tracks DONE";
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load(std::move(network), std::move(trackPositions));
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}
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void
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FeaturesEngine::loadFromCache(FeaturesEngineCache cache)
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{
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LMS_LOG(RECOMMENDATION, INFO) << "Constructing features classifier from cache...";
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load(std::move(cache._network), cache._trackPositions);
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}
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IEngine::TrackContainer
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FeaturesEngine::getSimilarTracksFromTrackList(Database::TrackListId trackListId, std::size_t maxCount) const
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{
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const TrackContainer trackIds {[&]
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{
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TrackContainer res;
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Database::Session& session {_db.getTLSSession()};
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auto transaction {session.createSharedTransaction()};
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const Database::TrackList::pointer trackList {Database::TrackList::getById(session, trackListId)};
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if (trackList)
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res = trackList->getTrackIds();
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return res;
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}()};
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return getSimilarTracks(trackIds, maxCount);
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}
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IEngine::TrackContainer
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FeaturesEngine::getSimilarTracks(const std::vector<Database::TrackId>& tracksIds, std::size_t maxCount) const
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{
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auto similarTrackIds {getSimilarObjects(tracksIds, _trackMatrix, _trackPositions, maxCount)};
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Database::Session& session {_db.getTLSSession()};
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{
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// Report only existing ids, as tracks may have been removed a long time ago (refreshing the SOM takes some time)
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auto transaction {session.createSharedTransaction()};
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similarTrackIds.erase(std::remove_if(std::begin(similarTrackIds), std::end(similarTrackIds),
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[&](Database::TrackId trackId)
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{
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return !Database::Track::exists(session, trackId);
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}), std::end(similarTrackIds));
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}
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return similarTrackIds;
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}
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IEngine::ReleaseContainer
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FeaturesEngine::getSimilarReleases(Database::ReleaseId releaseId, std::size_t maxCount) const
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{
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auto similarReleaseIds {getSimilarObjects({releaseId}, _releaseMatrix, _releasePositions, maxCount)};
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Database::Session& session {_db.getTLSSession()};
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if (!similarReleaseIds.empty())
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{
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// Report only existing ids
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auto transaction {session.createSharedTransaction()};
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similarReleaseIds.erase(std::remove_if(std::begin(similarReleaseIds), std::end(similarReleaseIds),
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[&](Database::ReleaseId releaseId)
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{
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return !Database::Release::exists(session, releaseId);
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}), std::end(similarReleaseIds));
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}
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return similarReleaseIds;
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}
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std::vector<Database::ArtistId>
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FeaturesEngine::getSimilarArtists(Database::ArtistId artistId, EnumSet<Database::TrackArtistLinkType> linkTypes, std::size_t maxCount) const
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{
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auto getSimilarArtistIdsForLinkType {[&] (Database::TrackArtistLinkType linkType)
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{
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std::vector<Database::ArtistId> similarArtistIds;
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const auto itArtists {_artistMatrix.find(linkType)};
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if (itArtists == std::cend(_artistMatrix))
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{
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return similarArtistIds;
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}
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return getSimilarObjects({artistId}, itArtists->second, _artistPositions, maxCount);
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}};
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std::unordered_set<Database::ArtistId> similarArtistIds;
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for (Database::TrackArtistLinkType linkType : linkTypes)
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{
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const auto similarArtistIdsForLinkType {getSimilarArtistIdsForLinkType(linkType)};
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similarArtistIds.insert(std::begin(similarArtistIdsForLinkType), std::end(similarArtistIdsForLinkType));
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}
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std::vector<Database::ArtistId> res(std::cbegin(similarArtistIds), std::cend(similarArtistIds));
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Database::Session& session {_db.getTLSSession()};
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{
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// Report only existing ids
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auto transaction {session.createSharedTransaction()};
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res.erase(std::remove_if(std::begin(res), std::end(res),
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[&](Database::ArtistId artistId)
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{
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return !Database::Artist::exists(session, artistId);
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}), std::end(res));
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}
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while (res.size() > maxCount)
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res.erase(Random::pickRandom(res));
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return res;
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}
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FeaturesEngineCache
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FeaturesEngine::toCache() const
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{
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return FeaturesEngineCache {*_network, _trackPositions};
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}
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void
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FeaturesEngine::load(bool forceReload, const ProgressCallback& progressCallback)
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{
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if (forceReload)
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{
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FeaturesEngineCache::invalidate();
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}
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else if (const std::optional<FeaturesEngineCache> cache {FeaturesEngineCache::read()})
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{
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loadFromCache(*cache);
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return;
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}
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TrainSettings trainSettings;
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trainSettings.featureSettingsMap = getDefaultTrainFeatureSettings();
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loadFromTraining(trainSettings, progressCallback);
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if (!_loadCancelled)
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toCache().write();
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}
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void
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FeaturesEngine::requestCancelLoad()
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{
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LMS_LOG(RECOMMENDATION, DEBUG) << "Requesting init cancellation";
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_loadCancelled = true;
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}
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void
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FeaturesEngine::load(const SOM::Network& network, const TrackPositions& trackPositions)
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{
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using namespace Database;
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_networkRefVectorsDistanceMedian = network.computeRefVectorsDistanceMedian();
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LMS_LOG(RECOMMENDATION, DEBUG) << "Median distance betweend ref vectors = " << _networkRefVectorsDistanceMedian;
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const SOM::Coordinate width {network.getWidth()};
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const SOM::Coordinate height {network.getHeight()};
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_releaseMatrix = ReleaseMatrix {width, height};
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_trackMatrix = TrackMatrix {width, height};
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LMS_LOG(RECOMMENDATION, DEBUG) << "Constructing maps...";
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Database::Session& session {_db.getTLSSession()};
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for (const auto& [trackId, positions] : trackPositions)
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{
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if (_loadCancelled)
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return;
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auto transaction {session.createSharedTransaction()};
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const Track::pointer track {Database::Track::getById(session, trackId)};
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if (!track)
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continue;
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for (const SOM::Position& position : positions)
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{
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Utils::push_back_if_not_present(_trackPositions[trackId], position);
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Utils::push_back_if_not_present(_trackMatrix[position], trackId);
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if (Release::pointer release {track->getRelease()})
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{
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const ReleaseId releaseId {release->getId()};
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Utils::push_back_if_not_present(_releasePositions[releaseId], position);
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Utils::push_back_if_not_present(_releaseMatrix[position], releaseId);
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}
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for (const TrackArtistLink::pointer& artistLink : track->getArtistLinks())
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{
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const ArtistId artistId {artistLink->getArtist()->getId()};
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Utils::push_back_if_not_present(_artistPositions[artistId], position);
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auto itArtists {_artistMatrix.find(artistLink->getType())};
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if (itArtists == std::cend(_artistMatrix))
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{
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auto [it, inserted] = _artistMatrix.try_emplace(artistLink->getType(), ArtistMatrix {width, height});
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assert(inserted);
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itArtists = it;
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}
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Utils::push_back_if_not_present(itArtists->second[position], artistId);
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}
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}
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}
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_network = std::make_unique<SOM::Network>(network);
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LMS_LOG(RECOMMENDATION, INFO) << "Classifier successfully loaded!";
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}
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} // ns Recommendation
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