Simplified logger configuration, it no longer depends on Wt
This commit is contained in:
emeric
@@ -26,7 +26,7 @@
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#include "playlist-constraints/ConsecutiveArtists.hpp"
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#include "playlist-constraints/ConsecutiveReleases.hpp"
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#include "playlist-constraints/DuplicateTracks.hpp"
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#include "utils/Logger.hpp"
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#include "utils/ILogger.hpp"
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namespace Recommendation
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{
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@@ -48,7 +48,7 @@ namespace Recommendation
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std::vector<TrackId> PlaylistGeneratorService::extendPlaylist(TrackListId tracklistId, std::size_t maxCount) const
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{
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LMS_LOG(RECOMMENDATION, DEBUG) << "Requested to extend playlist by " << maxCount << " similar tracks";
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LMS_LOG(RECOMMENDATION, DEBUG, "Requested to extend playlist by " << maxCount << " similar tracks");
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// supposed to be ordered from most similar to least similar
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std::vector<TrackId> similarTracks{ _recommendationService.findSimilarTracks(tracklistId, maxCount * 2) }; // ask for more tracks than we need as it will be easier to respect constraints
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@@ -29,7 +29,7 @@
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#include "services/database/Session.hpp"
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#include "services/database/ScanSettings.hpp"
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#include "utils/Exception.hpp"
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#include "utils/Logger.hpp"
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#include "utils/ILogger.hpp"
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namespace Recommendation
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{
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@@ -30,397 +30,384 @@
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#include "services/database/TrackFeatures.hpp"
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#include "services/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/ILogger.hpp"
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#include "utils/Random.hpp"
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namespace Recommendation {
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using namespace Database;
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std::unique_ptr<IEngine> createFeaturesEngine(Db& db)
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namespace Recommendation
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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<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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Session& session {_db.getTLSSession()};
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RangeResults<TrackFeaturesId> trackFeaturesIds;
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{
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auto transaction {session.createReadTransaction()};
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LMS_LOG(RECOMMENDATION, DEBUG) << "Getting Track features...";
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trackFeaturesIds = TrackFeatures::find(session);
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LMS_LOG(RECOMMENDATION, DEBUG) << "Getting Track features DONE (found " << trackFeaturesIds.results.size() << " track features)";
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}
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std::vector<SOM::InputVector> samples;
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std::vector<TrackId> samplesTrackIds;
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samples.reserve(trackFeaturesIds.results.size());
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samplesTrackIds.reserve(trackFeaturesIds.results.size());
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LMS_LOG(RECOMMENDATION, DEBUG) << "Extracting features...";
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// TODO handle errors using exceptions
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for (const TrackFeaturesId trackFeaturesId : trackFeaturesIds.results)
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{
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if (_loadCancelled)
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return;
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auto transaction {session.createReadTransaction()};
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TrackFeatures::pointer trackFeatures {TrackFeatures::find(session, trackFeaturesId)};
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if (!trackFeatures)
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continue;
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FeatureValuesMap featureValuesMap {trackFeatures->getFeatureValuesMap(featureNames)};
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if (featureValuesMap.empty())
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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(trackFeatures->getTrack()->getId());
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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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using namespace Database;
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std::unique_ptr<IEngine> createFeaturesEngine(Db& db)
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{
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return std::make_unique<FeaturesEngine>(db);
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}
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namespace
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{
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std::optional<SOM::InputVector> 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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SOM::InputVector 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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}
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const FeatureSettingsMap& 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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void 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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Session & session{ _db.getTLSSession() };
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RangeResults<TrackFeaturesId> trackFeaturesIds;
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{
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auto transaction{ session.createReadTransaction() };
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LMS_LOG(RECOMMENDATION, DEBUG, "Getting Track features...");
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trackFeaturesIds = TrackFeatures::find(session);
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LMS_LOG(RECOMMENDATION, DEBUG, "Getting Track features DONE (found " << trackFeaturesIds.results.size() << " track features)");
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}
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std::vector<SOM::InputVector> samples;
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std::vector<TrackId> samplesTrackIds;
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samples.reserve(trackFeaturesIds.results.size());
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samplesTrackIds.reserve(trackFeaturesIds.results.size());
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LMS_LOG(RECOMMENDATION, DEBUG, "Extracting features...");
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// TODO handle errors using exceptions
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for (const TrackFeaturesId trackFeaturesId : trackFeaturesIds.results)
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{
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if (_loadCancelled)
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return;
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auto transaction{ session.createReadTransaction() };
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TrackFeatures::pointer trackFeatures{ TrackFeatures::find(session, trackFeaturesId) };
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if (!trackFeatures)
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continue;
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FeatureValuesMap featureValuesMap{ trackFeatures->getFeatureValuesMap(featureNames) };
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if (featureValuesMap.empty())
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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(trackFeatures->getTrack()->getId());
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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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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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LMS_LOG(RECOMMENDATION, DEBUG, "Normalizing data...");
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SOM::DataNormalizer dataNormalizer{ nbDimensions };
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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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dataNormalizer.computeNormalizationFactors(samples);
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for (auto& sample : samples)
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dataNormalizer.normalizeData(sample);
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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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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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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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SOM::Network network{ size, size, nbDimensions };
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const SOM::Position position {network.getClosestRefVectorPosition(samples[i])};
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SOM::InputVector weights{ getInputVectorWeights(trainSettings.featureSettingsMap, nbDimensions) };
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network.setDataWeights(weights);
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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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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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load(std::move(network), std::move(trackPositions));
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}
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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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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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TrackContainer
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FeaturesEngine::findSimilarTracksFromTrackList(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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Session& session {_db.getTLSSession()};
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auto transaction {session.createReadTransaction()};
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const TrackList::pointer trackList {TrackList::find(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 findSimilarTracks(trackIds, maxCount);
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}
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TrackContainer
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FeaturesEngine::findSimilarTracks(const std::vector<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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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.createReadTransaction()};
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similarTrackIds.erase(std::remove_if(std::begin(similarTrackIds), std::end(similarTrackIds),
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[&](TrackId trackId)
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{
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return !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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ReleaseContainer
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FeaturesEngine::getSimilarReleases(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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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.createReadTransaction()};
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similarReleaseIds.erase(std::remove_if(std::begin(similarReleaseIds), std::end(similarReleaseIds),
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[&](ReleaseId releaseId)
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{
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return !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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ArtistContainer
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FeaturesEngine::getSimilarArtists(ArtistId artistId, EnumSet<TrackArtistLinkType> linkTypes, std::size_t maxCount) const
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{
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auto getSimilarArtistIdsForLinkType {[&] (TrackArtistLinkType linkType)
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||||
{
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ArtistContainer 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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||||
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return getSimilarObjects({artistId}, itArtists->second, _artistPositions, maxCount);
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}};
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std::unordered_set<ArtistId> similarArtistIds;
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||||
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||||
for (TrackArtistLinkType linkType : linkTypes)
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||||
{
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const auto similarArtistIdsForLinkType {getSimilarArtistIdsForLinkType(linkType)};
|
||||
similarArtistIds.insert(std::begin(similarArtistIdsForLinkType), std::end(similarArtistIdsForLinkType));
|
||||
}
|
||||
|
||||
ArtistContainer res(std::cbegin(similarArtistIds), std::cend(similarArtistIds));
|
||||
|
||||
Session& session {_db.getTLSSession()};
|
||||
{
|
||||
// Report only existing ids
|
||||
auto transaction {session.createReadTransaction()};
|
||||
|
||||
res.erase(std::remove_if(std::begin(res), std::end(res),
|
||||
[&](ArtistId artistId)
|
||||
{
|
||||
return !Artist::exists(session, artistId);
|
||||
}), std::end(res));
|
||||
}
|
||||
|
||||
while (res.size() > maxCount)
|
||||
res.erase(Random::pickRandom(res));
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
FeaturesEngineCache
|
||||
FeaturesEngine::toCache() const
|
||||
{
|
||||
return FeaturesEngineCache {*_network, _trackPositions};
|
||||
}
|
||||
|
||||
void
|
||||
FeaturesEngine::load(bool forceReload, const ProgressCallback& progressCallback)
|
||||
{
|
||||
if (forceReload)
|
||||
{
|
||||
FeaturesEngineCache::invalidate();
|
||||
}
|
||||
else if (std::optional<FeaturesEngineCache> cache {FeaturesEngineCache::read()})
|
||||
{
|
||||
loadFromCache(std::move(*cache));
|
||||
return;
|
||||
}
|
||||
|
||||
TrainSettings trainSettings;
|
||||
trainSettings.featureSettingsMap = getDefaultTrainFeatureSettings();
|
||||
|
||||
loadFromTraining(trainSettings, progressCallback);
|
||||
if (!_loadCancelled && _network)
|
||||
toCache().write();
|
||||
}
|
||||
|
||||
void
|
||||
FeaturesEngine::requestCancelLoad()
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, DEBUG) << "Requesting init cancellation";
|
||||
_loadCancelled = true;
|
||||
}
|
||||
|
||||
void
|
||||
FeaturesEngine::load(const SOM::Network& network, const TrackPositions& trackPositions)
|
||||
{
|
||||
using namespace Database;
|
||||
|
||||
_networkRefVectorsDistanceMedian = network.computeRefVectorsDistanceMedian();
|
||||
LMS_LOG(RECOMMENDATION, DEBUG) << "Median distance betweend ref vectors = " << _networkRefVectorsDistanceMedian;
|
||||
|
||||
const SOM::Coordinate width {network.getWidth()};
|
||||
const SOM::Coordinate height {network.getHeight()};
|
||||
|
||||
_releaseMatrix = ReleaseMatrix {width, height};
|
||||
_trackMatrix = TrackMatrix {width, height};
|
||||
|
||||
LMS_LOG(RECOMMENDATION, DEBUG) << "Constructing maps...";
|
||||
|
||||
Session& session {_db.getTLSSession()};
|
||||
|
||||
for (const auto& [trackId, positions] : trackPositions)
|
||||
{
|
||||
if (_loadCancelled)
|
||||
return;
|
||||
|
||||
auto transaction {session.createReadTransaction()};
|
||||
|
||||
const Track::pointer track {Track::find(session, trackId)};
|
||||
if (!track)
|
||||
continue;
|
||||
|
||||
for (const SOM::Position& position : positions)
|
||||
{
|
||||
Utils::push_back_if_not_present(_trackPositions[trackId], position);
|
||||
Utils::push_back_if_not_present(_trackMatrix[position], trackId);
|
||||
|
||||
if (Release::pointer release {track->getRelease()})
|
||||
{
|
||||
const ReleaseId releaseId {release->getId()};
|
||||
Utils::push_back_if_not_present(_releasePositions[releaseId], position);
|
||||
Utils::push_back_if_not_present(_releaseMatrix[position], releaseId);
|
||||
}
|
||||
for (const TrackArtistLink::pointer& artistLink : track->getArtistLinks())
|
||||
{
|
||||
const ArtistId artistId {artistLink->getArtist()->getId()};
|
||||
|
||||
Utils::push_back_if_not_present(_artistPositions[artistId], position);
|
||||
auto itArtists {_artistMatrix.find(artistLink->getType())};
|
||||
if (itArtists == std::cend(_artistMatrix))
|
||||
{
|
||||
[[maybe_unused]] auto [it, inserted] = _artistMatrix.try_emplace(artistLink->getType(), ArtistMatrix {width, height});
|
||||
assert(inserted);
|
||||
itArtists = it;
|
||||
}
|
||||
Utils::push_back_if_not_present(itArtists->second[position], artistId);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_network = std::make_unique<SOM::Network>(network);
|
||||
|
||||
LMS_LOG(RECOMMENDATION, INFO) << "Classifier successfully loaded!";
|
||||
}
|
||||
const SOM::Position position{ network.getClosestRefVectorPosition(samples[i]) };
|
||||
|
||||
trackPositions[samplesTrackIds[i]].push_back(position);
|
||||
}
|
||||
|
||||
LMS_LOG(RECOMMENDATION, DEBUG, "Classifying tracks DONE");
|
||||
|
||||
load(std::move(network), std::move(trackPositions));
|
||||
}
|
||||
|
||||
void FeaturesEngine::loadFromCache(FeaturesEngineCache&& cache)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, INFO, "Constructing features classifier from cache...");
|
||||
|
||||
load(std::move(cache._network), cache._trackPositions);
|
||||
}
|
||||
|
||||
TrackContainer FeaturesEngine::findSimilarTracksFromTrackList(TrackListId trackListId, std::size_t maxCount) const
|
||||
{
|
||||
const TrackContainer trackIds{ [&]
|
||||
{
|
||||
TrackContainer res;
|
||||
|
||||
Session& session {_db.getTLSSession()};
|
||||
|
||||
auto transaction {session.createReadTransaction()};
|
||||
|
||||
const TrackList::pointer trackList {TrackList::find(session, trackListId)};
|
||||
if (trackList)
|
||||
res = trackList->getTrackIds();
|
||||
|
||||
return res;
|
||||
}() };
|
||||
|
||||
return findSimilarTracks(trackIds, maxCount);
|
||||
}
|
||||
|
||||
TrackContainer FeaturesEngine::findSimilarTracks(const std::vector<TrackId>& tracksIds, std::size_t maxCount) const
|
||||
{
|
||||
auto similarTrackIds{ getSimilarObjects(tracksIds, _trackMatrix, _trackPositions, maxCount) };
|
||||
|
||||
Session& session{ _db.getTLSSession() };
|
||||
|
||||
{
|
||||
// Report only existing ids, as tracks may have been removed a long time ago (refreshing the SOM takes some time)
|
||||
auto transaction{ session.createReadTransaction() };
|
||||
|
||||
similarTrackIds.erase(std::remove_if(std::begin(similarTrackIds), std::end(similarTrackIds),
|
||||
[&](TrackId trackId)
|
||||
{
|
||||
return !Track::exists(session, trackId);
|
||||
}), std::end(similarTrackIds));
|
||||
}
|
||||
|
||||
return similarTrackIds;
|
||||
}
|
||||
|
||||
ReleaseContainer FeaturesEngine::getSimilarReleases(ReleaseId releaseId, std::size_t maxCount) const
|
||||
{
|
||||
auto similarReleaseIds{ getSimilarObjects({releaseId}, _releaseMatrix, _releasePositions, maxCount) };
|
||||
|
||||
Session& session{ _db.getTLSSession() };
|
||||
|
||||
if (!similarReleaseIds.empty())
|
||||
{
|
||||
// Report only existing ids
|
||||
auto transaction{ session.createReadTransaction() };
|
||||
|
||||
similarReleaseIds.erase(std::remove_if(std::begin(similarReleaseIds), std::end(similarReleaseIds),
|
||||
[&](ReleaseId releaseId)
|
||||
{
|
||||
return !Release::exists(session, releaseId);
|
||||
}), std::end(similarReleaseIds));
|
||||
}
|
||||
|
||||
return similarReleaseIds;
|
||||
}
|
||||
|
||||
ArtistContainer FeaturesEngine::getSimilarArtists(ArtistId artistId, EnumSet<TrackArtistLinkType> linkTypes, std::size_t maxCount) const
|
||||
{
|
||||
auto getSimilarArtistIdsForLinkType{ [&](TrackArtistLinkType linkType)
|
||||
{
|
||||
ArtistContainer similarArtistIds;
|
||||
|
||||
const auto itArtists {_artistMatrix.find(linkType)};
|
||||
if (itArtists == std::cend(_artistMatrix))
|
||||
{
|
||||
return similarArtistIds;
|
||||
}
|
||||
|
||||
return getSimilarObjects({artistId}, itArtists->second, _artistPositions, maxCount);
|
||||
} };
|
||||
|
||||
std::unordered_set<ArtistId> similarArtistIds;
|
||||
|
||||
for (TrackArtistLinkType linkType : linkTypes)
|
||||
{
|
||||
const auto similarArtistIdsForLinkType{ getSimilarArtistIdsForLinkType(linkType) };
|
||||
similarArtistIds.insert(std::begin(similarArtistIdsForLinkType), std::end(similarArtistIdsForLinkType));
|
||||
}
|
||||
|
||||
ArtistContainer res(std::cbegin(similarArtistIds), std::cend(similarArtistIds));
|
||||
|
||||
Session& session{ _db.getTLSSession() };
|
||||
{
|
||||
// Report only existing ids
|
||||
auto transaction{ session.createReadTransaction() };
|
||||
|
||||
res.erase(std::remove_if(std::begin(res), std::end(res),
|
||||
[&](ArtistId artistId)
|
||||
{
|
||||
return !Artist::exists(session, artistId);
|
||||
}), std::end(res));
|
||||
}
|
||||
|
||||
while (res.size() > maxCount)
|
||||
res.erase(Random::pickRandom(res));
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
FeaturesEngineCache FeaturesEngine::toCache() const
|
||||
{
|
||||
return FeaturesEngineCache{ *_network, _trackPositions };
|
||||
}
|
||||
|
||||
void FeaturesEngine::load(bool forceReload, const ProgressCallback& progressCallback)
|
||||
{
|
||||
if (forceReload)
|
||||
{
|
||||
FeaturesEngineCache::invalidate();
|
||||
}
|
||||
else if (std::optional<FeaturesEngineCache> cache{ FeaturesEngineCache::read() })
|
||||
{
|
||||
loadFromCache(std::move(*cache));
|
||||
return;
|
||||
}
|
||||
|
||||
TrainSettings trainSettings;
|
||||
trainSettings.featureSettingsMap = getDefaultTrainFeatureSettings();
|
||||
|
||||
loadFromTraining(trainSettings, progressCallback);
|
||||
if (!_loadCancelled && _network)
|
||||
toCache().write();
|
||||
}
|
||||
|
||||
void FeaturesEngine::requestCancelLoad()
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, DEBUG, "Requesting init cancellation");
|
||||
_loadCancelled = true;
|
||||
}
|
||||
|
||||
void FeaturesEngine::load(const SOM::Network& network, const TrackPositions& trackPositions)
|
||||
{
|
||||
using namespace Database;
|
||||
|
||||
_networkRefVectorsDistanceMedian = network.computeRefVectorsDistanceMedian();
|
||||
LMS_LOG(RECOMMENDATION, DEBUG, "Median distance betweend ref vectors = " << _networkRefVectorsDistanceMedian);
|
||||
|
||||
const SOM::Coordinate width{ network.getWidth() };
|
||||
const SOM::Coordinate height{ network.getHeight() };
|
||||
|
||||
_releaseMatrix = ReleaseMatrix{ width, height };
|
||||
_trackMatrix = TrackMatrix{ width, height };
|
||||
|
||||
LMS_LOG(RECOMMENDATION, DEBUG, "Constructing maps...");
|
||||
|
||||
Session & session{ _db.getTLSSession() };
|
||||
|
||||
for (const auto& [trackId, positions] : trackPositions)
|
||||
{
|
||||
if (_loadCancelled)
|
||||
return;
|
||||
|
||||
auto transaction{ session.createReadTransaction() };
|
||||
|
||||
const Track::pointer track{ Track::find(session, trackId) };
|
||||
if (!track)
|
||||
continue;
|
||||
|
||||
for (const SOM::Position& position : positions)
|
||||
{
|
||||
Utils::push_back_if_not_present(_trackPositions[trackId], position);
|
||||
Utils::push_back_if_not_present(_trackMatrix[position], trackId);
|
||||
|
||||
if (Release::pointer release{ track->getRelease() })
|
||||
{
|
||||
const ReleaseId releaseId{ release->getId() };
|
||||
Utils::push_back_if_not_present(_releasePositions[releaseId], position);
|
||||
Utils::push_back_if_not_present(_releaseMatrix[position], releaseId);
|
||||
}
|
||||
for (const TrackArtistLink::pointer& artistLink : track->getArtistLinks())
|
||||
{
|
||||
const ArtistId artistId{ artistLink->getArtist()->getId() };
|
||||
|
||||
Utils::push_back_if_not_present(_artistPositions[artistId], position);
|
||||
auto itArtists{ _artistMatrix.find(artistLink->getType()) };
|
||||
if (itArtists == std::cend(_artistMatrix))
|
||||
{
|
||||
[[maybe_unused]] auto [it, inserted] = _artistMatrix.try_emplace(artistLink->getType(), ArtistMatrix{ width, height });
|
||||
assert(inserted);
|
||||
itArtists = it;
|
||||
}
|
||||
Utils::push_back_if_not_present(itArtists->second[position], artistId);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_network = std::make_unique<SOM::Network>(network);
|
||||
|
||||
LMS_LOG(RECOMMENDATION, INFO, "Classifier successfully loaded!");
|
||||
}
|
||||
|
||||
} // ns Recommendation
|
||||
|
||||
@@ -23,233 +23,226 @@
|
||||
#include <boost/property_tree/xml_parser.hpp>
|
||||
|
||||
#include "utils/IConfig.hpp"
|
||||
#include "utils/Logger.hpp"
|
||||
#include "utils/ILogger.hpp"
|
||||
#include "utils/Service.hpp"
|
||||
|
||||
namespace Recommendation {
|
||||
|
||||
|
||||
static
|
||||
std::filesystem::path getCacheDirectory()
|
||||
namespace Recommendation
|
||||
{
|
||||
return Service<IConfig>::get()->getPath("working-dir") / "cache" / "features";
|
||||
}
|
||||
namespace
|
||||
{
|
||||
std::filesystem::path getCacheDirectory()
|
||||
{
|
||||
return Service<IConfig>::get()->getPath("working-dir") / "cache" / "features";
|
||||
}
|
||||
|
||||
static std::filesystem::path getCacheNetworkFilePath()
|
||||
{
|
||||
return getCacheDirectory() / "network";
|
||||
}
|
||||
std::filesystem::path getCacheNetworkFilePath()
|
||||
{
|
||||
return getCacheDirectory() / "network";
|
||||
}
|
||||
|
||||
static std::filesystem::path getCacheTrackPositionsFilePath()
|
||||
{
|
||||
return getCacheDirectory() / "track_positions";
|
||||
}
|
||||
std::filesystem::path getCacheTrackPositionsFilePath()
|
||||
{
|
||||
return getCacheDirectory() / "track_positions";
|
||||
}
|
||||
|
||||
static
|
||||
bool
|
||||
networkToCacheFile(const SOM::Network& network, std::filesystem::path path)
|
||||
{
|
||||
try
|
||||
{
|
||||
boost::property_tree::ptree root;
|
||||
bool networkToCacheFile(const SOM::Network& network, std::filesystem::path path)
|
||||
{
|
||||
try
|
||||
{
|
||||
boost::property_tree::ptree root;
|
||||
|
||||
root.put("width", network.getWidth());
|
||||
root.put("height", network.getHeight());
|
||||
root.put("dim_count", network.getInputDimCount());
|
||||
root.put("width", network.getWidth());
|
||||
root.put("height", network.getHeight());
|
||||
root.put("dim_count", network.getInputDimCount());
|
||||
|
||||
for (SOM::InputVector::value_type weight : network.getDataWeights())
|
||||
root.add("weights.weight", weight);
|
||||
for (SOM::InputVector::value_type weight : network.getDataWeights())
|
||||
root.add("weights.weight", weight);
|
||||
|
||||
for (SOM::Coordinate x = 0; x < network.getWidth(); ++x)
|
||||
{
|
||||
for (SOM::Coordinate y = 0; y < network.getWidth(); ++y)
|
||||
{
|
||||
const auto& refVector = network.getRefVector({x, y});
|
||||
for (SOM::Coordinate x = 0; x < network.getWidth(); ++x)
|
||||
{
|
||||
for (SOM::Coordinate y = 0; y < network.getWidth(); ++y)
|
||||
{
|
||||
const auto& refVector = network.getRefVector({ x, y });
|
||||
|
||||
boost::property_tree::ptree node;
|
||||
for (auto value : refVector)
|
||||
node.add("values.value", value);
|
||||
boost::property_tree::ptree node;
|
||||
for (auto value : refVector)
|
||||
node.add("values.value", value);
|
||||
|
||||
node.put("coord_x", x);
|
||||
node.put("coord_y", y);
|
||||
node.put("coord_x", x);
|
||||
node.put("coord_y", y);
|
||||
|
||||
root.add_child("ref_vectors.ref_vector", node);
|
||||
}
|
||||
}
|
||||
root.add_child("ref_vectors.ref_vector", node);
|
||||
}
|
||||
}
|
||||
|
||||
boost::property_tree::write_xml(path.string(), root);
|
||||
boost::property_tree::write_xml(path.string(), root);
|
||||
|
||||
LMS_LOG(RECOMMENDATION, DEBUG) << "Created network cache";
|
||||
return true;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR) << "Cannot create network cache: " << error.what();
|
||||
return false;
|
||||
}
|
||||
}
|
||||
LMS_LOG(RECOMMENDATION, DEBUG, "Created network cache");
|
||||
return true;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR, "Cannot create network cache: " << error.what());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::optional<SOM::Network>
|
||||
FeaturesEngineCache::createNetworkFromCacheFile(const std::filesystem::path& path)
|
||||
{
|
||||
if (!std::filesystem::exists(path))
|
||||
return std::nullopt;
|
||||
std::optional<SOM::Network> FeaturesEngineCache::createNetworkFromCacheFile(const std::filesystem::path& path)
|
||||
{
|
||||
if (!std::filesystem::exists(path))
|
||||
return std::nullopt;
|
||||
|
||||
try
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, INFO) << "Reading network from cache...";
|
||||
try
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, INFO, "Reading network from cache...");
|
||||
|
||||
boost::property_tree::ptree root;
|
||||
boost::property_tree::ptree root;
|
||||
|
||||
boost::property_tree::read_xml(path.string(), root);
|
||||
boost::property_tree::read_xml(path.string(), root);
|
||||
|
||||
SOM::Coordinate width {root.get<SOM::Coordinate>("width")};
|
||||
SOM::Coordinate height {root.get<SOM::Coordinate>("height")};
|
||||
std::size_t dimCount {root.get<std::size_t>("dim_count")};
|
||||
SOM::Coordinate width{ root.get<SOM::Coordinate>("width") };
|
||||
SOM::Coordinate height{ root.get<SOM::Coordinate>("height") };
|
||||
std::size_t dimCount{ root.get<std::size_t>("dim_count") };
|
||||
|
||||
SOM::Network res {width, height, dimCount};
|
||||
SOM::Network res{ width, height, dimCount };
|
||||
|
||||
{
|
||||
SOM::InputVector weights {dimCount};
|
||||
std::size_t i {};
|
||||
for (const auto& val : root.get_child("weights"))
|
||||
weights[i++] = val.second.get_value<double>();
|
||||
{
|
||||
SOM::InputVector weights{ dimCount };
|
||||
std::size_t i{};
|
||||
for (const auto& val : root.get_child("weights"))
|
||||
weights[i++] = val.second.get_value<double>();
|
||||
|
||||
res.setDataWeights(weights);
|
||||
}
|
||||
res.setDataWeights(weights);
|
||||
}
|
||||
|
||||
for (const auto& node : root.get_child("ref_vectors"))
|
||||
{
|
||||
SOM::Coordinate x {node.second.get<SOM::Coordinate>("coord_x")};
|
||||
SOM::Coordinate y {node.second.get<SOM::Coordinate>("coord_y")};
|
||||
for (const auto& node : root.get_child("ref_vectors"))
|
||||
{
|
||||
SOM::Coordinate x{ node.second.get<SOM::Coordinate>("coord_x") };
|
||||
SOM::Coordinate y{ node.second.get<SOM::Coordinate>("coord_y") };
|
||||
|
||||
SOM::InputVector refVector {dimCount};
|
||||
std::size_t i {};
|
||||
for (const auto& val : node.second.get_child("values"))
|
||||
refVector[i++] = val.second.get_value<SOM::InputVector::value_type>();
|
||||
SOM::InputVector refVector{ dimCount };
|
||||
std::size_t i{};
|
||||
for (const auto& val : node.second.get_child("values"))
|
||||
refVector[i++] = val.second.get_value<SOM::InputVector::value_type>();
|
||||
|
||||
res.setRefVector({x, y}, refVector);
|
||||
}
|
||||
res.setRefVector({ x, y }, refVector);
|
||||
}
|
||||
|
||||
LMS_LOG(RECOMMENDATION, INFO) << "Successfully read network from cache";
|
||||
LMS_LOG(RECOMMENDATION, INFO, "Successfully read network from cache");
|
||||
|
||||
return res;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR) << "Cannot read network cache: " << error.what();
|
||||
return std::nullopt;
|
||||
}
|
||||
}
|
||||
return res;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR, "Cannot read network cache: " << error.what());
|
||||
return std::nullopt;
|
||||
}
|
||||
}
|
||||
|
||||
bool
|
||||
FeaturesEngineCache::objectPositionToCacheFile(const TrackPositions& trackPositions, const std::filesystem::path& path)
|
||||
{
|
||||
try
|
||||
{
|
||||
boost::property_tree::ptree root;
|
||||
bool FeaturesEngineCache::objectPositionToCacheFile(const TrackPositions& trackPositions, const std::filesystem::path& path)
|
||||
{
|
||||
try
|
||||
{
|
||||
boost::property_tree::ptree root;
|
||||
|
||||
for (const auto& [id, positions] : trackPositions)
|
||||
{
|
||||
boost::property_tree::ptree node;
|
||||
for (const auto& [id, positions] : trackPositions)
|
||||
{
|
||||
boost::property_tree::ptree node;
|
||||
|
||||
node.put("id", id.getValue());
|
||||
node.put("id", id.getValue());
|
||||
|
||||
for (const SOM::Position& position : positions)
|
||||
{
|
||||
boost::property_tree::ptree positionNode;
|
||||
positionNode.put("x", position.x);
|
||||
positionNode.put("y", position.y);
|
||||
for (const SOM::Position& position : positions)
|
||||
{
|
||||
boost::property_tree::ptree positionNode;
|
||||
positionNode.put("x", position.x);
|
||||
positionNode.put("y", position.y);
|
||||
|
||||
node.add_child("position.position", positionNode);
|
||||
}
|
||||
node.add_child("position.position", positionNode);
|
||||
}
|
||||
|
||||
root.add_child("objects.object", node);
|
||||
}
|
||||
root.add_child("objects.object", node);
|
||||
}
|
||||
|
||||
boost::property_tree::write_xml(path.string(), root);
|
||||
return true;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR) << "Cannot cache object position: " << error.what();
|
||||
return false;
|
||||
}
|
||||
}
|
||||
boost::property_tree::write_xml(path.string(), root);
|
||||
return true;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR, "Cannot cache object position: " << error.what());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
std::optional<FeaturesEngineCache::TrackPositions>
|
||||
FeaturesEngineCache::createObjectPositionsFromCacheFile(const std::filesystem::path& path)
|
||||
{
|
||||
try
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, INFO) << "Reading object position from cache...";
|
||||
std::optional<FeaturesEngineCache::TrackPositions> FeaturesEngineCache::createObjectPositionsFromCacheFile(const std::filesystem::path& path)
|
||||
{
|
||||
try
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, INFO, "Reading object position from cache...");
|
||||
|
||||
boost::property_tree::ptree root;
|
||||
boost::property_tree::ptree root;
|
||||
|
||||
boost::property_tree::read_xml(path.string(), root);
|
||||
boost::property_tree::read_xml(path.string(), root);
|
||||
|
||||
TrackPositions res;
|
||||
TrackPositions res;
|
||||
|
||||
for (const auto& object : root.get_child("objects"))
|
||||
{
|
||||
const Database::TrackId id {object.second.get<Database::IdType::ValueType>("id")};
|
||||
for (const auto& position : object.second.get_child("position"))
|
||||
{
|
||||
auto x = position.second.get<SOM::Coordinate>("x");
|
||||
auto y = position.second.get<SOM::Coordinate>("y");
|
||||
for (const auto& object : root.get_child("objects"))
|
||||
{
|
||||
const Database::TrackId id{ object.second.get<Database::IdType::ValueType>("id") };
|
||||
for (const auto& position : object.second.get_child("position"))
|
||||
{
|
||||
auto x = position.second.get<SOM::Coordinate>("x");
|
||||
auto y = position.second.get<SOM::Coordinate>("y");
|
||||
|
||||
res[id].push_back({x, y});
|
||||
}
|
||||
}
|
||||
res[id].push_back({ x, y });
|
||||
}
|
||||
}
|
||||
|
||||
LMS_LOG(RECOMMENDATION, INFO) << "Successfully read object position from cache";
|
||||
LMS_LOG(RECOMMENDATION, INFO, "Successfully read object position from cache");
|
||||
|
||||
return res;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR) << "Cannot create object position from cache file: " << error.what();
|
||||
return std::nullopt;
|
||||
}
|
||||
}
|
||||
return res;
|
||||
}
|
||||
catch (boost::property_tree::ptree_error& error)
|
||||
{
|
||||
LMS_LOG(RECOMMENDATION, ERROR, "Cannot create object position from cache file: " << error.what());
|
||||
return std::nullopt;
|
||||
}
|
||||
}
|
||||
|
||||
void
|
||||
FeaturesEngineCache::invalidate()
|
||||
{
|
||||
std::filesystem::remove(getCacheNetworkFilePath());
|
||||
std::filesystem::remove(getCacheTrackPositionsFilePath());
|
||||
}
|
||||
void FeaturesEngineCache::invalidate()
|
||||
{
|
||||
std::filesystem::remove(getCacheNetworkFilePath());
|
||||
std::filesystem::remove(getCacheTrackPositionsFilePath());
|
||||
}
|
||||
|
||||
std::optional<FeaturesEngineCache>
|
||||
FeaturesEngineCache::read()
|
||||
{
|
||||
auto network{createNetworkFromCacheFile(getCacheNetworkFilePath())};
|
||||
if (!network)
|
||||
return std::nullopt;
|
||||
std::optional<FeaturesEngineCache> FeaturesEngineCache::read()
|
||||
{
|
||||
auto network{ createNetworkFromCacheFile(getCacheNetworkFilePath()) };
|
||||
if (!network)
|
||||
return std::nullopt;
|
||||
|
||||
auto trackPositions{createObjectPositionsFromCacheFile(getCacheTrackPositionsFilePath())};
|
||||
if (!trackPositions)
|
||||
return std::nullopt;
|
||||
auto trackPositions{ createObjectPositionsFromCacheFile(getCacheTrackPositionsFilePath()) };
|
||||
if (!trackPositions)
|
||||
return std::nullopt;
|
||||
|
||||
return FeaturesEngineCache {std::move(*network), std::move(*trackPositions)};
|
||||
}
|
||||
return FeaturesEngineCache{ std::move(*network), std::move(*trackPositions) };
|
||||
}
|
||||
|
||||
void
|
||||
FeaturesEngineCache::write() const
|
||||
{
|
||||
std::filesystem::create_directories(Service<IConfig>::get()->getPath("working-dir") / "cache" / "features");
|
||||
void FeaturesEngineCache::write() const
|
||||
{
|
||||
std::filesystem::create_directories(Service<IConfig>::get()->getPath("working-dir") / "cache" / "features");
|
||||
|
||||
if (!networkToCacheFile(_network, getCacheNetworkFilePath())
|
||||
|| !objectPositionToCacheFile(_trackPositions, getCacheTrackPositionsFilePath()))
|
||||
{
|
||||
invalidate();
|
||||
}
|
||||
}
|
||||
if (!networkToCacheFile(_network, getCacheNetworkFilePath())
|
||||
|| !objectPositionToCacheFile(_trackPositions, getCacheTrackPositionsFilePath()))
|
||||
{
|
||||
invalidate();
|
||||
}
|
||||
}
|
||||
|
||||
FeaturesEngineCache::FeaturesEngineCache(SOM::Network network, TrackPositions trackPositions)
|
||||
: _network {std::move(network)},
|
||||
_trackPositions {std::move(trackPositions)}
|
||||
{
|
||||
}
|
||||
FeaturesEngineCache::FeaturesEngineCache(SOM::Network network, TrackPositions trackPositions)
|
||||
: _network{ std::move(network) },
|
||||
_trackPositions{ std::move(trackPositions) }
|
||||
{
|
||||
}
|
||||
|
||||
} // namespace Recommendation
|
||||
|
||||
@@ -25,7 +25,7 @@
|
||||
#include "services/database/Release.hpp"
|
||||
#include "services/database/Session.hpp"
|
||||
#include "services/database/Track.hpp"
|
||||
#include "utils/Logger.hpp"
|
||||
#include "utils/ILogger.hpp"
|
||||
|
||||
namespace Recommendation::PlaylistGeneratorConstraint
|
||||
{
|
||||
|
||||
@@ -23,7 +23,7 @@
|
||||
#include "services/database/Release.hpp"
|
||||
#include "services/database/Session.hpp"
|
||||
#include "services/database/Track.hpp"
|
||||
#include "utils/Logger.hpp"
|
||||
#include "utils/ILogger.hpp"
|
||||
|
||||
namespace Recommendation::PlaylistGeneratorConstraint
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user