Restored audio simimarity based classifier
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 "FeaturesClassifier.hpp"
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#include <numeric>
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#include "database/Artist.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/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<IClassifier> createFeaturesClassifier()
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{
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return std::make_unique<FeaturesClassifier>();
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}
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const FeatureSettingsMap&
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FeaturesClassifier::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(FeaturesClassifier::FeaturesFetchFunc func, Database::IdType 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::IdType trackId, const std::unordered_set<FeatureName>& featureNames)
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{
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auto func = [&](Database::IdType 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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bool
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FeaturesClassifier::initFromTraining(Database::Session& session, const TrainSettings& trainSettings)
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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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std::vector<Database::IdType> 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::IdType> 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::IdType trackId : trackIds)
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{
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if (_initCancelled)
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return false;
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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 false;
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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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const SOM::Coordinate size {static_cast<SOM::Coordinate>(std::sqrt(samples.size() / trainSettings.sampleCountPerNeuron))};
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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 progressIndicator{[](const auto& iter)
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{
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LMS_LOG(RECOMMENDATION, DEBUG) << "Current pass = " << 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, progressIndicator);
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LMS_LOG(RECOMMENDATION, DEBUG) << "Training network DONE";
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if (_initCancelled)
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return false;
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LMS_LOG(RECOMMENDATION, DEBUG) << "Classifying tracks...";
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ObjectPositions trackPositions;
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for (std::size_t i {}; i < samples.size(); ++i)
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{
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if (_initCancelled)
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return false;
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const SOM::Position position {network.getClosestRefVectorPosition(samples[i])};
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trackPositions[samplesTrackIds[i]].insert(position);
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}
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LMS_LOG(RECOMMENDATION, DEBUG) << "Classifying tracks DONE";
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return init(session, std::move(network), std::move(trackPositions));
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}
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bool
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FeaturesClassifier::initFromCache(Database::Session& session, const FeaturesClassifierCache& cache)
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{
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LMS_LOG(RECOMMENDATION, INFO) << "Constructing features classifier from cache...";
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return init(session, std::move(cache._network), cache._trackPositions);
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}
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std::vector<Database::IdType>
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FeaturesClassifier::getSimilarTracksFromTrackList(Database::Session& session, Database::IdType trackListId, std::size_t maxCount) const
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{
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const std::unordered_set<Database::IdType> trackIds {[&]() -> std::unordered_set<Database::IdType>
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{
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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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{
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const std::vector<Database::IdType> orderedTrackIds {trackList->getTrackIds()};
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return std::unordered_set<Database::IdType> {std::cbegin(orderedTrackIds), std::cend(orderedTrackIds)};
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}
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return {};
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}()};
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return getSimilarTracks(session, trackIds, maxCount);
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}
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std::vector<Database::IdType>
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FeaturesClassifier::getSimilarTracks(Database::Session&, const std::unordered_set<Database::IdType>& tracksIds, std::size_t maxCount) const
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{
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return getSimilarObjects(tracksIds, _tracksMap, _trackPositions, maxCount);
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}
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std::vector<Database::IdType>
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FeaturesClassifier::getSimilarReleases(Database::Session&, Database::IdType releaseId, std::size_t maxCount) const
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{
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return getSimilarObjects({releaseId}, _releasesMap, _releasePositions, maxCount);
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}
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std::vector<Database::IdType>
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FeaturesClassifier::getSimilarArtists(Database::Session&, Database::IdType artistId, std::size_t maxCount) const
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{
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return getSimilarObjects({artistId}, _artistsMap, _artistPositions, maxCount);
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}
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FeaturesClassifierCache
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FeaturesClassifier::toCache() const
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{
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return FeaturesClassifierCache {*_network, _trackPositions};
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}
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bool
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FeaturesClassifier::init(Database::Session& session)
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{
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std::optional<FeaturesClassifierCache> cache {FeaturesClassifierCache::read()};
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if (cache)
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return initFromCache(session, *cache);
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TrainSettings trainSettings;
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trainSettings.featureSettingsMap = getDefaultTrainFeatureSettings();
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bool res {initFromTraining(session, trainSettings)};
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if (res)
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toCache().write();
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return res;
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}
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void
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FeaturesClassifier::requestCancelInit()
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{
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LMS_LOG(RECOMMENDATION, DEBUG) << "Requesting init cancellation";
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_initCancelled = true;
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}
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bool
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FeaturesClassifier::init(Database::Session& session,
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SOM::Network network,
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const ObjectPositions& tracksPosition)
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{
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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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_artistsMap = MatrixOfObjects {width, height};
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_releasesMap = MatrixOfObjects {width, height};
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_tracksMap = MatrixOfObjects {width, height};
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LMS_LOG(RECOMMENDATION, DEBUG) << "Constructing maps...";
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for (auto itTrackCoord : tracksPosition)
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{
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if (_initCancelled)
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return false;
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auto transaction {session.createSharedTransaction()};
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Database::IdType trackId {itTrackCoord.first};
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const std::unordered_set<SOM::Position>& positionSet {itTrackCoord.second};
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const Database::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 : positionSet)
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{
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_tracksMap[position].insert(trackId);
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_trackPositions[trackId].insert(position);
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if (track->getRelease())
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{
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_releasePositions[track->getRelease().id()].insert(position);
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_releasesMap[position].insert(track->getRelease().id());
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}
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for (const auto& artist : track->getArtists())
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{
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_artistPositions[artist.id()].insert(position);
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_artistsMap[position].insert(artist.id());
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}
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}
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}
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_network = std::make_unique<SOM::Network>(std::move(network));
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LMS_LOG(RECOMMENDATION, INFO) << "Classifier successfully initialized!";
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return true;
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}
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std::unordered_set<SOM::Position>
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FeaturesClassifier::getMatchingRefVectorsPosition(const std::unordered_set<Database::IdType>& ids, const ObjectPositions& objectPositions)
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{
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std::unordered_set<SOM::Position> res;
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if (ids.empty())
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return res;
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for (auto id : ids)
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{
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auto it = objectPositions.find(id);
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if (it == objectPositions.end())
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continue;
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for (const auto& position : it->second)
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res.insert(position);
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}
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return res;
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}
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std::unordered_set<Database::IdType>
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FeaturesClassifier::getObjectsIds(const std::unordered_set<SOM::Position>& positionSet, const MatrixOfObjects& objectsMap)
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{
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std::unordered_set<Database::IdType> res;
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for (const auto& position : positionSet)
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{
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for (auto id : objectsMap.get(position))
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res.insert(id);
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}
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return res;
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}
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std::vector<Database::IdType>
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FeaturesClassifier::getSimilarObjects(const std::unordered_set<Database::IdType>& ids,
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const MatrixOfObjects& objectsMap,
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const ObjectPositions& objectPosition,
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std::size_t maxCount) const
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{
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std::vector<Database::IdType> res;
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std::unordered_set<SOM::Position> searchedRefVectorsPosition {getMatchingRefVectorsPosition(ids, objectPosition)};
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if (searchedRefVectorsPosition.empty())
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return res;
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while (1)
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{
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std::unordered_set<Database::IdType> closestObjectIds {getObjectsIds(searchedRefVectorsPosition, objectsMap)};
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// Remove objects that are already in input or already reported
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for (auto id : ids)
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closestObjectIds.erase(id);
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{
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std::vector<Database::IdType> objectIdsToAdd {std::cbegin(closestObjectIds), std::cend(closestObjectIds)};
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Random::shuffleContainer(objectIdsToAdd );
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std::copy(std::cbegin(objectIdsToAdd), std::cend(objectIdsToAdd), std::back_inserter(res));
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}
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if (res.size() > maxCount)
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res.resize(maxCount);
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if (res.size() == maxCount)
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break;
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// If there is not enough objects, try again with closest neighbour until there is too much distance
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const std::optional<SOM::Position> closestRefVectorPosition {_network->getClosestRefVectorPosition(searchedRefVectorsPosition, _networkRefVectorsDistanceMedian * 0.75)};
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if (!closestRefVectorPosition)
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break;
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searchedRefVectorsPosition.insert(closestRefVectorPosition.value());
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}
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return res;
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}
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} // ns Recommendation
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