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Refactored namespaces

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This commit is contained in:
emeric
2024-03-12 08:32:08 +01:00
parent 487960b413
commit 4b7c4295ec
501 changed files with 12605 additions and 12631 deletions
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src/libs/services/recommendation/impl/features/FeaturesEngine.cpp
+31 -31
View File
@@ -30,12 +30,12 @@
#include "database/TrackFeatures.hpp"
#include "database/TrackList.hpp"
#include "som/DataNormalizer.hpp"
#include "utils/ILogger.hpp"
#include "utils/Random.hpp"
#include "core/ILogger.hpp"
#include "core/Random.hpp"
namespace Recommendation
namespace lms::recommendation
{
using namespace Database;
using namespace db;
std::unique_ptr<IEngine> createFeaturesEngine(Db& db)
{
@@ -44,10 +44,10 @@ namespace Recommendation
namespace
{
std::optional<SOM::InputVector> convertFeatureValuesMapToInputVector(const FeatureValuesMap& featureValuesMap, std::size_t nbDimensions)
std::optional<som::InputVector> convertFeatureValuesMapToInputVector(const FeatureValuesMap& featureValuesMap, std::size_t nbDimensions)
{
std::size_t i{};
std::optional<SOM::InputVector> res{ SOM::InputVector {nbDimensions} };
std::optional<som::InputVector> res{ som::InputVector {nbDimensions} };
for (const auto& [featureName, values] : featureValuesMap)
{
if (values.size() != getFeatureDef(featureName).nbDimensions)
@@ -64,9 +64,9 @@ namespace Recommendation
return res;
}
SOM::InputVector getInputVectorWeights(const FeatureSettingsMap& featureSettingsMap, std::size_t nbDimensions)
som::InputVector getInputVectorWeights(const FeatureSettingsMap& featureSettingsMap, std::size_t nbDimensions)
{
SOM::InputVector weights{ nbDimensions };
som::InputVector weights{ nbDimensions };
std::size_t index{};
for (const auto& [featureName, featureSettings] : featureSettingsMap)
{
@@ -120,7 +120,7 @@ namespace Recommendation
LMS_LOG(RECOMMENDATION, DEBUG, "Getting Track features DONE (found " << trackFeaturesIds.results.size() << " track features)");
}
std::vector<SOM::InputVector> samples;
std::vector<som::InputVector> samples;
std::vector<TrackId> samplesTrackIds;
samples.reserve(trackFeaturesIds.results.size());
@@ -143,7 +143,7 @@ namespace Recommendation
if (featureValuesMap.empty())
continue;
std::optional<SOM::InputVector> inputVector{ convertFeatureValuesMapToInputVector(featureValuesMap, nbDimensions) };
std::optional<som::InputVector> inputVector{ convertFeatureValuesMapToInputVector(featureValuesMap, nbDimensions) };
if (!inputVector)
continue;
@@ -159,13 +159,13 @@ namespace Recommendation
}
LMS_LOG(RECOMMENDATION, DEBUG, "Normalizing data...");
SOM::DataNormalizer dataNormalizer{ nbDimensions };
som::DataNormalizer dataNormalizer{ nbDimensions };
dataNormalizer.computeNormalizationFactors(samples);
for (auto& sample : samples)
dataNormalizer.normalizeData(sample);
SOM::Coordinate size{ static_cast<SOM::Coordinate>(std::sqrt(samples.size() / trainSettings.sampleCountPerNeuron)) };
som::Coordinate size{ static_cast<som::Coordinate>(std::sqrt(samples.size() / trainSettings.sampleCountPerNeuron)) };
if (size < 2)
{
LMS_LOG(RECOMMENDATION, WARNING, "Very few tracks (" << samples.size() << ") are being used by the features engine, expect bad behaviors");
@@ -173,12 +173,12 @@ namespace Recommendation
}
LMS_LOG(RECOMMENDATION, INFO, "Found " << samples.size() << " tracks, constructing a " << size << "*" << size << " network");
SOM::Network network{ size, size, nbDimensions };
som::Network network{ size, size, nbDimensions };
SOM::InputVector weights{ getInputVectorWeights(trainSettings.featureSettingsMap, nbDimensions) };
som::InputVector weights{ getInputVectorWeights(trainSettings.featureSettingsMap, nbDimensions) };
network.setDataWeights(weights);
auto somProgressCallback{ [&](const SOM::Network::CurrentIteration& iter)
auto somProgressCallback{ [&](const som::Network::CurrentIteration& iter)
{
LMS_LOG(RECOMMENDATION, DEBUG, "Current pass = " << iter.idIteration << " / " << iter.iterationCount);
progressCallback(Progress {iter.idIteration, iter.iterationCount});
@@ -186,7 +186,7 @@ namespace Recommendation
LMS_LOG(RECOMMENDATION, DEBUG, "Training network...");
network.train(samples, trainSettings.iterationCount,
progressCallback ? somProgressCallback : SOM::Network::ProgressCallback{},
progressCallback ? somProgressCallback : som::Network::ProgressCallback{},
[this] { return _loadCancelled; });
LMS_LOG(RECOMMENDATION, DEBUG, "Training network DONE");
@@ -197,7 +197,7 @@ namespace Recommendation
if (_loadCancelled)
return;
const SOM::Position position{ network.getClosestRefVectorPosition(samples[i]) };
const som::Position position{ network.getClosestRefVectorPosition(samples[i]) };
trackPositions[samplesTrackIds[i]].push_back(position);
}
@@ -275,7 +275,7 @@ namespace Recommendation
return similarReleaseIds;
}
ArtistContainer FeaturesEngine::getSimilarArtists(ArtistId artistId, EnumSet<TrackArtistLinkType> linkTypes, std::size_t maxCount) const
ArtistContainer FeaturesEngine::getSimilarArtists(ArtistId artistId, core::EnumSet<TrackArtistLinkType> linkTypes, std::size_t maxCount) const
{
auto getSimilarArtistIdsForLinkType{ [&](TrackArtistLinkType linkType)
{
@@ -313,7 +313,7 @@ namespace Recommendation
}
while (res.size() > maxCount)
res.erase(Random::pickRandom(res));
res.erase(core::random::pickRandom(res));
return res;
}
@@ -349,15 +349,15 @@ namespace Recommendation
_loadCancelled = true;
}
void FeaturesEngine::load(const SOM::Network& network, const TrackPositions& trackPositions)
void FeaturesEngine::load(const som::Network& network, const TrackPositions& trackPositions)
{
using namespace Database;
using namespace db;
_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() };
const som::Coordinate width{ network.getWidth() };
const som::Coordinate height{ network.getHeight() };
_releaseMatrix = ReleaseMatrix{ width, height };
_trackMatrix = TrackMatrix{ width, height };
@@ -377,22 +377,22 @@ namespace Recommendation
if (!track)
continue;
for (const SOM::Position& position : positions)
for (const som::Position& position : positions)
{
Utils::push_back_if_not_present(_trackPositions[trackId], position);
Utils::push_back_if_not_present(_trackMatrix[position], trackId);
core::utils::push_back_if_not_present(_trackPositions[trackId], position);
core::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);
core::utils::push_back_if_not_present(_releasePositions[releaseId], position);
core::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);
core::utils::push_back_if_not_present(_artistPositions[artistId], position);
auto itArtists{ _artistMatrix.find(artistLink->getType()) };
if (itArtists == std::cend(_artistMatrix))
{
@@ -400,12 +400,12 @@ namespace Recommendation
assert(inserted);
itArtists = it;
}
Utils::push_back_if_not_present(itArtists->second[position], artistId);
core::utils::push_back_if_not_present(itArtists->second[position], artistId);
}
}
}
_network = std::make_unique<SOM::Network>(network);
_network = std::make_unique<som::Network>(network);
LMS_LOG(RECOMMENDATION, INFO, "Classifier successfully loaded!");
}