/*
* Copyright (C) 2018 Emeric Poupon
*
* This file is part of LMS.
*
* LMS is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* LMS is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LMS. If not, see .
*/
#pragma once
#include
#include
#include
#include
#include
#include
#include "core/Utils.hpp"
#include "som/DataNormalizer.hpp"
#include "som/Network.hpp"
#include "FeaturesDefs.hpp"
#include "FeaturesEngineCache.hpp"
#include "IEngine.hpp"
namespace lms::db
{
class Session;
}
namespace lms::recommendation
{
using FeatureWeight = double;
class FeaturesEngine : public IEngine
{
public:
FeaturesEngine(db::Db& db)
: _db{ db } {}
FeaturesEngine(const FeaturesEngine&) = delete;
FeaturesEngine(FeaturesEngine&&) = delete;
FeaturesEngine& operator=(const FeaturesEngine&) = delete;
FeaturesEngine& operator=(FeaturesEngine&&) = delete;
static const FeatureSettingsMap& getDefaultTrainFeatureSettings();
private:
void load(bool forceReload, const ProgressCallback& progressCallback) override;
void requestCancelLoad() override;
TrackContainer findSimilarTracksFromTrackList(db::TrackListId tracklistId, std::size_t maxCount) const override;
TrackContainer findSimilarTracks(const std::vector& tracksId, std::size_t maxCount) const override;
ReleaseContainer getSimilarReleases(db::ReleaseId releaseId, std::size_t maxCount) const override;
ArtistContainer getSimilarArtists(db::ArtistId artistId, core::EnumSet linkTypes, std::size_t maxCount) const override;
void loadFromCache(FeaturesEngineCache&& cache);
// Use training (may be very slow)
struct TrainSettings
{
std::size_t iterationCount{ 10 };
float sampleCountPerNeuron{ 4 };
FeatureSettingsMap featureSettingsMap;
};
void loadFromTraining(const TrainSettings& trainSettings, const ProgressCallback& progressCallback);
template
using ObjectPositions = std::unordered_map>;
using ArtistPositions = ObjectPositions;
using ReleasePositions = ObjectPositions;
using TrackPositions = ObjectPositions;
template
using ObjectMatrix = som::Matrix>;
using ArtistMatrix = ObjectMatrix;
using ReleaseMatrix = ObjectMatrix;
using TrackMatrix = ObjectMatrix;
void load(const som::Network& network, const TrackPositions& tracksPosition);
FeaturesEngineCache toCache() const;
template
static std::vector getMatchingRefVectorsPosition(const std::vector& ids, const ObjectPositions& objectPositions);
template
static std::vector getObjectsIds(const std::vector& positions, const ObjectMatrix& objectsMatrix);
template
std::vector getSimilarObjects(const std::vector& ids,
const ObjectMatrix& objectMatrix,
const ObjectPositions& objectPositions,
std::size_t maxCount) const;
db::Db& _db;
bool _loadCancelled{};
std::unique_ptr _network;
double _networkRefVectorsDistanceMedian{};
ArtistPositions _artistPositions;
std::unordered_map _artistMatrix;
ReleasePositions _releasePositions;
ReleaseMatrix _releaseMatrix;
TrackPositions _trackPositions;
TrackMatrix _trackMatrix;
};
template
std::vector FeaturesEngine::getMatchingRefVectorsPosition(const std::vector& ids, const ObjectPositions& objectPositions)
{
std::vector res;
if (ids.empty())
return res;
for (const IdType id : ids)
{
auto it = objectPositions.find(id);
if (it == objectPositions.end())
continue;
for (const som::Position& position : it->second)
core::utils::push_back_if_not_present(res, position);
}
return res;
}
template
std::vector FeaturesEngine::getObjectsIds(const std::vector& positions, const ObjectMatrix& objectMatrix)
{
std::vector res;
for (const som::Position& position : positions)
{
for (const IdType id : objectMatrix.get(position))
core::utils::push_back_if_not_present(res, id);
}
return res;
}
template
std::vector FeaturesEngine::getSimilarObjects(const std::vector& ids,
const ObjectMatrix& objectMatrix,
const ObjectPositions& objectPositions,
std::size_t maxCount) const
{
std::vector res;
std::vector searchedRefVectorsPosition{ getMatchingRefVectorsPosition(ids, objectPositions) };
if (searchedRefVectorsPosition.empty())
return res;
while (1)
{
std::vector closestObjectIds{ getObjectsIds(searchedRefVectorsPosition, objectMatrix) };
// Remove objects that are already in input or already reported
closestObjectIds.erase(std::remove_if(std::begin(closestObjectIds), std::end(closestObjectIds),
[&](IdType id) {
return std::find(std::cbegin(ids), std::cend(ids), id) != std::cend(ids);
}),
std::end(closestObjectIds));
for (IdType id : closestObjectIds)
{
if (res.size() == maxCount)
break;
core::utils::push_back_if_not_present(res, id);
}
if (res.size() == maxCount)
break;
// If there is not enough objects, try again with closest neighbour until there is too much distance
const std::optional closestRefVectorPosition{ _network->getClosestRefVectorPosition(searchedRefVectorsPosition, _networkRefVectorsDistanceMedian * 0.75) };
if (!closestRefVectorPosition)
break;
core::utils::push_back_if_not_present(searchedRefVectorsPosition, closestRefVectorPosition.value());
}
return res;
}
} // namespace lms::recommendation