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Breakable training, added cache

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This commit is contained in:
emeric
2019-02-27 13:57:43 +01:00
parent 6fcb693261
commit 11a0ed9f12
23 changed files with 632 additions and 288 deletions
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tools/feature-extractor/LmsFeatureExtractor.cpp
+25 -51
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@@ -5,9 +5,6 @@
#include <chrono>
#include <random>
#include <curl/curl.h>
#include "database/DatabaseHandler.hpp"
#include "database/Track.hpp"
#include "database/Artist.hpp"
@@ -17,7 +14,6 @@
#include "utils/Config.hpp"
#include "similarity/features/som/DataNormalizer.hpp"
#include "similarity/features/som/Network.hpp"
#include "similarity/features/som/AcousticBrainzUtils.hpp"
static
std::ostream& operator<<(std::ostream& os, const Database::Track::pointer& track)
@@ -73,14 +69,11 @@ int main(int argc, char *argv[])
{
try
{
constexpr std::size_t width = 10;
constexpr std::size_t height = 10;
// constexpr std::size_t nbTracks = 80;
constexpr std::size_t nbIterations = 100;
const std::size_t width = 15;
const std::size_t height = 15;
const std::size_t nbIterations = 2;
const std::size_t nbTracks = 5000;
// std::vector<std::string> items = { "lowlevel.barkbands.median", "lowlevel.erbbands.median", "lowlevel.melbands.median"};
// constexpr std::size_t nbDims = 27 + 40 + 40;
//std::vector<std::string> items = { "tonal.hpcp.median"};
const std::map<std::string, std::size_t> featuresSettings =
{
// { "lowlevel.average_loudness", 1 },
@@ -111,41 +104,20 @@ int main(int argc, char *argv[])
std::cout << "Getting all features..." << std::endl;
Wt::Dbo::Transaction transaction(db.getSession());
auto tracks = Database::Track::getAll(db.getSession());
std::vector<Database::Track::pointer> trainingTracks;
for (auto track : tracks)
{
if (track->getMBID().empty())
continue;
if (!track->hasTrackFeatures())
{
std::string features = AcousticBrainz::extractLowLevelFeatures(track->getMBID());
if (features.empty())
continue;
Database::TrackFeatures::create(db.getSession(), track, features);
}
trainingTracks.push_back(track);
}
auto tracks = Database::Track::getAllWithFeatures(db.getSession());
std::cout << "Getting all features DONE" << std::endl;
/* auto now = std::chrono::system_clock::now();
std::mt19937 randGenerator(std::chrono::duration_cast<std::chrono::milliseconds>(now.time_since_epoch()).count());
std::shuffle(trainingTracks.begin(), trainingTracks.end(), randGenerator);
trainingTracks.resize(nbTracks);
std::shuffle(tracks.begin(), tracks.end(), randGenerator);
*/
std::cout << "Getting all features DONE" << std::endl;
tracks.resize(nbTracks);
std::cout << "Reading features..." << std::endl;
std::vector< std::vector<double> > tracksFeatures;
for (auto track : trainingTracks)
for (auto track : tracks)
{
auto features = getTrackFeatures(db.getSession(), track, featuresSettings);
@@ -188,10 +160,11 @@ int main(int argc, char *argv[])
auto medianDistance = network.computeRefVectorsDistanceMedian();
std::cout << "MEDIAN distance = " << medianDistance << std::endl;
#if 0
std::cout << "Classifying tracks..." << std::endl;
SOM::Matrix< std::vector<Database::Track::pointer> > tracksMap(width, height);
for (auto track : trainingTracks)
for (auto track : tracks)
{
auto features = getTrackFeatures(db.getSession(), track, featuresSettings);
@@ -200,17 +173,17 @@ int main(int argc, char *argv[])
normalizer.normalizeData(features);
auto coords = network.getClosestRefVectorCoords(features);
tracksMap[coords].push_back(track);
auto position = network.getClosestRefVectorPosition(features);
tracksMap[position].push_back(track);
}
std::cout << "Classifying tracks DONE" << std::endl;
// Dump tracks
for (std::size_t y = 0; y < tracksMap.getHeight(); ++y)
for (SOM::Coordinate y = 0; y < tracksMap.getHeight(); ++y)
{
for (std::size_t x = 0; x < tracksMap.getWidth(); ++x)
for (SOM::Coordinate x = 0; x < tracksMap.getWidth(); ++x)
{
std::cout << "{" << x << ", " << y << "}" << std::endl;
const auto& tracks = tracksMap[{x, y}];
@@ -223,7 +196,7 @@ int main(int argc, char *argv[])
}
// For each track, get the nearest tracks
for (auto track : trainingTracks)
for (auto track : tracks)
{
auto features = getTrackFeatures(db.getSession(), track, featuresSettings);
@@ -232,27 +205,28 @@ int main(int argc, char *argv[])
normalizer.normalizeData(features);
auto refVectorCoords = network.getClosestRefVectorCoords(features);
auto refVectorPosition = network.getClosestRefVectorPosition(features);
std::cout << "Getting nearest songs for track " << track << " in {" << refVectorCoords.x << ", " << refVectorCoords.y << "}:" << std::endl;
for (auto similarTrack : tracksMap[refVectorCoords])
std::cout << "Getting nearest songs for track " << track << " in {" << refVectorPosition.x << ", " << refVectorPosition.y << "}:" << std::endl;
for (auto similarTrack : tracksMap[refVectorPosition])
std::cout << " - " << similarTrack << std::endl;
std::set<SOM::Coords> neighbourCoords = {refVectorCoords};
std::set<SOM::Position> neighbourPosition = {refVectorPosition};
for (std::size_t i = 0; i < 5; ++i)
{
auto coords = network.getClosestRefVectorCoords(neighbourCoords, medianDistance);
if (!coords)
auto position = network.getClosestRefVectorPosition(neighbourPosition, medianDistance);
if (!position)
break;
std::cout << " - in {" << coords->x << ", " << coords->y << "}, dist = " << network.getRefVectorsDistance(*coords, refVectorCoords) << std::endl;
for (auto similarTrack : tracksMap[*coords])
std::cout << " - in {" << position->x << ", " << position->y << "}, dist = " << network.getRefVectorsDistance(*position, refVectorPosition) << std::endl;
for (auto similarTrack : tracksMap[*position])
std::cout << " - " << similarTrack << std::endl;
neighbourCoords.insert(*coords);
neighbourPosition.insert(*position);
}
}
#endif
std::cout << "Classifying tracks DONE" << std::endl;
}