private void PredictionNuThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex, ref ConcurrentBag<string> nuResults)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
double temp = tempResult.FlaggedAreaSize();
double temp2 = tempResult.CalculateTotalNormalArea();
double areaCovered = ((double)tempResult.FlaggedAreaSize() / tempResult.CalculateTotalNormalArea() > 1) ? 1 : tempResult.FlaggedAreaSize() / (double)tempResult.CalculateTotalNormalArea();
nuResults.Add($"{tempResult.parameter.Nu.ToString()}:"
+ $"{tempResult.CalculateHitResult().eventHits/ (double)tempResult.CalculateHitResult().eventsTotal};"
+ $"{tempResult.CalculateHitResult().hits / ((double)tempResult.CalculateHitResult().misses+ tempResult.CalculateHitResult().hits)};"
+ $"{areaCovered}");
mutex.WaitOne();
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}
// private int numberOfTasks = 10;
private async Task<NoveltyResult> DoNoveltyDetection(SENSOR sensor, int start, int end)
{
string sensorPath = path + "/" + sensor.ToString();
var data = featureVectors[sensor].Select(x => x.Features).ToList();
ConcurrentStack<SVMParameter> svmParams = new ConcurrentStack<SVMParameter>();
//Debug purpose
/*for (int i = 0; i < 10; i++)
{
SVMParameter s = new SVMParameter();
s.C = 100;
s.Gamma = 0.01;
s.Kernel = SVMKernelType.RBF;
s.Type = SVMType.ONE_CLASS;
s.Nu = 0.01;
svmParams.Push(s);
}*/
svmParams.PushRange(GenerateOneClassSVMParameters().ToArray());
SetProgressMax(svmParams.Count + 1);
NoveltyResult bestCoveredResult = null;
Mutex bestResultMu = new Mutex(false, sensor.ToString());
int count = 1;
List<Task> tasks = new List<Task>();
for (int i = 0; i < threadMAX.Value; i++)
{
Task task = Task.Run(() => PredictionThread(ref count, sensor, start, end, ref svmParams, data, svmParams.Count, ref bestCoveredResult, ref bestResultMu));
tasks.Add(task);
}
await Task.WhenAll(tasks);
List<int> integers = Enumerable.Range(1, 100).ToList();
List <double> nus = integers.Select(x => ((double)x) / 100).ToList();
//Cover
foreach (double d in nus)
{
SVMParameter para = new SVMParameter();
para.Gamma = bestCoveredResult.parameter.Gamma;
para.Nu = d;
para.Kernel = bestCoveredResult.parameter.Kernel;
svmParams.Push(para);
}
count = 1;
SetProgressMax(svmParams.Count + 1);
bestResultMu.Dispose();
bestResultMu = new Mutex(false, sensor.ToString());
List<Task> nuCov = new List<Task>();
ConcurrentBag<string> covNu = new ConcurrentBag<string>();
for (int i = 0; i < threadMAX.Value; i++)
{
Task task = Task.Run(() => PredictionNuThread(ref count, sensor, start, end, ref svmParams, data, svmParams.Count, ref bestCoveredResult, ref bestResultMu, ref covNu));
tasks.Add(task);
}
await Task.WhenAll(tasks);
File.WriteAllLines(path + "/Params.txt", new List<string> { bestCoveredResult.parameter.Gamma.ToString(), bestCoveredResult.parameter.Kernel.ToString() });
File.WriteAllLines(path + $"/{sensor.ToString()}CovNu.txt", covNu.ToList());
bestResultMu.Dispose();
return bestCoveredResult;
}
private void PredictionThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
List<OneClassFV> outliersFromSam = new List<OneClassFV>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
mutex.WaitOne();
if (bestCoveredResult == null)
{
bestCoveredResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
}
else if (tempResult.CalculateCoveredScore() > bestCoveredResult.CalculateCoveredScore())
{
//bestResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali); ;
bestCoveredResult = tempResult;
}
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}
private async Task<NoveltyResult> DoVotingNoveltyDetection(SENSOR sensor, int start, int end, double nu, string type)
{
string sensorPath = path + "/" + sensor.ToString();
var data = featureVectors[sensor].Select(x => x.Features).ToList();
ConcurrentStack<SVMParameter> svmParams = new ConcurrentStack<SVMParameter>();
svmParams.PushRange(GenerateVotingSVMParameters(nu).ToArray());
SetProgressMax(svmParams.Count + 1);
NoveltyResult bestResult = null;
Mutex bestResultMu = new Mutex(false, sensor.ToString());
int count = 1;
List<Task> tasks = new List<Task>();
for (int i = 0; i < threadMAX.Value; i++)
{
Task task = Task.Run(() => PredictionVoteThread(ref count, sensor, start, end, ref svmParams, data, svmParams.Count, ref bestResult, bestResultMu, type));
tasks.Add(task);
}
await Task.WhenAll(tasks);
bestResultMu.Dispose();
return bestResult;
}