Thread CreateMachineThread(string name, List<SVMParameter> pars, List<Feature> feats, SAMDataPoint.FeelingModel feels, Action<int, int> UpdateCallback, bool useControlSAM)
{
return new Thread(() =>
{
StdClassifier mac = new StdClassifier(name, pars, feats, samData);
mac.UpdateCallback = UpdateCallback;
var res = mac.OldCrossValidate(feels, 1, useControlSAM);
SaveBestResult(res, mac.Name + "_" + feels);
});
}
private void btn_CalculateResults_Click(object sender, EventArgs e)
{
FolderBrowserDialog fbd = new FolderBrowserDialog() { Description = "Select folder to load test subjects from" };
if (fbd.ShowDialog() == DialogResult.OK)
{
FolderBrowserDialog sfd = new FolderBrowserDialog() { Description = "Select folder to load excel sheets from" };
if (sfd.ShowDialog() == DialogResult.OK)
{
Excel.Application exc = new Excel.Application() { Visible = false };
List<string> singles = new List<string>()
{
"EEG",
"FACE",
"GSR",
"HR"
};
List<string> fusion = new List<string>()
{
"Stacking",
"Voting"
};
Dictionary<SAMDataPoint.FeelingModel, int> cellOffsets = new Dictionary<SAMDataPoint.FeelingModel, int>()
{
[SAMDataPoint.FeelingModel.Arousal2High] = 11,
[SAMDataPoint.FeelingModel.Arousal2Low] = 24,
[SAMDataPoint.FeelingModel.Arousal3] = 40,
[SAMDataPoint.FeelingModel.Valence2High] = 53,
[SAMDataPoint.FeelingModel.Valence2Low] = 66,
[SAMDataPoint.FeelingModel.Valence3] = 82
};
//test subject[SAMModel[machineType]]
//items: C, Gamma, Kernel
var configs = new Dictionary<string, Dictionary<SAMDataPoint.FeelingModel, Dictionary<string, Tuple<double, double, int>>>>();
foreach (var configBook in singles.Concat(fusion))
{
Log.LogMessage($"Reading {configBook}...");
Excel.Workbook dataBook = exc.Workbooks.Open(sfd.SelectedPath + $"/{configBook}.xlsx", ExcelHandler.missingValue,
false,
ExcelHandler.missingValue,
ExcelHandler.missingValue,
ExcelHandler.missingValue,
true,
ExcelHandler.missingValue,
ExcelHandler.missingValue,
true,
ExcelHandler.missingValue,
ExcelHandler.missingValue,
ExcelHandler.missingValue);
foreach (Excel.Worksheet sheet in dataBook.Sheets)
{
if (sheet.Name == "Overview" || sheet.Name == "First" || sheet.Name == "Last") continue;
if (!configs.ContainsKey(sheet.Name))
{
configs.Add(sheet.Name, new Dictionary<SAMDataPoint.FeelingModel, Dictionary<string, Tuple<double, double, int>>>());
}
if (configs[sheet.Name].Count == 0)
{
foreach (SAMDataPoint.FeelingModel feel in Enum.GetValues(typeof(SAMDataPoint.FeelingModel)))
{
configs[sheet.Name].Add(feel, new Dictionary<string, Tuple<double, double, int>>());
singles.ForEach(x => configs[sheet.Name][feel].Add(x, null));
fusion.ForEach(x => configs[sheet.Name][feel].Add(x, null));
if (feel.ToString().Contains("Valence"))
{
configs[sheet.Name][feel].Remove("GSR");
}
}
}
foreach (SAMDataPoint.FeelingModel feel in Enum.GetValues(typeof(SAMDataPoint.FeelingModel)))
{
if (configBook == "GSR" && feel.ToString().Contains("Valence")) continue;
Excel.Range c = (Excel.Range)sheet.Cells[cellOffsets[feel], 3];
Excel.Range gamma = (Excel.Range)sheet.Cells[cellOffsets[feel] + 1, 3];
Excel.Range kernel = (Excel.Range)sheet.Cells[cellOffsets[feel] + 2, 3];
if (c.Value != null && gamma.Value != null && kernel.Value != null)
{
configs[sheet.Name][feel][configBook] = Tuple.Create(c.Value, gamma.Value, (int)kernel.Value);
}
}
}
dataBook.Close();
}
List<string> skippedList = new List<string>();
foreach (var subject in configs)
{
foreach (var feel in subject.Value)
{
foreach (var machine in feel.Value)
{
if (machine.Value == null)
{
skippedList.Add(subject.Key);
}
}
}
}
foreach (var skipped in skippedList)
{
configs.Remove(skipped);
Log.LogMessage($"Skipped {skipped}");
}
var resultsList = new Dictionary<string, Dictionary<string, List<int>>>();
foreach (string feel in Enum.GetValues(typeof(SAMDataPoint.FeelingModel)))
{
resultsList.Add(feel, new Dictionary<string, List<int>>());
resultsList.Add("SAM", new Dictionary<string, List<int>>());
foreach (var item in singles.Concat(fusion))
{
resultsList[feel].Add(item, new List<int>());
}
}
foreach (var subject in configs)
{
if (LoadData(fbd.SelectedPath + $"/{subject.Key}", _fd))
{
foreach (var feel in subject.Value)
{
resultsList["SAM"][feel.Key.ToString()].AddRange(samData.dataPoints.Select(x => x.ToAVCoordinate(feel.Key)));
foreach (var machine in feel.Value)
{
Log.LogMessage($"Calculating {subject.Key} / {feel} / {machine}");
var param = new SVMParameter()
{
C = machine.Value.Item1,
Gamma = machine.Value.Item2,
Kernel = (SVMKernelType)machine.Value.Item3
};
if (singles.Contains(machine.Key))
{
var classifier = new StdClassifier(new SVMConfiguration(param, FeatureCreator.GetFeatures(machine.Key, feel.Key)), samData);
var results = classifier.OldCrossValidate(feel.Key, 1);
foreach (var res in results[0].guesses)
{
resultsList[feel.Key.ToString()][machine.Key].Add(res);
}
}
else
{
//var classifier = new MetaClassifier(machine.Key, param, samData)
}
}
}
}
else
{
Log.LogMessage($"Skipped {subject.Key} due to bad data");
skippedList.Add(subject.Key);
}
}
Excel.Workbook resultBook = exc.Workbooks.Add(ExcelHandler.missingValue);
Excel.Worksheet metaSheet = resultBook.Sheets.Add();
metaSheet.Name = "Meta";
metaSheet.Cells[1, 1] = "Skipped " + skippedList.Count;
for (int i = 0; i < skippedList.Count; i++)
{
metaSheet.Cells[i + 2, 1] = skippedList[i];
}
foreach (var feel in Enum.GetNames(typeof(SAMDataPoint.FeelingModel)))
{
Excel.Worksheet feelSheet = resultBook.Sheets.Add();
feelSheet.Name = feel;
}
exc.Quit();
}
}
}
private void btn_RunAll_Click(object sender, EventArgs e)
{
FolderBrowserDialog fbd = new FolderBrowserDialog();
if (fbd.ShowDialog() == DialogResult.OK)
{
chk_useControlValues.Enabled = false;
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
Log.LogMessage("Starting Stopwatch");
List<SAMDataPoint.FeelingModel> feelings = new List<SAMDataPoint.FeelingModel>()
{
SAMDataPoint.FeelingModel.Arousal2High,
SAMDataPoint.FeelingModel.Arousal2Low,
SAMDataPoint.FeelingModel.Arousal3,
SAMDataPoint.FeelingModel.Valence2Low,
SAMDataPoint.FeelingModel.Valence2High,
SAMDataPoint.FeelingModel.Valence3
};
eh = new ExcelHandler(fbd.SelectedPath);
if (!eh.BooksOpen)
{
Log.LogMessage("Cannot open or write to books");
return;
}
var dataFolders = Directory.GetDirectories(fbd.SelectedPath);
List<SVMParameter> parameters = GenerateSVMParameters();
//Debug param
/* List<SVMParameter> parameters = new List<SVMParameter> { new SVMParameter() };
parameters[0].C = 32;
parameters[0].Gamma = 0.25;
parameters[0].Kernel = SVMKernelType.SIGMOID;*/
int curDat = 1;
int maxDat = dataFolders.Length;
foreach (var item in dataFolders)
{
if (item.Split('\\').Last() == "Stats")
{
Log.LogMessage("Stats folder skipping");
continue;
}
DataProgressHandler DPH = new DataProgressHandler(item);
if (DPH.AllDone)
{
Log.LogMessage("Already did " + item + ", skipping..");
curDat++;
continue;
}
if (!LoadData(item, _fd))
{
Log.LogMessage(item.Split('-').Last() + " is not classifiable");
continue;
}
string personName = item.Split('\\').Last();
eh.AddPersonToBooks(personName);
foreach (var feel in feelings)
{
statusLabel.Text = "STANDARD: " + curDat + "/" + maxDat + " -> " + feel + " -> " + item.Split('\\').Last();
gsrProg = 0;
gsrTot = 1;
hrProg = 0;
hrTot = 1;
eegProg = 0;
eegTot = 1;
faceProg = 0;
faceTot = 1;
bool gsrWrite = false;
bool hrWrite = false;
bool eegWrite = false;
bool faceWrite = false;
if (feel != SAMDataPoint.FeelingModel.Valence2High &&
feel != SAMDataPoint.FeelingModel.Valence2Low &&
feel != SAMDataPoint.FeelingModel.Valence3 &&
!skipGSR &&
!DPH.done["GSR" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] &&
shouldRun["GSR.dat"])
{
gsrThread = CreateMachineThread("GSR", parameters, FeatureCreator.GSRArousalOptimizationFeatures, feel, (cur, max) => { gsrProg = cur; gsrTot = max; }, chk_useControlValues.Checked);
gsrThread.Priority = threadPrio;
gsrThread.Start();
}
else
{
Log.LogMessage("GSR skipping");
}
if (!DPH.done["HR" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] && !skipHR && shouldRun["HR.dat"])
{
hrThread = CreateMachineThread("HR", parameters,
(feel == SAMDataPoint.FeelingModel.Valence2High || feel == SAMDataPoint.FeelingModel.Valence2Low || feel == SAMDataPoint.FeelingModel.Valence3)
? FeatureCreator.HRValenceOptimizationFeatures : FeatureCreator.HRArousalOptimizationFeatures,
feel, (cur, max) => { hrProg = cur; hrTot = max; }, chk_useControlValues.Checked);
hrThread.Priority = threadPrio;
hrThread.Start();
}
else
{
Log.LogMessage("HR skipping");
}
if (!DPH.done["EEG" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] && !skipEEG && shouldRun["EEG.dat"])
{
eegThread = CreateMachineThread("EEG", parameters,
(feel == SAMDataPoint.FeelingModel.Valence2High || feel == SAMDataPoint.FeelingModel.Valence2Low || feel == SAMDataPoint.FeelingModel.Valence3)
? FeatureCreator.EEGValenceOptimizationFeatures : FeatureCreator.EEGArousalOptimizationFeatures,
feel, (cur, max) => { eegProg = cur; eegTot = max; }, chk_useControlValues.Checked);
eegThread.Priority = threadPrio;
eegThread.Start();
}
else
{
Log.LogMessage("EEG skipping");
}
if (!DPH.done["Face" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] && !skipFace && shouldRun["KINECT.dat"])
{
faceThread = CreateMachineThread("FACE", parameters,
(feel == SAMDataPoint.FeelingModel.Valence2High || feel == SAMDataPoint.FeelingModel.Valence2Low || feel == SAMDataPoint.FeelingModel.Valence3)
? FeatureCreator.FACEValenceOptimizationFeatures : FeatureCreator.FACEArousalOptimizationFeatures,
feel, (cur, max) => { faceProg = cur; faceTot = max; }, chk_useControlValues.Checked);
faceThread.Priority = threadPrio;
faceThread.Start();
}
else
{
Log.LogMessage("Face skipping");
}
List<SVMConfiguration> confs = new List<SVMConfiguration>();
SVMConfiguration gsrConf;
SVMConfiguration eegConf;
SVMConfiguration hrConf;
SVMConfiguration faceConf;
while ((gsrThread != null && gsrThread.IsAlive) || (hrThread != null && hrThread.IsAlive) || (eegThread != null && eegThread.IsAlive) || (faceThread != null && faceThread.IsAlive))
{
Thread.Sleep(1000);
eegBar.Value = (int)(((double)eegProg / eegTot) * 100);
gsrBar.Value = (int)(((double)gsrProg / gsrTot) * 100);
faceBar.Value = (int)(((double)faceProg / faceTot) * 100);
hrBar.Value = (int)(((double)hrProg / hrTot) * 100);
Application.DoEvents();
if (gsrThread != null && !gsrThread.IsAlive && !gsrWrite)
{
gsrWrite = true;
gsrConf = svmConfs.OfType<SVMConfiguration>().First((x) => x.Name.StartsWith("GSR") && x.Name.Contains(feel.ToString()));
confs.Add(gsrConf);
var gsrMac = new StdClassifier(gsrConf, samData);
var gsrRes = gsrMac.OldCrossValidate(feel, 1);
Log.LogMessage("Best result for person " + curDat + " GSR " + feel + " is " + gsrRes[0].GetAccuracy());
eh.AddDataToPerson(personName, ExcelHandler.Book.GSR, gsrRes.First(), feel);
DPH.done["GSR" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] = true;
DPH.SaveProgress();
gsrThread = null;
}
if (hrThread != null && !hrThread.IsAlive && !hrWrite)
{
hrWrite = true;
hrConf = svmConfs.OfType<SVMConfiguration>().First((x) => x.Name.StartsWith("HR") && x.Name.Contains(feel.ToString()));
confs.Add(hrConf);
var hrMac = new StdClassifier(hrConf, samData);
var hrRes = hrMac.OldCrossValidate(feel, 1);
Log.LogMessage("Best result for person " + curDat + " HR " + feel + " is " + hrRes[0].GetAccuracy());
eh.AddDataToPerson(personName, ExcelHandler.Book.HR, hrRes.First(), feel);
DPH.done["HR" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] = true;
DPH.SaveProgress();
hrThread = null;
}
if (eegThread != null && !eegThread.IsAlive && !eegWrite)
{
eegWrite = true;
eegConf = svmConfs.OfType<SVMConfiguration>().First((x) => x.Name.StartsWith("EEG") && x.Name.Contains(feel.ToString()));
confs.Add(eegConf);
var eegMac = new StdClassifier(eegConf, samData);
var eegRes = eegMac.OldCrossValidate(feel, 1);
Log.LogMessage("Best result for person " + curDat + " EEG " + feel + " is " + eegRes[0].GetAccuracy());
eh.AddDataToPerson(personName, ExcelHandler.Book.EEG, eegRes.First(), feel);
DPH.done["EEG" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] = true;
DPH.SaveProgress();
eegThread = null;
}
if (faceThread != null && !faceThread.IsAlive && !faceWrite)
{
faceWrite = true;
faceConf = svmConfs.OfType<SVMConfiguration>().First((x) => x.Name.StartsWith("FACE") && x.Name.Contains(feel.ToString()));
confs.Add(faceConf);
var faceMac = new StdClassifier(faceConf, samData);
var faceRes = faceMac.OldCrossValidate(feel, 1);
Log.LogMessage("Best result for person " + curDat + " Face " + feel + " is " + faceRes[0].GetAccuracy());
eh.AddDataToPerson(personName, ExcelHandler.Book.FACE, faceRes.First(), feel);
DPH.done["Face" + Enum.GetName(typeof(SAMDataPoint.FeelingModel), feel)] = true;
DPH.SaveProgress();
faceThread = null;
}
}
Log.LogMessage("Done with single machine searching.");
foreach (var cnf in confs)
{
Log.LogMessage("Saving " + cnf.Name + "...");
}
//Write normal results
eh.Save();
Log.LogMessage("Total time: " + stopwatch.Elapsed + " Current person: " + curDat + " and model " + feel.ToString());
}
curDat++;
eh.Save();
Log.LogMessage("DonnoDK");
}
eh.CloseBooks();
Log.LogMessage("Closing books and saving");
Log.LogMessage("Done in: " + stopwatch.Elapsed);
}
}