private void RunUnsupervisedLearner()
{
UnsupervisedLearner UL = new UnsupervisedLearner();
ResultGroups = UL.run(DeconData);
}
//This function draws the ROC curve by the TP and FP rates calculated from the score.
private void scoreBasedGraph()
{
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
SupervisedLearner SL = new SupervisedLearner();
Feature Fea = readFeature(oFDAddFeatureFiles.FileName);
//Create the list of random composition hypotesis for testing FDR.
//ObtainTrue Position data.
List<CompositionHypothesisEntry> CH = comp.getCompHypo(oFDCompositionTest.FileName);
List<ResultsGroup>[] TrueDATA = SL.EvaluateFeature(oFDTest, CH, Fea);
this.drawGraph(TrueDATA, " Loaded Features");
//Checkbox1 is default features.####################################
List<ResultsGroup>[] DefaultFeature = new List<ResultsGroup>[oFDTest.FileNames.Count()];
String path = Application.StartupPath + "\\FeatureDefault.fea";
Feature DeFea = readFeature(path);
if (checkBox1.Checked == true)
{
List<ResultsGroup>[] TrueDATADefault = SL.EvaluateFeature(oFDTest, CH, DeFea);
this.drawGraph(TrueDATADefault, " Default Features");
}
//################################################
//Checkbox2 is unsupervised Learning. It is a bit different from supervised learning, so it is hard-coded here.
if (checkBox2.Checked == true)
{
UnsupervisedLearner UL = new UnsupervisedLearner();
List<ResultsGroup>[] USLTrueDATA = UL.evaluate(oFDTest, Fea);
//ROC curve needs match to perform, so we will use the match list from Supervised learning and apply them to USLDATA.
for (int i = 0; i < oFDTest.FileNames.Count(); i++)
{
USLTrueDATA[i] = USLTrueDATA[i].OrderByDescending(b => b.DeconRow.MonoisotopicMassWeight).ToList();
int USllasttruematch = 0;
for (int j = 0; j < TrueDATA[i].Count; j++)
{
if (TrueDATA[i][j].Match == true)
{
for (int k = USllasttruematch; k < USLTrueDATA[i].Count; k++)
{
if (USLTrueDATA[i][k].DeconRow.MonoisotopicMassWeight < TrueDATA[i][j].DeconRow.MonoisotopicMassWeight)
{
USllasttruematch = k;
break;
}
if (USLTrueDATA[i][k].DeconRow.MonoisotopicMassWeight == TrueDATA[i][j].DeconRow.MonoisotopicMassWeight)
{
USLTrueDATA[i][k].Match = true;
USLTrueDATA[i][k].PredictedComposition = TrueDATA[i][j].PredictedComposition;
USllasttruematch = k + 1;
break;
}
if (USLTrueDATA[i][k].DeconRow.MonoisotopicMassWeight > TrueDATA[i][j].DeconRow.MonoisotopicMassWeight)
{
USLTrueDATA[i][k].Match = false;
}
}
}
}
}
//Now that both of the data got their matchs, draw the graph
this.drawGraph(USLTrueDATA, " Unsupervised Learning + Loaded Features");
}
//#############################unsupervised learning part ends#################
//Finally populate the Resulting datagridview and the combobox1
comboBox1.Invoke(new MethodInvoker(delegate
{
for (int i = 0; i < TF.Count; i++)
{
comboBox1.Items.Add(TF[i].TableName);
}
comboBox1.SelectedIndex = 0;
}));
dataGridView1.Invoke(new MethodInvoker(delegate
{
dataGridView1.DataSource = TF[0];
}));
}
//This function draws the ROC curve by the TP and FP rates calculated from the false data set
private void DecoyDataROC()
{
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
SupervisedLearner SL = new SupervisedLearner();
Features featuresMenu = new Features();
String currentpath = Application.StartupPath + "\\FeatureCurrent.fea";
Feature currentFeatures = featuresMenu.readFeature(currentpath);
//Create the list of random composition hypotesis for testing FDR.
//ObtainTrue Position data.
falseDataset fD = new falseDataset();
List<CompositionHypothesisEntry> falseCH = fD.genFalse(oFDcposTest.FileName, CompositionHypothesisList, oFDPPMSD);
List<ResultsGroup>[] decoyData = SL.EvaluateFeature(DeconData, falseCH, currentFeatures);
this.drawGraph(decoyData, " ", 0);
//Checkbox1 is default features.####################################
List<ResultsGroup>[] DefaultFeature = new List<ResultsGroup>[DeconData.FileNames.Count()];
String path = Application.StartupPath + "\\FeatureDefault.fea";
Feature DeFea = featuresMenu.readFeature(path);
if (checkBox1.Checked == true)
{
List<ResultsGroup>[] decoyDataDefault = SL.EvaluateFeature(DeconData, falseCH, DeFea);
this.drawGraph(decoyDataDefault, " Default Features", 0);
}
//################################################
//Checkbox2 is unsupervised Learning. It is a bit different from supervised learning, so it is hard-coded here.
UnsupervisedLearner UL = new UnsupervisedLearner();
if (checkBox2.Checked == true)
{
List<ResultsGroup>[] USLFalseDATA = UL.evaluate(DeconData, currentFeatures);
//ROC curve needs match to perform, so we will use the match list from Supervised learning and apply them to USLDATA.
for (int i = 0; i < DeconData.FileNames.Count(); i++)
{
decoyData[i] = decoyData[i].OrderByDescending(a => a.DeconRow.MonoisotopicMassWeight).ToList();
USLFalseDATA[i] = USLFalseDATA[i].OrderByDescending(b => b.DeconRow.MonoisotopicMassWeight).ToList();
int USllasttruematch = 0;
for (int j = 0; j < decoyData[i].Count; j++)
{
if (decoyData[i][j].Match == true)
{
for (int k = USllasttruematch; k < USLFalseDATA[i].Count; k++)
{
if (USLFalseDATA[i][k].DeconRow.MonoisotopicMassWeight < decoyData[i][j].DeconRow.MonoisotopicMassWeight)
{
USllasttruematch = k;
break;
}
if (USLFalseDATA[i][k].DeconRow.MonoisotopicMassWeight == decoyData[i][j].DeconRow.MonoisotopicMassWeight)
{
USLFalseDATA[i][k].Match = true;
USLFalseDATA[i][k].PredictedComposition = decoyData[i][j].PredictedComposition;
USllasttruematch = k + 1;
break;
}
if (USLFalseDATA[i][k].DeconRow.MonoisotopicMassWeight > decoyData[i][j].DeconRow.MonoisotopicMassWeight)
{
USLFalseDATA[i][k].Match = false;
}
}
}
}
}
//Now that both of the data got their matchs, draw the graph
this.drawGraph(USLFalseDATA, " Unsupervised Learning", 0);
}
//#############################unsupervised learning part ends#################
//Finally populate the Resulting datagridview and the combobox1
comboBox2.Invoke(new MethodInvoker(delegate
{
for (int i = 0; i < TF.Count; i++)
{
comboBox2.Items.Add(TF[i].TableName);
}
comboBox2.SelectedIndex = 0;
}));
dataGridView2.Invoke(new MethodInvoker(delegate
{
dataGridView2.DataSource = TF[0];
}));
}
