//First method, run supervised learning with composition hypothesis.
public MS1ResultsViewer(OpenFileDialog oFDDeconData, String ComHypoLink)
{
InitializeComponent();
DeconData = oFDDeconData;
CompositionHypothesisTabbedForm ct = new CompositionHypothesisTabbedForm();
CompositionHypothesisList = ct.getCompHypo(ComHypoLink);
//This part is used to show the "Please Wait" box while running the code.
BackgroundWorker bw = new BackgroundWorker();
bw.DoWork += new DoWorkEventHandler(bw_DoWork);
bw.RunWorkerCompleted += new RunWorkerCompletedEventHandler(bw_RunWorkerCompleted);
//set lable and your waiting text in this form
try
{
bw.RunWorkerAsync();//this will run the DoWork code at background thread
WaitForm.ShowDialog();//show the please wait box
}
catch (Exception ex)
{
MessageBox.Show(ex.ToString());
}
var elementsAndMolecules = GroupingResults.GetElementsAndMolecules(ResultGroups[0]);
ElementIDs = elementsAndMolecules.Item1;
MoleculeNames = elementsAndMolecules.Item2;
//ElementIDs.Clear();
//MoleculeNames.Clear();
//for (int i = 0; i < ResultGroups[0].Count(); i++)
//{
// if (ResultGroups[0][i].PredictedComposition.ElementNames.Count() > 0)
// {
// for (int j = 0; j < ResultGroups[0][i].PredictedComposition.ElementNames.Count(); j++)
// {
// ElementIDs.Add(ResultGroups[0][i].PredictedComposition.ElementNames[j]);
// }
// for (int j = 0; j < ResultGroups[0][i].PredictedComposition.MoleculeNames.Count(); j++)
// {
// MoleculeNames.Add(ResultGroups[0][i].PredictedComposition.MoleculeNames[j]);
// }
// }
//}
comboBox1.DataSource = oFDDeconData.FileNames;
comboBox1.SelectedIndex = 0;
VolumeHistogramFileSelector.DataSource = oFDDeconData.FileNames;
VolumeHistogramFileSelector.SelectedIndex = 0;
ResultsGridView.DataSource = ToDataTable(ResultGroups, 0);
ResultsGridView.ReadOnly = true;
}
//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];
}));
}
void bw_DoWork(object sender, DoWorkEventArgs e)
{
//Obtain Feature
Feature Ans = new Feature();
SupervisedLearner SL = new SupervisedLearner();
CompositionHypothesisTabbedForm comp = new CompositionHypothesisTabbedForm();
List<CompositionHypothesisEntry> comhy = comp.getCompHypo(oFDComposition.FileName);
Ans = SL.obtainFeatures(oFDTrain, comhy);
//Write Features to File
String path = Application.StartupPath + "\\FeatureCurrent.fea";
FileStream FS = new FileStream(path, FileMode.Create, FileAccess.Write);
StreamWriter Write1 = new StreamWriter(FS);
Write1.Write(Ans.Initial + "," + Ans.numChargeStates + "," + Ans.ScanDensity + "," + Ans.numModiStates + "," + Ans.totalVolume + "," + Ans.ExpectedA + "," + Ans.CentroidScan + "," + Ans.numOfScan + "," + Ans.avgSigNoise);
Write1.Flush();
Write1.Close();
FS.Close();
}
