private void Button_Click_1(object sender, RoutedEventArgs e)
{
string collectionName = CollectionName.Text;
int featureCount = int.Parse(FeatureCount.Text);
//int featureToSelectCount = int.Parse(FeatureToSelectCount.Text);
DatabaseService databaseService = new DatabaseService();
var dataSet = databaseService.ConvertMongoColectionToListOfLists(featureCount, collectionName);
SpearmanCorrelation spearmanCorrelation = new SpearmanCorrelation();
var wynik = spearmanCorrelation.MakeCorelationTable(featureCount, collectionName, dataSet);
////before your loop
var csv = new StringBuilder();
for (int i = 0; i < featureCount + 1; i++)
{
var newLine = string.Empty;
for (int j = 0; j < featureCount + 1; j++)
{
newLine += wynik[i, j].ToString() + ";";
}
csv.AppendLine(newLine);
}
File.WriteAllText($"E://cos//wynikiDobreDoMGR//Spearman_{collectionName}_corelationTable_{DateTime.Now.ToString("yyyy-dd-M--HH-mm-ss")}.txt", csv.ToString());
//FiltersHelper filtersHelper = new FiltersHelper();
//filtersHelper.SelectFeaturesAndWriteToFile(wynik, featureToSelectCount, "Spearman_" + collectionName, featureCount);
FeatureSelection featureSelection = new FeatureSelection();
featureSelection.SelectFeaturesAndCheckClasification(wynik, featureCount + 1, dataSet, collectionName, "Spearman");
MessageBox.Show("Wykonano obliczenia.");
}
public void RandomForestsCrossValidationTest_2()
{
this.ProcessPcapFile(SnoopersPcaps.Default.app_identification_dnsHttpTls_cap);
var applicationProtocolModelEvaluator = this.AppIdentService.CreateApplicationProtocolModels(this.L7Conversations, 10);
var featureVectors = applicationProtocolModelEvaluator.TrainingFeatureVectors.ToList();
var appIdentAcordSource = new AppIdentAcordSource();
appIdentAcordSource.Init(featureVectors);
Console.WriteLine($"Feature vectors: {featureVectors.Count}");
var featureSelection = new FeatureSelection(appIdentAcordSource.FeatureSelector);
featureSelection.ProcessFeatureSelection(appIdentAcordSource, 0.5);
foreach (var feature in featureSelection.FeatureSelector.SelectedFeatures)
{
Console.WriteLine($"{feature.Name}");
}
var rfcModel = this.AccordAppIdent.GetBestRandomForestsWithGridSearch(appIdentAcordSource, out var bestParameters, out var minError);
// get the cross validation results
var cvResults = this.AccordAppIdent.GetCrossValidationResultsOfRandomForestModel(appIdentAcordSource, bestParameters);
Console.WriteLine("### CV Results ###");
Console.WriteLine("\n### Training stats ###");
Console.WriteLine(">> model error mean: {0}\n>> model std: {1}", Math.Round(cvResults.Training.Mean, 6), Math.Round(cvResults.Training.StandardDeviation, 6));
Console.WriteLine("\n### Validation stats ###");
Console.WriteLine(">> model error mean: {0}\n>> model std: {1}", Math.Round(cvResults.Validation.Mean, 6), Math.Round(cvResults.Validation.StandardDeviation, 6));
var minErorr = cvResults.Validation.Values.Min();
var bestIndex = cvResults.Validation.Values.IndexOf(minErorr);
var classifier = cvResults.Models[bestIndex];
var validationDataSource = classifier.Tag as AccordAppIdent.ValidationDataSource;
var predictedValues = classifier.Model.Decide(validationDataSource.ValidationInputs);
var i = 0;
foreach (var label in appIdentAcordSource.Labels.Distinct())
{
var conf = new ConfusionMatrix(validationDataSource.ValidationOutputs, predictedValues, i++);
Console.WriteLine($"##########################");
Console.WriteLine($"Protocol: {label}");
Console.WriteLine($"TP: {conf.TruePositives}");
Console.WriteLine($"TN: {conf.TrueNegatives}");
Console.WriteLine($"FN: {conf.FalseNegatives}");
Console.WriteLine($"FP: {conf.FalsePositives}");
Console.WriteLine($"Accuracy: {conf.Accuracy}");
Console.WriteLine($"Precision: {conf.Precision}");
Console.WriteLine($"Specificity: {conf.Specificity}");
Console.WriteLine($"Sensitivity: {conf.Sensitivity}");
Console.WriteLine($"FScore: {conf.FScore}");
}
}
public FeatureSelector EliminateCorelatedFeatures(AppIdentAcordSource appIdentAcordSource, double trashold, AppIdentTestContext appIdentTestContext)
{
var featureSelection = new FeatureSelection();
featureSelection.ProcessFeatureSelection(appIdentAcordSource, trashold);
var featureSelector = appIdentAcordSource.FeatureSelector;
appIdentTestContext.Save(featureSelector);
return(featureSelector);
}
// Left hand
public override object VisitComposite_selector_simple([NotNull] GrammarParser.Composite_selector_simpleContext context)
{
// 1. Visit the children (Feature_selector_abs and Attribute_selector)
FeatureSelection matchingFeatureSelection = (FeatureSelection)Visit(context.feature_selector_abs());
string attributeIdentifier = (string)Visit(context.attribute_selector());
// 2. Get the matching AttributeValue
AttributeValue matchingAttributeValue = matchingFeatureSelection.AttributeValues.Find(attrval => attrval.AttributeIdentifier == attributeIdentifier);
// 3. Return the AttributeValue together with its parent FeatureSelection
return(new AttrValAndFeatureSelPair()
{
ParentFeatureSelection = matchingFeatureSelection,
AttributeValue = matchingAttributeValue
});
}
//[DotMemoryUnit(CollectAllocations = true)]
//[AssertTraffic(AllocatedSizeInBytes = 1024*1024)]
public void CorrelationMatrixTest_M2()
{
MainWindow window = null;
this.ProcessPcapFile(SnoopersPcaps.Default.app_identification_dnsHttpTls_cap);
this.ProcessPcapFile(SnoopersPcaps.Default.app_identification_learn1_cap);
this.ProcessPcapFile(SnoopersPcaps.Default.app_identification_refSkype_cap);
this.ProcessPcapFile(SnoopersPcaps.Default.app_identification_streamSkypeHttpTls_cap);
this.ProcessPcapFile(SnoopersPcaps.Default.app_identification_testM1_cap);
this.ProcessPcapFile(SnoopersPcaps.Default.app_identification_testM2_cap);
List <L7Conversation> testSet;
var applicationProtocolModelEvaluator = this.AppIdentService.CreateApplicationProtocolModels(this.L7Conversations, 0.9, out testSet);
var featureVectors = applicationProtocolModelEvaluator.TrainingFeatureVectors.ToList();
var appIdentAcordSource = new AppIdentAcordSource();
appIdentAcordSource.Init(featureVectors);
var featureSelection = new FeatureSelection(appIdentAcordSource.FeatureSelector);
var selection = featureSelection.ProcessFeatureSelection(appIdentAcordSource, 0.3);
var correlationMatrix = selection.Last();
// The dispatcher thread
var t = new Thread(() =>
{
window = new MainWindow
{
DataContext = new MainViewModel(appIdentAcordSource.FeatureNames, correlationMatrix)
};
//window.DataContext = new MainViewModel();
// Initiates the dispatcher thread shutdown when the window closes
window.Closed += (s, e) => window.Dispatcher.InvokeShutdown();
window.Show();
// Makes the thread support message pumping
Dispatcher.Run();
});
// Configure the thread
t.SetApartmentState(ApartmentState.STA);
t.Start();
t.Join();
}
// Common
public override object VisitFeature_selector_abs([NotNull] GrammarParser.Feature_selector_absContext context)
{
// Variables
string featureIdentifier = context.IDENTIFIER() != null?context.IDENTIFIER().ToString() : null;
bool isRootFeatureSelector = context.ROOTFEATURE() != null ? true : false;
FeatureSelection matchingFeatureSelection = null;
// Find the matching FeatureSelection in the ConfigurationInstance
if (featureIdentifier != null)
{
matchingFeatureSelection = configInstance.GetFeatureSelectionByFeatureIdentifier(featureIdentifier);
}
else if (isRootFeatureSelector)
{
matchingFeatureSelection = configInstance.RootFeatureSelection;
}
//
return(matchingFeatureSelection);
}
public override object VisitComposite_selector_advanced([NotNull] GrammarParser.Composite_selector_advancedContext context)
{
// 1. Visit the children (Feature_selector_abs, Feature_selector_rel and Attribute_selector)
FeatureSelection absFeatureSelection = (FeatureSelection)Visit(context.feature_selector_abs());
// 2. Get the matching relative FeatureSelections
var relativeSelectorType = (int)Visit(context.feature_selector_rel());
List <FeatureSelection> relativeMatchingFeatureSelections = null;
if (relativeSelectorType == GrammarParser.DESCENDANTS_KEYWORD)
{
relativeMatchingFeatureSelections = configInstance
.GetDescendantFeatureSelections(absFeatureSelection)
.Where(featureSel => featureSel.SelectionState == Core.BLOs.FeatureSelectionStates.Selected)
.ToList();
}
else
{
throw new Exception("Only >descendants supported by the relative feature selector");
}
// 3. Get the matching AttributeValues for all of the relative FeatureSelections
string attributeIdentifier = (string)Visit(context.attribute_selector());
List <AttributeValue> matchingAttributeValues = new List <AttributeValue>();
relativeMatchingFeatureSelections.ForEach(featureSelection =>
{
AttributeValue attrVal = featureSelection.AttributeValues.FirstOrDefault(attributeVal => attributeVal.AttributeIdentifier == attributeIdentifier);
if (attrVal != null)
{
matchingAttributeValues.Add(attrVal);
}
});
return(matchingAttributeValues);
}
private static void generateTree(DTreeNode currentNode)
{
FeatureSelection featureSelection = new FeatureSelection();
DTreeNode splitNode = featureSelection.GetSplitingFeature(currentNode);
if (splitNode == null)
{
return;
}
currentNode.entropy = splitNode.entropy;
if (currentNode.entropy == 0.0)
{
currentNode.className = splitNode.className;
currentNode.isLeaf = true;
return;
}
currentNode.spliteFeatureName = splitNode.spliteFeatureName;
currentNode.informationGain = splitNode.informationGain;
foreach (DTreeNode child in splitNode.childrenNodes)
{
DTreeNode childNode = new DTreeNode();
childNode.spliteFeatureValue = child.spliteFeatureValue;
List <FeatureDataPair> featureValuePairs = currentNode.previousFeatureValues.ToList();
featureValuePairs.Add(new FeatureDataPair(currentNode.spliteFeatureName, childNode.spliteFeatureValue));
childNode.previousFeatureValues = featureValuePairs;
childNode.depth = currentNode.depth + 1;
currentNode.childrenNodes.Add(childNode);
generateTree(childNode);
}
}