public void Execute()
{
// - Prerequisites:
// - Graphstructure
// - Training Data
// - 2 Node Classifications
TrainingData = new List <GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> >();
EvaluationData = new List <GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> >();
// decision wether user wants to train or load pre-trained data
var requestTraining = new UserDecision("Use Training.", "Load Training Result.");
requestTraining.Request();
if (requestTraining.Decision == 0) // use training
{
// -n characteristics for each node
{
var request = new UserInput(UserInputLookFor.Folder);
request.DefaultPath = "..\\..\\CRFToolApp\\bin\\Graphs";
request.TextForUser = "******";
request.Request();
GraphDataFolder = request.UserText;
foreach (var file in Directory.EnumerateFiles(GraphDataFolder))
{
var graph = JSONX.LoadFromJSON <GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> >(file);
TrainingData.Add(graph);
}
}
// - Step 1:
// - discretize characteristics
#region Use Training Pede
{
// create features
CreateFeatures(Dataset, TrainingData);
InitCRFScores(TrainingData);
var request = new OLMRequest(OLMVariant.Ising, TrainingData);
request.BasisMerkmale.AddRange(Dataset.NodeFeatures);
request.BasisMerkmale.AddRange(Dataset.EdgeFeatures);
request.LossFunctionValidation = OLM.LossRatio;
request.Request();
// zugehörige Scores erzeugen für jeden Graphen (auch Evaluation)
CreateCRFScores(TrainingData, Dataset.NodeFeatures, request.Result.ResultingWeights);
// store trained Weights
Dataset.NumberIntervals = NumberIntervals;
Dataset.Characteristics = TrainingData.First().Data.Characteristics.ToArray();
Dataset.EdgeCharacteristic = "IsingEdgeCharacteristic";
Dataset.Weights = request.Result.ResultingWeights;
Dataset.SaveAsJSON("results.json");
}
#endregion
}
else
{ // load pre-trained data
var request = new UserInput();
request.TextForUser = "******";
request.Request();
var file = request.UserText;
var trainingResult = JSONX.LoadFromJSON <WorkflowOneDataset>(file);
Dataset = trainingResult;
}
//- Step2:
// User Choice here
{
var request = new UserInput(UserInputLookFor.Folder);
request.TextForUser = "******";
request.Request();
GraphDataFolder = request.UserText;
foreach (var file in Directory.EnumerateFiles(GraphDataFolder))
{
var graph = JSONX.LoadFromJSON <GWGraph <CRFNodeData, CRFEdgeData, CRFGraphData> >(file);
EvaluationData.Add(graph);
}
}
// remove double edges
{
var edgesToRemove = new LinkedList <GWEdge <CRFNodeData, CRFEdgeData, CRFGraphData> >();
foreach (var graph in EvaluationData)
{
foreach (var edge in graph.Edges.ToList())
{
if (graph.Edges.Any(e => (e != edge) && ((e.Foot == edge.Head && e.Head == edge.Foot && e.GWId.CompareTo(edge.GWId) < 0) || (e.Foot == edge.Foot && e.Head == edge.Head && e.GWId.CompareTo(edge.GWId) < 0))))
{
edgesToRemove.Add(edge);
graph.Edges.Remove(edge);
}
}
}
foreach (var edge in edgesToRemove)
{
edge.Foot.Edges.Remove(edge);
edge.Head.Edges.Remove(edge);
}
}
//scores erzeugen
CreateCRFScores(EvaluationData, Dataset.NodeFeatures, Dataset.Weights);
// - Create ROC Curve
{
}
// - Give Maximum with Viterbi
{
foreach (var graph in EvaluationData)
{
var request = new SolveInference(graph, null, 2);
request.Request();
graph.Data.AssginedLabeling = request.Solution.Labeling;
}
//show results in 3D Viewer
{
//var request = new ShowGraphs();
//request.Graphs = EvaluationData;
//request.Request();
}
}
// - Give Sample with MCMC
{
foreach (var graph in EvaluationData)
{
SoftwareGraphLearningParameters parameters = new SoftwareGraphLearningParameters();
parameters.NumberOfGraphs = 60;
parameters.NumberNodes = 50;
parameters.NumberLabels = 2;
parameters.NumberCategories = 4;
parameters.IntraConnectivityDegree = 0.15;
parameters.InterConnectivityDegree = 0.01;
//sample parameters
var samplerParameters = new MHSamplerParameters();
var sglGraph = graph.Convert((nodeData) => new SGLNodeData()
{
}, (edgeData) => new SGLEdgeData(), (graphData) => new SGLGraphData());
samplerParameters.Graph = sglGraph;
samplerParameters.NumberChains = 1;
//sampler starten
var gibbsSampler = new MHSampler();
gibbsSampler.Do(samplerParameters);
}
}
}
public void Do(SoftwareGraphLearningParameters parameters)
{
Build.Do();
var graphs = new List <IGWGraph <SGLNodeData, SGLEdgeData, SGLGraphData> >();
#region Graphen erzeugen
var erdösGraphCreator = new ErdösGraphCreator();
var categoryGraphCreator = new CategoryGraphCreator();
for (int i = 0; i < parameters.NumberOfGraphs; i++)
{
var newGraph = erdösGraphCreator.CreateGraph(new ErdösGraphCreationParameter(parameters));
// Kategoriengraph erzeugen
categoryGraphCreator.CreateCategoryGraph(newGraph);
graphs.Add(newGraph);
//var graph3D = newGraph.Wrap3D();
//new ShowGraph3D(graph3D).Request();
}
#endregion
#region Beobachtungen erzeugen
// TODO: Nach welchem Vorgehen Beobachtungen erzeugen?
// erstmal: für jede Kategorie: zufällig x [0,1): Prob(0) = x, Prob(1) = 1-x
for (int i = 0; i < parameters.NumberOfGraphs; i++)
{
var graph = graphs[i];
// Beobachtung für graph erzeugen
foreach (var node in graph.Nodes)
{
var categoryGraph = graph.Data.CategoryGraph;
var category = categoryGraph.Nodes.ToList().Find(catNode => catNode.Data.Category == node.Data.Category);
double probability = random.NextDouble();
if (probability <= category.Data.ObservationProbabilty)
{
node.Data.Observation = 0;
}
else
{
node.Data.Observation = 1;
}
}
}
//information about graph
/*
* var graphView = new GraphView();
* foreach (var graph in graphs)
* {
* graphView.GetGraphInfo(graph);
*
* } */
#endregion
#region Training
// zwei Parameter: Korrelationsparameter || Konformitätsparameter
// zusätzliche parameter (a (negativ), b) (vorgegeben in SoftwareGraphLearningParameters)
// Zielfunktion setzt sich aus zwei Zielen zusammen:
// Ziel 1: Homogenität: erstmal: Für jede Kategorie: Score := a * (Math.Abs(0.95 - homogenityRatio))
// Ziel 2: Unabhängigkeit: erstmal: Für jede Kategorie: Score += b, falls
// Ziel 3: Korrelation mit lokalen Beobachtungen
// Math.Sign([Mittelwert Knotenscore] - 0.5) == Math.Sign([Mittelwert Knotenlabeling] - 0.5)
// Ziel 3: Korrelation
//in results sind die durchschnittl homogenitäten der graphen für die 10 * 10 datenpunkte gespeichert
var results = new List <LocalResult>();
for (int k = 1; k <= 10; k++)
{
for (int l = 1; l <= 10; l++)
{
var conformity = k * 0.1;
var correlation = l * 0.1;
var isingModel = new IsingModel(conformity, correlation);
//in resultstemp sind homogenitäten für die graphen bei gleicher correlation + conformity
var resultsTemp = new List <LocalResult>();
int counter = 0;
foreach (var graph in graphs)
{
var localResult = new LocalResult(parameters.NumberCategories, conformity, correlation);
#region homogenities normal
// anhand der Observation & Korrelation/Konformität CRF-Scores berechnen
// Berechne Scores für nodes und edges
isingModel.CreateCRFScore(graph);
// Viterbiheuristik starten
var request = new SolveInference(graph, null, parameters.NumberLabels);
request.RequestInDefaultContext();
// sammeln der ergebnisse
var resultingLabeling = request.Solution.Labeling;
//labeling auf nodes mappen
var nodes = graph.Nodes.ToList();
foreach (var node in nodes)
{
node.Data.AssignedLabel = resultingLabeling[node.GraphId];
}
//homogenität berechnen -> in localResult speichern
var categoryGraph = graph.Data.CategoryGraph;
//durch jede kategorie gehen
foreach (var catNode in categoryGraph.Nodes)
{
int amountZeroLabeled = 0;
//für jeden knoten in aktueller kategorie, anzahl 0 labels zählen
foreach (var node in catNode.Data.Nodes)
{
if (node.Data.AssignedLabel == 0)
{
amountZeroLabeled++;
}
}
//homogenität = max(a; 1-a) a = anteil mit 0 gelabelt
var homogenityRatio = Math.Max((amountZeroLabeled * 1.0) / catNode.Data.NumberNodes,
1 - (amountZeroLabeled * 1.0) / catNode.Data.NumberNodes);
//homgenität speichern in homogenity array
localResult.Homs[catNode.Data.Category] = homogenityRatio;
}
#endregion
#region distinction isolated
//distinction isoliert berechnen
foreach (var edge in graph.Edges)
{ //set score of all inter edges to 0
if (edge.Data.Type == EdgeType.Inter)
{
edge.Data.Scores = new double[2, 2] {
{ 0, 0 }, { 0, 0 }
};
}
}
// Viterbiheuristik starten
request = new SolveInference(graph, null, parameters.NumberLabels);
request.RequestInDefaultContext();
// sammeln der ergebnisse
resultingLabeling = request.Solution.Labeling;
//labeling auf nodes mappen
nodes = graph.Nodes.ToList();
foreach (var node in nodes)
{
node.Data.LabelTemp = resultingLabeling[node.GraphId];
}
categoryGraph = graph.Data.CategoryGraph;
//durch jede kategorie gehen
foreach (var catNode in categoryGraph.Nodes)
{
int amountDifferentLabeled = 0;
//für jede kategorie, anzahl unterschiedlich gelabelter knoten zählen
foreach (var node in catNode.Data.Nodes)
{
if (node.Data.AssignedLabel != node.Data.LabelTemp)
{
amountDifferentLabeled++;
}
}
//distinctRatio = anteil der ungleich gelabelten nodes
var distinctRatio = (amountDifferentLabeled * 1.0) / catNode.Data.NumberNodes;
//distinctRatio in distinction array speichern
localResult.Distincts[catNode.Data.Category] = distinctRatio;
}
#endregion
resultsTemp.Add(localResult);
if (counter == 0)
{
graph.SaveAsJSON("exampleGraph_" + k + "_" + l + ".txt");
//var graph3D = graph.Wrap3D();
//new ShowGraph3D(graph3D).Request();
}
counter++;
}//end of foreach graph in graphs
//jetzt durchschnittswerte für alle in resultsTemp berechnen
//und diese durchschnitte in results speichern
var averageResult = new LocalResult(parameters.NumberCategories, conformity, correlation);
//values aufaddieren
foreach (var localresult in resultsTemp)
{
averageResult.AddValues(localresult);
}
//durchschnitte für kategorien
for (int i = 0; i < averageResult.Homs.Length; i++)
{
averageResult.Homs[i] /= resultsTemp.Count;
averageResult.Distincts[i] /= resultsTemp.Count;
}
//gesamtdurchschnitte
double avgHomogenity = 0;
double avgDistinction = 0;
for (int i = 0; i < averageResult.Homs.Length; i++)
{
avgHomogenity += averageResult.Homs[i];
avgDistinction += averageResult.Distincts[i];
}
avgHomogenity /= averageResult.Homs.Length;
avgDistinction /= averageResult.Distincts.Length;
averageResult.AvgHomogenity = avgHomogenity;
averageResult.AvgDistinction = avgDistinction;
averageResult.ResultValue = avgHomogenity + (1 - avgDistinction);
//ausgabe
Log.Post("Konformität: " + conformity + " - Korrelation: " + correlation + " ", LogCategory.Result);
Log.Post("Avg Homogenity: " + averageResult.AvgHomogenity, LogCategory.Result);
Log.Post("Avg Distinction: " + averageResult.AvgDistinction, LogCategory.Result);
Log.Post(Environment.NewLine, LogCategory.Result);
results.Add(averageResult);
}
}
/* Erwartungen:
*
* 1) gleiche Werte in allen Communities
* 2) Homogenität ansteigend in Correlation
*
*
* */
// Auswertung der Homogenität und Unabhängigkeit
var bestResult = results.MaxEntry(r => r.ResultValue);
Log.Post("Exhaustive Search Result:");
Log.Post("conformity: " + bestResult.Conformity);
Log.Post("correlation: " + bestResult.Correlation);
bestResult.SaveAsJSON(@"..\..\bestResult.txt");
results.SaveAsJSON(@"..\..\results.txt");
#endregion
#region OLM
var con = bestResult.Conformity;
var cor = bestResult.Correlation;
//create referenceLabeling for best parameters
var isingModell = new IsingModel(con, cor);
foreach (var graph in graphs)
{
isingModell.CreateCRFScore(graph);
var request = new SolveInference(graph, null, parameters.NumberLabels);
request.RequestInDefaultContext();
graph.Data.ReferenceLabeling = request.Solution.Labeling;
}
var req = new OLMRequest(OLMVariant.Default, graphs);
req.BasisMerkmale = new BasisMerkmal <ICRFNodeData, ICRFEdgeData, ICRFGraphData>[]
{ new IsingMerkmalNode(), new IsingMerkmalEdge() };
req.LossFunctionValidation = LossFunction;
req.MaxIterations = 100;
req.RequestInDefaultContext();
double[] olmWeights = req.Result.ResultingWeights;
Log.Post("OLM Result: ");
for (int i = 0; i < olmWeights.Length; i++)
{
Log.Post(olmWeights[i] + "");
}
#endregion
}//end of Do Method
public static void Run()
{
new FileLogger("Log/");
new ConsoleLoggerSafe();
SoftwareGraphLearningParameters parameters = new SoftwareGraphLearningParameters();
parameters.NumberOfGraphs = 60;
parameters.NumberNodes = 50;
parameters.NumberLabels = 2;
parameters.NumberCategories = 4;
parameters.IntraConnectivityDegree = 0.15;
parameters.InterConnectivityDegree = 0.01;
//new WorkCycle().Do(parameters);
var graph = new ErdösGraphCreator().CreateGraph(new ErdösGraphCreationParameter(parameters));
new CategoryGraphCreator().CreateCategoryGraph(graph);
// Beobachtung für graph erzeugen
foreach (var node in graph.Nodes)
{
var categoryGraph = graph.Data.CategoryGraph;
var category = categoryGraph.Nodes.ToList().Find(catNode => catNode.Data.Category == node.Data.Category);
double probability = CodeBase.BaseProgram.Random.NextDouble();
if (probability <= category.Data.ObservationProbabilty)
{
node.Data.Observation = 0;
}
else
{
node.Data.Observation = 1;
}
}
//scores erzeugen
var isingModel = new IsingModel(1.0, 0.7);
isingModel.CreateCRFScore(graph);
//sample parameters
var samplerParameters = new MHSamplerParameters();
samplerParameters.Graph = graph;
samplerParameters.NumberChains = 3;
//sampler starten
var gibbsSampler = new MHSampler();
gibbsSampler.Do(samplerParameters);
Console.Read();
BaseProgram.Exit.Enter();
}
