Changeset 10908 for branches/DataPreprocessing/HeuristicLab.DataPreprocessing.Views/3.3/PreprocessingFeatureCorrelationView.cs

Timestamp:

05/28/14 15:09:26 (10 years ago)

Author:

mleitner

Message:

Add Feature correlation matrix, Add limit for distinct values in histogramm classification.

File:

• branches/DataPreprocessing/HeuristicLab.DataPreprocessing.Views/3.3/PreprocessingFeatureCorrelationView.cs (copied) (copied from branches/DataPreprocessing/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.cs) (6 diffs)

branches/DataPreprocessing/HeuristicLab.DataPreprocessing.Views/3.3/PreprocessingFeatureCorrelationView.cs

@@ -28 +28 @@
 using HeuristicLab.MainForm.WindowsForms;
 using HeuristicLab.PluginInfrastructure;
+using System;
+using HeuristicLab.DataPreprocessing;
 
 namespace HeuristicLab.Problems.DataAnalysis.Views {
-  [View("Feature Correlation View")]
-  [Content(typeof(DataAnalysisProblemData), false)]
-  public abstract partial class AbstractFeatureCorrelationView : AsynchronousContentView {
+  [View("Preprocessing Feature Correlation View")]
+  [Content(typeof(CorrelationMatrixContent), false)]
+  public partial class PreprocessingFeatureCorrelationView : AsynchronousContentView {
     public const string ALLSAMPLES = "All Samples";
     public const string TRAININGSAMPLES = "Training Samples";
@@ -41 +43 @@
     protected FeatureCorrelationCalculator fcc;
 
-    public new DataAnalysisProblemData Content {
-      get { return (DataAnalysisProblemData)base.Content; }
+    public new CorrelationMatrixContent Content
+    {
+      get { return (CorrelationMatrixContent) base.Content; }
       set { base.Content = value; }
     }
 
-    protected AbstractFeatureCorrelationView() {
+    private FeatureCorrelationCache correlationCache;
+
+    public PreprocessingFeatureCorrelationView() {
+
+      correlationCache = new FeatureCorrelationCache();
       InitializeComponent();
       fcc = new FeatureCorrelationCalculator();
@@ -75 +82 @@
       fcc.TryCancelCalculation();
       if (Content != null) {
-        fcc.ProblemData = Content;
+        fcc.ProblemData = Content.ProblemData;
         CalculateCorrelation();
       } else {
@@ -86 +93 @@
 
     protected virtual bool[] SetInitialVariableVisibility() {
-      bool[] initialVisibility = new bool[Content.Dataset.DoubleVariables.Count()];
+      bool[] initialVisibility = new bool[Content.ProblemData.Dataset.DoubleVariables.Count()];
       int i = 0;
-      foreach (var variable in Content.Dataset.DoubleVariables) {
-        initialVisibility[i] = Content.AllowedInputVariables.Contains(variable);
+      foreach (var variable in Content.ProblemData.Dataset.DoubleVariables) {
+        initialVisibility[i] = Content.ProblemData.AllowedInputVariables.Contains(variable);
         i++;
       }
@@ -102 +109 @@
     }
 
-    protected abstract void CalculateCorrelation();
-    protected abstract void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e);
+     protected void CalculateCorrelation() {
+      if (correlationCalcComboBox.SelectedItem == null) return;
+      if (partitionComboBox.SelectedItem == null) return;
+
+      IDependencyCalculator calc = (IDependencyCalculator)correlationCalcComboBox.SelectedValue;
+      string partition = (string)partitionComboBox.SelectedValue;
+      dataView.Enabled = false;
+      double[,] corr = correlationCache.GetCorrelation(calc, partition);
+      if (corr == null) {
+        fcc.CalculateElements(calc, partition);
+      } else {
+        fcc.TryCancelCalculation();
+        var correlation = new DoubleMatrix(corr, Content.ProblemData.Dataset.DoubleVariables, Content.ProblemData.Dataset.DoubleVariables);
+        UpdateDataView(correlation);
+      }
+    }
+
+    protected void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e) {
+      if (InvokeRequired) {
+        Invoke(new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished), sender, e);
+        return;
+      }
+      correlationCache.SetCorrelation(e.Calculcator, e.Partition, e.Correlation);
+      var correlation = new DoubleMatrix(e.Correlation, Content.ProblemData.Dataset.DoubleVariables, Content.ProblemData.Dataset.DoubleVariables);
+      UpdateDataView(correlation);
+    }
 
     protected void UpdateDataView(DoubleMatrix correlation) {
@@ -125 +156 @@
       progressBar.Value = e.ProgressPercentage;
     }
+
+      [NonDiscoverableType]
+      private class FeatureCorrelationCache : Object {
+        private Dictionary<Tuple<IDependencyCalculator, string>, double[,]> correlationsCache;
+
+        public FeatureCorrelationCache()
+          : base() {
+          InitializeCaches();
+        }
+
+        private void InitializeCaches() {
+          correlationsCache = new Dictionary<Tuple<IDependencyCalculator, string>, double[,]>();
+        }
+
+        public void Reset() {
+          InitializeCaches();
+        }
+
+        public double[,] GetCorrelation(IDependencyCalculator calc, string partition) {
+          double[,] corr;
+          var key = new Tuple<IDependencyCalculator, string>(calc, partition);
+          correlationsCache.TryGetValue(key, out corr);
+          return corr;
+        }
+
+        public void SetCorrelation(IDependencyCalculator calc, string partition, double[,] correlation) {
+          var key = new Tuple<IDependencyCalculator, string>(calc, partition);
+          correlationsCache[key] = correlation;
+        }
+      }
   }
 }