Changeset 2349 for trunk/sources/HeuristicLab.Modeling

Timestamp:

09/14/09 12:59:50 (15 years ago)

Author:

gkronber

Message:

Created a subclass for time-series prognosis with SVM and created a simple version of TheilInequalityCoefficientEvaluator in HL.Modeling. (#705)

Location:

trunk/sources/HeuristicLab.Modeling/3.2

Files:

• HeuristicLab.Modeling-3.2.csproj (modified) (1 diff)
• SimpleTheilInequalityCoefficientEvaluator.cs (copied) (copied from trunk/sources/HeuristicLab.GP.StructureIdentification.TimeSeries/3.3/TheilInequalityCoefficientEvaluator.cs) (3 diffs)

trunk/sources/HeuristicLab.Modeling/3.2/HeuristicLab.Modeling-3.2.csproj

@@ -85 +85 @@
     <Compile Include="BestSolutionStorer.cs" />
     <Compile Include="ClassificationProblemInjector.cs" />
+    <Compile Include="SimpleTheilInequalityCoefficientEvaluator.cs" />
     <Compile Include="SolutionExtractor.cs" />
     <Compile Include="PredictorBase.cs" />

trunk/sources/HeuristicLab.Modeling/3.2/SimpleTheilInequalityCoefficientEvaluator.cs

@@ -25 +25 @@
 using HeuristicLab.DataAnalysis;
 
-namespace HeuristicLab.GP.StructureIdentification.TimeSeries {
-  public class TheilInequalityCoefficientEvaluator : GPEvaluatorBase {
+namespace HeuristicLab.Modeling {
+  public class SimpleTheilInequalityCoefficientEvaluator : SimpleEvaluatorBase {
     public override string Description {
       get {
-        return @"Evaluates 'FunctionTree' for all samples of 'Dataset' and calculates
-the 'Theil inequality coefficient (Theil's U2 not U1!)' of estimated values vs. real values of 'TargetVariable'.
+        return @"Calculates the Theil inequality coefficient (Theil's U2 not U1!) of estimated values vs. real values of 'TargetVariable'.
 
 U2 = Sqrt(1/N * Sum(P_t - A_t)^2 ) / Sqrt(1/N * Sum(A_t)^2 ) 
@@ -42 +41 @@
     }
 
-    public TheilInequalityCoefficientEvaluator()
-      : base() {
-      AddVariableInfo(new VariableInfo("TheilInequalityCoefficient", "Theil's inequality coefficient (U2) of the model", typeof(DoubleData), VariableKind.New));
-      AddVariableInfo(new VariableInfo("TheilInequalityCoefficientBias", "Bias proportion of Theil's inequality coefficient", typeof(DoubleData), VariableKind.New));
-      AddVariableInfo(new VariableInfo("TheilInequalityCoefficientVariance", "Variance proportion of Theil's inequality coefficient", typeof(DoubleData), VariableKind.New));
-      AddVariableInfo(new VariableInfo("TheilInequalityCoefficientCovariance", "Covariance proportion of Theil's inequality coefficient", typeof(DoubleData), VariableKind.New));
+    public override string OutputVariableName {
+      get {
+        return "TheilInequalityCoefficient";
+      }
     }
 
-    public override void Evaluate(IScope scope, ITreeEvaluator evaluator, Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
-      #region create result variables
-      DoubleData theilInequaliy = GetVariableValue<DoubleData>("TheilInequalityCoefficient", scope, false, false);
-      if (theilInequaliy == null) {
-        theilInequaliy = new DoubleData();
-        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("TheilInequalityCoefficient"), theilInequaliy));
-      }
-      DoubleData uBias = GetVariableValue<DoubleData>("TheilInequalityCoefficientBias", scope, false, false);
-      if (uBias == null) {
-        uBias = new DoubleData();
-        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("TheilInequalityCoefficientBias"), uBias));
-      }
-      DoubleData uVariance = GetVariableValue<DoubleData>("TheilInequalityCoefficientVariance", scope, false, false);
-      if (uVariance == null) {
-        uVariance = new DoubleData();
-        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("TheilInequalityCoefficientVariance"), uVariance));
-      }
-      DoubleData uCovariance = GetVariableValue<DoubleData>("TheilInequalityCoefficientCovariance", scope, false, false);
-      if (uCovariance == null) {
-        uCovariance = new DoubleData();
-        scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("TheilInequalityCoefficientCovariance"), uCovariance));
-      }
-      #endregion
+    public override double Evaluate(double[,] values) {
+      return Calculate(values);
+    }
 
+    public static double Calculate(double[,] values) {
+      int n = values.GetLength(0);
       double errorsSquaredSum = 0.0;
       double originalSquaredSum = 0.0;
-      double[] estimatedChanges = new double[end - start];
-      double[] originalChanges = new double[end - start];
       int nSamples = 0;
-      for (int sample = start; sample < end; sample++) {
-        double prevValue = dataset.GetValue(sample - 1, targetVariable);
-        double estimatedChange = evaluator.Evaluate(sample) - prevValue;
-        double originalChange = dataset.GetValue(sample, targetVariable) - prevValue;
-        if (updateTargetValues) {
-          dataset.SetValue(sample, targetVariable, estimatedChange + prevValue);
-        }
+      for (int sample = 1; sample < n; sample++) {
+        double prevValue = values[sample - 1, 1];
+        double estimatedChange = values[sample, 0] - prevValue;
+        double originalChange = values[sample, 1] - prevValue;
         if (!double.IsNaN(originalChange) && !double.IsInfinity(originalChange)) {
           double error = estimatedChange - originalChange;
           errorsSquaredSum += error * error;
           originalSquaredSum += originalChange * originalChange;
-          estimatedChanges[sample - start] = estimatedChange;
-          originalChanges[sample - start] = originalChange;
           nSamples++;
         }
@@ -98 +70 @@
       if (double.IsNaN(quality) || double.IsInfinity(quality))
         quality = double.MaxValue;
-      theilInequaliy.Data = quality; // U2
-
-      // decomposition into U_bias + U_variance + U_covariance parts
-      double bias = Statistics.Mean(estimatedChanges) - Statistics.Mean(originalChanges);
-      bias *= bias; // squared
-      uBias.Data = bias / (errorsSquaredSum / nSamples);
-
-      double variance = Statistics.StandardDeviation(estimatedChanges) - Statistics.StandardDeviation(originalChanges);
-      variance *= variance; // squared
-      uVariance.Data = variance / (errorsSquaredSum / nSamples);
-
-      // all parts add up to one so I don't have to calculate the correlation coefficient for the covariance proportion
-      uCovariance.Data = 1.0 - uBias.Data - uVariance.Data;
+      return quality;
     }
   }