Changeset 3892 for trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers

Timestamp:

06/02/10 19:10:35 (14 years ago)

Author:

gkronber

Message:

Improved code for analyzers for SVR and symbolic regression. #1009

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/BestSymbolicRegressionSolutionAnalyzer.cs (modified) (3 diffs)

trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/BestSymbolicRegressionSolutionAnalyzer.cs

@@ -39 +39 @@
   [Item("BestSymbolicRegressionSolutionAnalyzer", "An operator for analyzing the best solution of symbolic regression problems given in symbolic expression tree encoding.")]
   [StorableClass]
-  public sealed class BestSymbolicRegressionSolutionAnalyzer : SingleSuccessorOperator, ISymbolicRegressionAnalyzer {
+  public sealed class BestSymbolicRegressionSolutionAnalyzer : RegressionSolutionAnalyzer, ISymbolicRegressionAnalyzer {
     private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
     private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
-    private const string ProblemDataParameterName = "ProblemData";
-    private const string QualityParameterName = "Quality";
-    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
-    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
+    private const string BestSolutionInputvariableCountResultName = "Variables used by best solution";
     private const string BestSolutionParameterName = "BestSolution";
-    private const string BestSolutionQualityParameterName = "BestSolutionQuality";
-    private const string ResultsParameterName = "Results";
-    private const string BestSolutionResultName = "Best solution (on validiation set)";
-    private const string BestSolutionInputvariableCountResultName = "Variables used by best solution";
-    private const string BestSolutionTrainingRSquared = "Best solution R² (training)";
-    private const string BestSolutionTestRSquared = "Best solution R² (test)";
-    private const string BestSolutionTrainingMse = "Best solution mean squared error (training)";
-    private const string BestSolutionTestMse = "Best solution mean squared error (test)";
-    private const string BestSolutionTrainingRelativeError = "Best solution average relative error (training)";
-    private const string BestSolutionTestRelativeError = "Best solution average relative error (test)";
 
+    #region parameter properties
     public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
       get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
@@ -64 +52 @@
       get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
     }
-    public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
-      get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
-    }
-    public ScopeTreeLookupParameter<DoubleValue> QualityParameter {
-      get { return (ScopeTreeLookupParameter<DoubleValue>)Parameters[QualityParameterName]; }
-    }
-    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
-    }
-    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
-      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
-    }
     public ILookupParameter<SymbolicRegressionSolution> BestSolutionParameter {
       get { return (ILookupParameter<SymbolicRegressionSolution>)Parameters[BestSolutionParameterName]; }
     }
-    public ILookupParameter<DoubleValue> BestSolutionQualityParameter {
-      get { return (ILookupParameter<DoubleValue>)Parameters[BestSolutionQualityParameterName]; }
+    #endregion
+    #region properties
+    public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
+      get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
     }
-    public ILookupParameter<ResultCollection> ResultsParameter {
-      get { return (ILookupParameter<ResultCollection>)Parameters[ResultsParameterName]; }
+    public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
+      get { return SymbolicExpressionTreeParameter.ActualValue; }
     }
+    #endregion
 
     public BestSymbolicRegressionSolutionAnalyzer()
@@ -90 +69 @@
       Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
       Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
-      Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data for which the symbolic expression tree is a solution."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper estimation limit that was set for the evaluation of the symbolic expression trees."));
-      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower estimation limit that was set for the evaluation of the symbolic expression trees."));
-      Parameters.Add(new ScopeTreeLookupParameter<DoubleValue>(QualityParameterName, "The qualities of the symbolic regression trees which should be analyzed."));
       Parameters.Add(new LookupParameter<SymbolicRegressionSolution>(BestSolutionParameterName, "The best symbolic regression solution."));
-      Parameters.Add(new LookupParameter<DoubleValue>(BestSolutionQualityParameterName, "The quality of the best symbolic regression solution."));
-      Parameters.Add(new LookupParameter<ResultCollection>(ResultsParameterName, "The result collection where the best symbolic regression solution should be stored."));
     }
 
-    public override IOperation Apply() {
-      ItemArray<DoubleValue> qualities = QualityParameter.ActualValue;
-      ResultCollection results = ResultsParameter.ActualValue;
-      ISymbolicExpressionTreeInterpreter interpreter = SymbolicExpressionTreeInterpreterParameter.ActualValue;
-      ItemArray<SymbolicExpressionTree> expressions = SymbolicExpressionTreeParameter.ActualValue;
-      DataAnalysisProblemData problemData = ProblemDataParameter.ActualValue;
-      DoubleValue upperEstimationLimit = UpperEstimationLimitParameter.ActualValue;
-      DoubleValue lowerEstimationLimit = LowerEstimationLimitParameter.ActualValue;
+    protected override DataAnalysisSolution UpdateBestSolution() {
+      double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
+      double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
 
-      int i = qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index;
+      int i = Quality.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index;
 
-      SymbolicRegressionSolution solution = BestSolutionParameter.ActualValue;
-      if (solution == null) {
-        var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)interpreter.Clone(), expressions[i], GetInputVariables(expressions[i]));
-        solution = new SymbolicRegressionSolution(problemData, model, lowerEstimationLimit.Value, upperEstimationLimit.Value);
+      if (BestSolutionQualityParameter.ActualValue == null || BestSolutionQualityParameter.ActualValue.Value > Quality[i].Value) {
+        var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(),
+          SymbolicExpressionTree[i],
+          GetInputVariables(SymbolicExpressionTree[i]));
+        var solution = new SymbolicRegressionSolution(ProblemData, model, lowerEstimationLimit, upperEstimationLimit);
+
         BestSolutionParameter.ActualValue = solution;
-        BestSolutionQualityParameter.ActualValue = qualities[i];
-        results.Add(new Result(BestSolutionResultName, solution));
-        results.Add(new Result(BestSolutionInputvariableCountResultName, new IntValue(model.InputVariables.Count())));
-        #region calculate R2,MSE,Rel Error
-        double[] trainingValues = problemData.Dataset.GetVariableValues(
-          problemData.TargetVariable.Value,
-          problemData.TrainingSamplesStart.Value,
-          problemData.TrainingSamplesEnd.Value);
-        double[] testValues = problemData.Dataset.GetVariableValues(
-          problemData.TargetVariable.Value,
-          problemData.TestSamplesStart.Value,
-          problemData.TestSamplesEnd.Value);
-        double trainingR2 = SimpleRSquaredEvaluator.Calculate(trainingValues, solution.EstimatedTrainingValues);
-        double testR2 = SimpleRSquaredEvaluator.Calculate(testValues, solution.EstimatedTestValues);
-        double trainingMse = SimpleMSEEvaluator.Calculate(trainingValues, solution.EstimatedTrainingValues);
-        double testMse = SimpleMSEEvaluator.Calculate(testValues, solution.EstimatedTestValues);
-        double trainingRelError = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(trainingValues, solution.EstimatedTrainingValues);
-        double testRelError = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(testValues, solution.EstimatedTestValues);
-        results.Add(new Result(BestSolutionTrainingRSquared, new DoubleValue(trainingR2)));
-        results.Add(new Result(BestSolutionTestRSquared, new DoubleValue(testR2)));
-        results.Add(new Result(BestSolutionTrainingMse, new DoubleValue(trainingMse)));
-        results.Add(new Result(BestSolutionTestMse, new DoubleValue(testMse)));
-        results.Add(new Result(BestSolutionTrainingRelativeError, new DoubleValue(trainingRelError)));
-        results.Add(new Result(BestSolutionTestRelativeError, new DoubleValue(testRelError)));
-        #endregion
-      } else {
-        if (BestSolutionQualityParameter.ActualValue.Value > qualities[i].Value) {
-          var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)interpreter.Clone(), expressions[i], GetInputVariables(expressions[i]));
-          solution = new SymbolicRegressionSolution(problemData, model, lowerEstimationLimit.Value, upperEstimationLimit.Value);
-          BestSolutionParameter.ActualValue = solution;
-          BestSolutionQualityParameter.ActualValue = qualities[i];
-          results[BestSolutionResultName].Value = solution;
-          results[BestSolutionInputvariableCountResultName].Value = new IntValue(model.InputVariables.Count());
-          #region update R2,MSE, Rel Error
-          double[] trainingValues = problemData.Dataset.GetVariableValues(
-            problemData.TargetVariable.Value,
-            problemData.TrainingSamplesStart.Value,
-            problemData.TrainingSamplesEnd.Value);
-          double[] testValues = problemData.Dataset.GetVariableValues(
-            problemData.TargetVariable.Value,
-            problemData.TestSamplesStart.Value,
-            problemData.TestSamplesEnd.Value);
-          double trainingR2 = SimpleRSquaredEvaluator.Calculate(trainingValues, solution.EstimatedTrainingValues);
-          double testR2 = SimpleRSquaredEvaluator.Calculate(testValues, solution.EstimatedTestValues);
-          double trainingMse = SimpleMSEEvaluator.Calculate(trainingValues, solution.EstimatedTrainingValues);
-          double testMse = SimpleMSEEvaluator.Calculate(testValues, solution.EstimatedTestValues);
-          double trainingRelError = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(trainingValues, solution.EstimatedTrainingValues);
-          double testRelError = SimpleMeanAbsolutePercentageErrorEvaluator.Calculate(testValues, solution.EstimatedTestValues);
-          results[BestSolutionTrainingRSquared].Value = new DoubleValue(trainingR2);
-          results[BestSolutionTestRSquared].Value = new DoubleValue(testR2);
-          results[BestSolutionTrainingMse].Value = new DoubleValue(trainingMse);
-          results[BestSolutionTestMse].Value = new DoubleValue(testMse);
-          results[BestSolutionTrainingRelativeError].Value = new DoubleValue(trainingRelError);
-          results[BestSolutionTestRelativeError].Value = new DoubleValue(testRelError);
-          #endregion
+        BestSolutionQualityParameter.ActualValue = Quality[i];
+
+        if (Results.ContainsKey(BestSolutionInputvariableCountResultName)) {
+          Results[BestSolutionInputvariableCountResultName].Value = new IntValue(model.InputVariables.Count());
+        } else {
+          Results.Add(new Result(BestSolutionInputvariableCountResultName, new IntValue(model.InputVariables.Count())));
         }
       }
-
-      return base.Apply();
+      return BestSolutionParameter.ActualValue;
     }