Changeset 17660

Timestamp:

07/09/20 12:57:32 (4 years ago)

Author:

chaider

Message:

#3076

• Set worst nmse value to 1.0 in SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator
• Added SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator which allows to use linear scaling within IA

Location:

branches/3076_IA_evaluators_analyzers/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4

Files:

• HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj (modified) (1 diff)
• SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator.cs (modified) (1 diff)
• SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator.cs (copied) (copied from branches/3076_IA_evaluators_analyzers/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator.cs) (3 diffs)

branches/3076_IA_evaluators_analyzers/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj

@@ -178 +178 @@
     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionMeanRelativeErrorEvaluator.cs" />
     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveConstraintConstOptEvaluator.cs" />
+    <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator.cs" />
     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator.cs" />
     <Compile Include="SingleObjective\Evaluators\SymbolicRegressionSingleObjectiveConstraintPearsonRSquaredEvaluator.cs" />

branches/3076_IA_evaluators_analyzers/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator.cs

@@ -99 +99 @@
 
       if (!SymbolicRegressionConstraintAnalyzer.ConstraintsSatisfied(constraints, variableRanges, solution))
-        return double.MaxValue;
+        return 1.0;
 
       var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
       var nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out var errorState);
-      if (errorState != OnlineCalculatorError.None) nmse = double.NaN;
+      if (errorState != OnlineCalculatorError.None) nmse = 1.0;
 
       return nmse;

branches/3076_IA_evaluators_analyzers/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator.cs

@@ -9 +9 @@
 
 namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression.SingleObjective.Evaluators {
-  [Item("Constraint ConstOpt NMSE Evaluator", "")]
-  [StorableType("DED36D85-A4BA-4019-B12D-C523F7327899")]
-  public class SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator
+  [Item("Constraint Scaling NMSE Evaluator", "")]
+  [StorableType("5B69083F-74EE-446C-A7D1-9DEAAA128AC6")]
+  public class SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator
     : SymbolicRegressionSingleObjectiveEvaluator {
-    private const string UseConstOptParameterName = "Use ConstOpt";
-    private const string ConstOptIterationsParameterName = "ConstOpt Iterations";
-
-    public IFixedValueParameter<BoolValue> UseConstOptParamerter =>
-      (IFixedValueParameter<BoolValue>) Parameters[UseConstOptParameterName];
-
-    public IFixedValueParameter<IntValue> ConstOpterIterationsParameter =>
-      (IFixedValueParameter<IntValue>) Parameters[ConstOptIterationsParameterName];
-
-    public bool UseConstOpt {
-      get => UseConstOptParamerter.Value.Value;
-      set => UseConstOptParamerter.Value.Value = value;
-    }
-
-    public int ConstOptIterations {
-      get => ConstOpterIterationsParameter.Value.Value;
-      set => ConstOpterIterationsParameter.Value.Value = value;
-    }
 
     [StorableConstructor]
-    protected SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator(StorableConstructorFlag _) : base(_) {}
+    protected SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator(StorableConstructorFlag _) : base(_) {}
 
-    protected SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator(
-      SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator original, Cloner cloner) :
+    protected SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator(
+      SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator original, Cloner cloner) :
       base(original, cloner) { }
 
     public override IDeepCloneable Clone(Cloner cloner) {
-      return new SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator(this, cloner);
+      return new SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator(this, cloner);
     }
 
-    public SymbolicRegressionSingleObjectiveConstraintConstOptNmseEvaluator() {
-      Parameters.Add(new FixedValueParameter<BoolValue>(UseConstOptParameterName,
-        "Define whether constOpt is active or not.", new BoolValue(true)));
-      Parameters.Add(new FixedValueParameter<IntValue>(ConstOptIterationsParameterName,
-        "Define how many constOpt iterations should be performed.", new IntValue(10)));
-    }
+    public SymbolicRegressionSingleObjectiveConstraintScalingNmseEvaluator() { }
 
     public override bool Maximization => false;
 
-    [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() {
-      if (!Parameters.ContainsKey(UseConstOptParameterName)) {
-        Parameters.Add(new FixedValueParameter<BoolValue>(UseConstOptParameterName,
-          "Define whether constOpt is active or not.", new BoolValue(true)));
-      }
-
-      if (!Parameters.ContainsKey(ConstOptIterationsParameterName)) {
-        Parameters.Add(new FixedValueParameter<IntValue>(ConstOptIterationsParameterName,
-          "Define how many constOpt iterations should be performed.", new IntValue(10)));
-      }
-    }
 
     public override IOperation InstrumentedApply() {
@@ -71 +36 @@
       var interpreter                               = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
       var estimationLimits                          = EstimationLimitsParameter.ActualValue;
-      var applyLinearScaling                        = false;
-      var constantOptimizationIterations            = ConstOptIterations;
-      var constantOptimizationUpdateVariableWeights = true;
-
-      //Use Const Opt
-      if(UseConstOpt)
-        SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows,
-          applyLinearScaling, constantOptimizationIterations, constantOptimizationUpdateVariableWeights,
-          estimationLimits.Lower, estimationLimits.Upper);
+      var applyLinearScaling                        = false; 
+      
+     //Check for interval arithmetic grammar
+     //remove scaling nodes for linear scaling evaluation
+     var rootNode = new ProgramRootSymbol().CreateTreeNode();
+     var startNode = new StartSymbol().CreateTreeNode();
+     SymbolicExpressionTree newTree = null;
+     foreach (var node in solution.IterateNodesPrefix()) {
+       if (node.Symbol.Name == "Scaling") {
+         for (var i = 0; i < node.SubtreeCount; ++i) {
+           startNode.AddSubtree(node.GetSubtree(i));
+         }
+         rootNode.AddSubtree(startNode);
+         newTree = new SymbolicExpressionTree(rootNode);
+         break;
+       }
+     }
+     //calculate alpha and beta for scaling
+     var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(newTree, problemData.Dataset, rows);
+     var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+     OnlineLinearScalingParameterCalculator.Calculate(estimatedValues, targetValues, out var alpha, out var beta, out var errorState);
+     var a = alpha;
+     var b = beta;
+     //Set alpha and beta to the scaling nodes from ia grammar
+     foreach (var node in solution.IterateNodesPrefix()) {
+       if (node.Symbol.Name == "Offset") {
+         node.RemoveSubtree(1);
+         var alphaNode = new ConstantTreeNode(new Constant()) {Value = alpha};
+         node.AddSubtree(alphaNode);
+       } else if (node.Symbol.Name == "Scaling") {
+         node.RemoveSubtree(1);
+         var betaNode = new ConstantTreeNode(new Constant()) {Value = beta};
+         node.AddSubtree(betaNode);
+       }
+     }
 
       var quality = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows,
@@ -99 +90 @@
 
       if (!SymbolicRegressionConstraintAnalyzer.ConstraintsSatisfied(constraints, variableRanges, solution))
-        return double.MaxValue;
+        return 1.0;
 
       var boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
       var nmse = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out var errorState);
-      if (errorState != OnlineCalculatorError.None) nmse = double.NaN;
+      if (errorState != OnlineCalculatorError.None) nmse = 1.0;
 
       return nmse;