Changeset 2034 for trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators

Timestamp:

06/09/09 14:34:56 (16 years ago)

Author:

gkronber

Message:

Implemented a first version of an operator to calculate variable impacts of models (generated by GP or SVM). #644 (Variable impact of CEDMA models should be calculated and stored in the result DB)

Location:

trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators

Files:

• EarlyStoppingMeanSquaredErrorEvaluator.cs (modified) (1 diff)
• GPEvaluatorBase.cs (modified) (2 diffs)
• UncertainMeanSquaredErrorEvaluator.cs (modified) (1 diff)

trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/EarlyStoppingMeanSquaredErrorEvaluator.cs

@@ -45 +45 @@
     // evaluates the function-tree for the given target-variable and the whole dataset and returns the MSE
     public override void Evaluate(IScope scope, ITreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
-      double qualityLimit = GetVariableValue<DoubleData>("QualityLimit", scope, false).Data;
+      double qualityLimit = GetVariableValue<DoubleData>("QualityLimit", scope, true).Data;
       DoubleData mse = GetVariableValue<DoubleData>("MSE", scope, false, false);
       if (mse == null) {

trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/GPEvaluatorBase.cs

@@ -40 +40 @@
       AddVariableInfo(new VariableInfo("PunishmentFactor", "Punishment factor for invalid estimations", typeof(DoubleData), VariableKind.In));
       AddVariableInfo(new VariableInfo("TotalEvaluatedNodes", "Number of evaluated nodes", typeof(DoubleData), VariableKind.In | VariableKind.Out));
+      AddVariableInfo(new VariableInfo("TrainingSamplesStart", "Start index of training samples in dataset", typeof(IntData), VariableKind.In));
+      AddVariableInfo(new VariableInfo("TrainingSamplesEnd", "End index of training samples in dataset", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo("SamplesStart", "Start index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo("SamplesEnd", "End index of samples in dataset to evaluate", typeof(IntData), VariableKind.In));
@@ -51 +53 @@
       IFunctionTree functionTree = GetVariableValue<IFunctionTree>("FunctionTree", scope, true);
       double punishmentFactor = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data;
-      int treeSize = scope.GetVariableValue<IntData>("TreeSize", false).Data;
+      int treeSize = scope.GetVariableValue<IntData>("TreeSize", true).Data;
       double totalEvaluatedNodes = scope.GetVariableValue<DoubleData>("TotalEvaluatedNodes", true).Data;
+      int trainingStart = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
+      int trainingEnd = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
       int start = GetVariableValue<IntData>("SamplesStart", scope, true).Data;
       int end = GetVariableValue<IntData>("SamplesEnd", scope, true).Data;
       bool useEstimatedValues = GetVariableValue<BoolData>("UseEstimatedTargetValue", scope, true).Data;
       ITreeEvaluator evaluator = GetVariableValue<ITreeEvaluator>("TreeEvaluator", scope, true);
-      evaluator.PrepareForEvaluation(functionTree);
+      evaluator.PrepareForEvaluation(dataset, targetVariable, trainingStart, trainingEnd, punishmentFactor, functionTree);
 
       double[] backupValues = null;

trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/Evaluators/UncertainMeanSquaredErrorEvaluator.cs

@@ -51 +51 @@
     // evaluates the function-tree for the given target-variable and the whole dataset and returns the MSE
     public override void Evaluate(IScope scope, ITreeEvaluator evaluator, HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable, int start, int end, bool updateTargetValues) {
-      double qualityLimit = GetVariableValue<DoubleData>("QualityLimit", scope, false).Data;
+      double qualityLimit = GetVariableValue<DoubleData>("QualityLimit", scope, true).Data;
       int minSamples = GetVariableValue<IntData>("MinEvaluatedSamples", scope, true).Data;
       MersenneTwister mt = GetVariableValue<MersenneTwister>("Random", scope, true);