Changeset 2328 for trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/PredictorBuilder.cs

Timestamp:

09/03/09 15:00:23 (15 years ago)

Author:

gkronber

Message:

this is the remaining part of changeset r2327.
Applied changes in modeling plugins that are necessary for the new model analyzer (#722)

• predictor has properties for the lower and upper limit of the predicted value
• added views for predictors that show the limits (also added a new view for GeneticProgrammingModel that shows the size and height of the model)
• Reintroduced TreeEvaluatorInjectors that read a PunishmentFactor and calculate the lower and upper limits for estimated values (limits are set in the tree evaluators)
• Added operators to create Predictors. Changed modeling algorithms to use the predictors for the calculation of final model qualities and variable impacts (to be compatible with the new model analyzer the predictors use a very large PunishmentFactor)
• replaced all private implementations of double.IsAlmost and use HL.Commons instead (see #733 r2324)
• Implemented operator SolutionExtractor and moved BestSolutionStorer from HL.Logging to HL.Modeling (fixes #734)

File:

• trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/PredictorBuilder.cs (modified) (3 diffs)

trunk/sources/HeuristicLab.GP.StructureIdentification/3.3/PredictorBuilder.cs

@@ -28 +28 @@
 using HeuristicLab.GP.Interfaces;
 using HeuristicLab.Modeling;
+using HeuristicLab.DataAnalysis;
 
 namespace HeuristicLab.GP.StructureIdentification {
@@ -35 +36 @@
       AddVariableInfo(new VariableInfo("FunctionTree", "The function tree", typeof(IGeneticProgrammingModel), VariableKind.In));
       AddVariableInfo(new VariableInfo("TreeEvaluator", "The tree evaluator used to evaluate the model", typeof(ITreeEvaluator), VariableKind.In));
+      AddVariableInfo(new VariableInfo("PunishmentFactor", "The punishment factor limits the estimated values to a certain range", typeof(DoubleData), VariableKind.In));
+      AddVariableInfo(new VariableInfo("Dataset", "The dataset", typeof(Dataset), VariableKind.In));
+      AddVariableInfo(new VariableInfo("TrainingSamplesStart", "Start index of training set", typeof(DoubleData), VariableKind.In));
+      AddVariableInfo(new VariableInfo("TrainingSamplesEnd", "End index of training set", typeof(DoubleData), VariableKind.In));
+      AddVariableInfo(new VariableInfo("TargetVariable", "Index of the target variable", typeof(IntData), VariableKind.In));
       AddVariableInfo(new VariableInfo("Predictor", "The predictor combines the function tree and the evaluator and can be used to generate estimated values", typeof(IPredictor), VariableKind.New));
     }
@@ -44 +50 @@
     public override IOperation Apply(IScope scope) {
       IGeneticProgrammingModel model = GetVariableValue<IGeneticProgrammingModel>("FunctionTree", scope, true);
-      ITreeEvaluator evaluator = GetVariableValue<ITreeEvaluator>("TreeEvaluator", scope, true);
-      scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Predictor"), new Predictor(evaluator, model)));
+      ITreeEvaluator evaluator = (ITreeEvaluator)GetVariableValue<ITreeEvaluator>("TreeEvaluator", scope, true).Clone();
+      double punishmentFactor = GetVariableValue<DoubleData>("PunishmentFactor", scope, true).Data;
+      Dataset dataset = GetVariableValue<Dataset>("Dataset", scope, true);
+      int start = GetVariableValue<IntData>("TrainingSamplesStart", scope, true).Data;
+      int end = GetVariableValue<IntData>("TrainingSamplesEnd", scope, true).Data;
+      int targetVariable = GetVariableValue<IntData>("TargetVariable", scope, true).Data;
+      double mean = dataset.GetMean(targetVariable, start, end);
+      double range = dataset.GetRange(targetVariable, start, end);
+      double minEstimatedValue = mean - punishmentFactor * range;
+      double maxEstimatedValue = mean + punishmentFactor * range;
+      scope.AddVariable(new HeuristicLab.Core.Variable(scope.TranslateName("Predictor"), new Predictor(evaluator, model, minEstimatedValue, maxEstimatedValue)));
       return null;
     }