Changeset 1275 for branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Server/DispatcherBase.cs

Timestamp:

03/06/09 14:41:27 (15 years ago)

Author:

gkronber

Message:

Added more result values to be stored into the CEDMA db for each experiment. #419 (Refactor CEDMA plugins)

File:

• branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Server/DispatcherBase.cs (modified) (10 diffs)

branches/CEDMA-Refactoring-Ticket419/HeuristicLab.CEDMA.Server/DispatcherBase.cs

@@ -49 +49 @@
     };
 
+    private static int MaxGenerations {
+      get { return 3; }
+    }
+
+    private static int MaxEvaluatedSolutions {
+      get { return 3000; }
+    }
+
     public DispatcherBase(IStore store) {
       this.store = store;
@@ -70 +78 @@
       DataSet dataSet = new DataSet(store, dataSetEntity);
 
-      int targetVariable = SelectTargetVariable(dataSet, dataSet.Problem.AllowedInputVariables.ToArray());
+      int targetVariable = SelectTargetVariable(dataSet, dataSet.Problem.AllowedTargetVariables.ToArray());
       Algorithm selectedAlgorithm = SelectAlgorithm(dataSet, targetVariable, possibleAlgorithms[dataSet.Problem.LearningTask]);
       string targetVariableName = dataSet.Problem.GetVariableName(targetVariable);
@@ -92 +100 @@
         case Algorithm.StandardGpRegression: {
             var algo = new HeuristicLab.GP.StructureIdentification.StandardGP();
-            SetGpParameters(algo, complexity);
-            SetProblemParameters(algo, problem, targetVariable);
-            algo.MaxGenerations = 2;
+            SetComplexityParameters(algo, complexity);
+            SetProblemParameters(algo, problem, targetVariable);
+            algo.PopulationSize = 10000;
+            algo.MaxGenerations = MaxGenerations;
             Execution exec = new Execution(algo.Engine);
             exec.Description = "StandardGP - Complexity: " + complexity;
@@ -101 +110 @@
         case Algorithm.OffspringGpRegression: {
             var algo = new HeuristicLab.GP.StructureIdentification.OffspringSelectionGP();
-            SetGpParameters(algo, complexity);
-            SetProblemParameters(algo, problem, targetVariable);
-            algo.MaxEvaluatedSolutions = 10000;
+            SetComplexityParameters(algo, complexity);
+            SetProblemParameters(algo, problem, targetVariable);
+            algo.MaxEvaluatedSolutions = MaxEvaluatedSolutions;
             Execution exec = new Execution(algo.Engine);
             exec.Description = "OffspringGP - Complexity: " + complexity;
@@ -110 +119 @@
         case Algorithm.StandardGpClassification: {
             var algo = new HeuristicLab.GP.StructureIdentification.Classification.StandardGP();
-            SetGpParameters(algo, complexity);
-            SetProblemParameters(algo, problem, targetVariable);
-            algo.MaxGenerations = 2;
+            SetComplexityParameters(algo, complexity);
+            SetProblemParameters(algo, problem, targetVariable);
+            algo.PopulationSize = 10000;
+            algo.MaxGenerations = MaxGenerations;
             Execution exec = new Execution(algo.Engine);
             exec.Description = "StandardGP - Complexity: " + complexity;
@@ -119 +129 @@
         case Algorithm.OffspringGpClassification: {
             var algo = new HeuristicLab.GP.StructureIdentification.Classification.OffspringSelectionGP();
-            SetGpParameters(algo, complexity);
-            SetProblemParameters(algo, problem, targetVariable);
-            algo.MaxEvaluatedSolutions = 10000;
+            SetComplexityParameters(algo, complexity);
+            SetProblemParameters(algo, problem, targetVariable);
+            algo.MaxEvaluatedSolutions = MaxEvaluatedSolutions;
             Execution exec = new Execution(algo.Engine);
             exec.Description = "OffspringGP - Complexity: " + complexity;
@@ -128 +138 @@
         case Algorithm.StandardGpForecasting: {
             var algo = new HeuristicLab.GP.StructureIdentification.TimeSeries.StandardGP();
-            SetGpParameters(algo, complexity);
-            SetProblemParameters(algo, problem, targetVariable);
-            algo.MaxGenerations = 2;
+            SetComplexityParameters(algo, complexity);
+            SetProblemParameters(algo, problem, targetVariable);
+            algo.PopulationSize = 10000;
+            algo.MaxGenerations = MaxGenerations;
             Execution exec = new Execution(algo.Engine);
             exec.Description = "StandardGP - Complexity: " + complexity;
@@ -137 +148 @@
         case Algorithm.OffspringGpForecasting: {
             var algo = new HeuristicLab.GP.StructureIdentification.TimeSeries.OffspringSelectionGP();
-            SetGpParameters(algo, complexity);
-            SetProblemParameters(algo, problem, targetVariable);
-            algo.MaxEvaluatedSolutions = 10000;
+            SetComplexityParameters(algo, complexity);
+            SetProblemParameters(algo, problem, targetVariable);
+            algo.MaxEvaluatedSolutions = MaxEvaluatedSolutions;
             Execution exec = new Execution(algo.Engine);
             exec.Description = "OffspringGP - Complexity: " + complexity;
@@ -150 +161 @@
     }
 
-    private void SetGpParameters(AlgorithmBase algo, ModelComplexity complexity) {
-      algo.SetSeedRandomly = true;
-      algo.PopulationSize = 1000;
-      algo.Elites = 1;
+    private void SetComplexityParameters(AlgorithmBase algo, ModelComplexity complexity) {
       switch (complexity) {
         case ModelComplexity.Low: {
@@ -196 +204 @@
 
     private IEnumerable<double> GetDifferentClassValues(HeuristicLab.DataAnalysis.Dataset dataset, int targetVariable) {
-      throw new NotImplementedException();
+      return Enumerable.Range(0, dataset.Rows).Select(x => dataset.GetValue(x, targetVariable)).Distinct();
     }
   }