Changeset 5927 for branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs

Timestamp:

04/01/11 17:15:41 (13 years ago)

Author:

cneumuel

Message:

#1215

• worked on configurability of SymbolicExpressionGrammar

File:

• branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs (modified) (13 diffs)

branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs

@@ -14 +14 @@
 using HeuristicLab.Core;
 using HeuristicLab.Data;
+using HeuristicLab.Encodings.RealVectorEncoding;
 using HeuristicLab.Hive.ExperimentManager;
 using HeuristicLab.Optimization;
@@ -19 +20 @@
 using HeuristicLab.PluginInfrastructure;
 using HeuristicLab.PluginInfrastructure.Manager;
+using HeuristicLab.Problems.DataAnalysis.Symbolic;
+using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
 using HeuristicLab.Problems.MetaOptimization;
 using HeuristicLab.Problems.TestFunctions;
@@ -46 +49 @@
     //private static int baseAlgorithmMaxGenerations = 1000;
 
-    private static int metaAlgorithmPopulationSize = 5;
+    private static int metaAlgorithmPopulationSize = 10;
     private static int metaAlgorithmMaxGenerations = 15;
-    private static int metaProblemRepetitions = 2;
-    private static int baseAlgorithmMaxGenerations = 5;
+    private static int metaProblemRepetitions = 3;
+    private static int baseAlgorithmMaxGenerations = 10;
     private static double mutationProbability = 0.10;
 
@@ -74 +77 @@
       //TestNormalCrossover();
       //TestItemDictionary();
-      
+
+      //TestSymbolicDataAnalysisGrammar(); return;
+
       MetaOptimizationProblem metaOptimizationProblem = new MetaOptimizationProblem();
       metaOptimizationProblem.Repetitions = new IntValue(metaProblemRepetitions);
@@ -90 +95 @@
       //Console.WriteLine("Press enter to start");
       //Console.ReadLine();
-      TestConfiguration(algorithmVc, new GeneticAlgorithm() { Problem = new SingleObjectiveTestFunctionProblem() });
+      TestConfiguration(algorithmVc, typeof(GeneticAlgorithm), metaOptimizationProblem.Problems.First());
 
       Console.WriteLine("Press enter to start");
@@ -99 +104 @@
 
       Console.ReadLine();
+    }
+
+    private void TestSymbolicDataAnalysisGrammar() {
+      var random = new MersenneTwister();
+
+      var grammar1 = new TypeCoherentExpressionGrammar();
+      var grammar2 = new TypeCoherentExpressionGrammar();
+
+      grammar2.Symbols.Single(x => x.Name == "Addition").InitialFrequency = 0.5;
+
+      Console.WriteLine("========== Grammar1: ==========");
+      PrintGrammar(grammar1);
+      Console.WriteLine("========== Grammar2: ==========");
+      PrintGrammar(grammar2);
+
+
+      RealVector v1 = GetInitialFrequenciesAsRealVector(grammar1);
+      RealVector v2 = GetInitialFrequenciesAsRealVector(grammar2);
+
+      for (int i = 0; i < 10; i++) {
+        RealVector v3 = DiscreteCrossover.Apply(random, new ItemArray<RealVector>(new List<RealVector> { v1, v2 }));
+
+        var grammar3 = new TypeCoherentExpressionGrammar();
+        SetInitialFrequenciesFromRealVector(grammar3, v3);
+
+        Console.WriteLine("\n========== Crossed: ==========");
+        PrintGrammar(grammar3);
+      }
+
+    }
+
+    private static void PrintGrammar(TypeCoherentExpressionGrammar grammar) {
+      foreach (var symbol in grammar.Symbols) {
+        Console.WriteLine("{0} ({1})", symbol.ToString(), symbol.InitialFrequency);
+      }
+    }
+
+    private static RealVector GetInitialFrequenciesAsRealVector(TypeCoherentExpressionGrammar grammar) {
+      var vector = new RealVector(grammar.Symbols.Count());
+      for (int i = 0; i < grammar.Symbols.Count(); i++) {
+        vector[i] = grammar.Symbols.ElementAt(i).InitialFrequency;
+      }
+      return vector;
+    }
+
+    private static void SetInitialFrequenciesFromRealVector(TypeCoherentExpressionGrammar grammar, RealVector vector) {
+      for (int i = 0; i < grammar.Symbols.Count(); i++) {
+        grammar.Symbols.ElementAt(i).InitialFrequency = vector[i];
+      }
     }
 
@@ -384 +438 @@
       // because creating ThreadLocal<T> takes all the runtime.
       // when the algs are not stored in a list however this effect does not occur.
-      
+
 
       for (int i = 0; i < 1000; i++) {
@@ -557 +611 @@
     private static void TestTypeDiscovery() {
       PluginLoader.pluginAssemblies.Any();
-      
+
       var items = ApplicationManager.Manager.GetInstances(typeof(DoubleArray)).ToArray();
 
@@ -638 +692 @@
     private static ParameterConfigurationTree SetupGAAlgorithm(Type baseLevelAlgorithmType, MetaOptimizationProblem metaOptimizationProblem) {
       metaOptimizationProblem.AlgorithmType.Value = baseLevelAlgorithmType;
-      metaOptimizationProblem.ProblemType.Value = typeof(SingleObjectiveTestFunctionProblem);
-      ParameterConfigurationTree algorithmVc = metaOptimizationProblem.ParameterConfigurationTree;
-      ((IntValue)algorithmVc.AlgorithmConfiguration.ParameterConfigurations.Single(x => x.Name == "MaximumGenerations").ActualValue.Value).Value = baseAlgorithmMaxGenerations;
-
-      metaOptimizationProblem.Problems.Clear();
-      metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
-        Evaluator = new GriewankEvaluator(),
-        ProblemSize = new IntValue(2)
-      });
+      //metaOptimizationProblem.Problems.Clear();
+
+      //metaOptimizationProblem.ProblemType.Value = typeof(SingleObjectiveTestFunctionProblem);
+      //metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
+      //  Evaluator = new GriewankEvaluator(),
+      //  ProblemSize = new IntValue(2)
+      //});
       //metaOptimizationProblem.Problems.Add(new HeuristicLab.Problems.TestFunctions.SingleObjectiveTestFunctionProblem() {
       //  Evaluator = new GriewankEvaluator(),
@@ -656 +708 @@
       //});
 
-      ConfigurePopulationSize(algorithmVc, 15, 20, 1);
+      metaOptimizationProblem.ProblemType.Value = typeof(SymbolicRegressionSingleObjectiveProblem);
+      
+      ParameterConfigurationTree algorithmVc = metaOptimizationProblem.ParameterConfigurationTree;
+      ((IntValue)algorithmVc.AlgorithmConfiguration.ParameterConfigurations.Single(x => x.Name == "MaximumGenerations").ActualValue.Value).Value = baseAlgorithmMaxGenerations;
+
+      //ConfigurePopulationSize(algorithmVc, 15, 20, 1);
       ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
-      //ConfigureMutationOperator(algorithmVc);
+      ConfigureMutationOperator(algorithmVc);
       //ConfigureElites(algorithmVc, 0, 8, 1);
       //ConfigureSelectionOperator(algorithmVc, true);
+
+      ConfigureSymbolicExpressionGrammar(algorithmVc);
+
       return algorithmVc;
     }
 
-    private static void TestConfiguration(ParameterConfigurationTree algorithmVc, GeneticAlgorithm baseLevelAlgorithm) {
+    private static void ConfigureSymbolicExpressionGrammar(ParameterConfigurationTree vc) {
+      var pc = vc.ProblemConfiguration.ParameterConfigurations.Single(x => x.Name == "SymbolicExpressionTreeGrammar");
+      pc.Optimize = true;
+
+      SymbolicExpressionGrammarValueConfiguration symbolicExpressionGrammarVc = null;
+      foreach (var valconf in pc.ValueConfigurations) {
+        if (valconf.ActualValue.Value.ItemName != "TypeCoherentExpressionGrammar") {
+          pc.ValueConfigurations.SetItemCheckedState(valconf, false);
+        } else {
+          symbolicExpressionGrammarVc = valconf as SymbolicExpressionGrammarValueConfiguration;
+        }
+      }
+      symbolicExpressionGrammarVc.Optimize = true;
+
+      var additionPc = symbolicExpressionGrammarVc.ParameterConfigurations.Single(x => x.Name == "Addition");
+      additionPc.Optimize = true;
+      var initialFrequencyVc = ((ParameterizedValueConfiguration)additionPc.ValueConfigurations.First()).ParameterConfigurations.Single(x => x.Name == "InitialFrequency");
+      initialFrequencyVc.Optimize = true;
+    }
+
+    private static void TestConfiguration(ParameterConfigurationTree algorithmVc, Type baseLevelAlgorithmType, IProblem problem) {
       IRandom rand = new FastRandom(0);
+      var baseLevelAlgorithm = (GeneticAlgorithm)MetaOptimizationUtil.CreateParameterizedAlgorithmInstance(algorithmVc, baseLevelAlgorithmType, problem);
+
       // set random values
       for (int i = 0; i < 10; i++) {
@@ -739 +821 @@
 
       // uncheck multiMutator to avoid Michalewicz issue
-      var multiMutator = mutationOperator.ValueConfigurations.Where(x => x.ActualValue.Value != null && x.ActualValue.Value.ItemName.StartsWith("Multi")).SingleOrDefault();
-      if (multiMutator != null) {
-        mutationOperator.ValueConfigurations.SetItemCheckedState(multiMutator, false);
-      }
+      //var multiMutator = mutationOperator.ValueConfigurations.Where(x => x.ActualValue.Value != null && x.ActualValue.Value.ItemName.StartsWith("Multi")).SingleOrDefault();
+      //if (multiMutator != null) {
+      //  mutationOperator.ValueConfigurations.SetItemCheckedState(multiMutator, false);
+      //}
 
       // add another normal - don't do this with 'new', because ActualNames will not be set correctly. It should be copied from an existing one
@@ -864 +946 @@
                 var orderedRuns = rc.OrderBy(x => x.Results["AverageQualityNormalized"]);
 
-                TableBuilder tb = new TableBuilder("QNorm", "Qualities"/*, "PoSi"/* "MutRa", "Eli", "SelOp", "MutOp", "NrSelSubScopes"*/);
+                TableBuilder tb = new TableBuilder("QNorm", "Qualities"/*, "PoSi"*/ ,"MutRa" /*,"Eli", "SelOp"*/, "MutOp"/*, "NrSelSubScopes"*/);
                 foreach (IRun run in orderedRuns) {
                   //string selector;
@@ -874 +956 @@
 
                   tb.AppendRow(
-                    ((DoubleValue)run.Results["AverageQualityNormalized"]).Value.ToString("#0.0000"),
-                    ((DoubleArray)run.Results["RunsAverageQualities"]).ToString()
-                    //((IntValue)run.Parameters["PopulationSize"]).Value.ToString(),
-                    //((DoubleValue)run.Parameters["MutationProbability"]).Value.ToString("0.0000"),
-                    //((IntValue)run.Parameters["Elites"]).Value.ToString(),
-                    //Shorten(selector, 20),
-                    //Shorten(run.Parameters.ContainsKey("Mutator") ? run.Parameters["Mutator"].ToString() : "null", 40),
-                    //((ISelector)run.Parameters["Selector"]).NumberOfSelectedSubScopesParameter.Value.ToString()
+                    ((DoubleValue)run.Results["AverageQualityNormalized"]).Value.ToString("#0.0000")
+                    ,((DoubleArray)run.Results["RunsAverageQualities"]).ToString()
+                    //,((IntValue)run.Parameters["PopulationSize"]).Value.ToString()
+                    ,((DoubleValue)run.Parameters["MutationProbability"]).Value.ToString("0.0000")
+                    //,((IntValue)run.Parameters["Elites"]).Value.ToString()
+                    //,Shorten(selector, 20)
+                    ,Shorten(run.Parameters.ContainsKey("Mutator") ? run.Parameters["Mutator"].ToString() : "null", 40)
+                    //,((ISelector)run.Parameters["Selector"]).NumberOfSelectedSubScopesParameter.Value.ToString()
                     );
                 }