Changeset 6017 for branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs

Timestamp:

04/17/11 11:03:48 (13 years ago)

Author:

cneumuel

Message:

#1215

• fixed import of existing algorithm
• moved operators in subfolders
• extended tests for SymbolicExpressionGrammar

File:

• branches/HeuristicLab.MetaOptimization/HeuristicLab.MetaOptimization.Test/Program.cs (modified) (15 diffs)

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

@@ -15 +15 @@
 using HeuristicLab.Data;
 using HeuristicLab.Encodings.RealVectorEncoding;
-using HeuristicLab.Hive.ExperimentManager;
+//using HeuristicLab.Hive.ExperimentManager;
 using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
@@ -30 +30 @@
   class Program {
     static void Main(string[] args) {
-      //PluginLoader.pluginAssemblies.Any();
       PluginManager pm = new PluginManager(Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location));
       pm.DiscoverAndCheckPlugins();
-
       pm.Run(pm.Applications.Where(x => x.Name == "TestApp").SingleOrDefault());
     }
@@ -49 +47 @@
     //private static int baseAlgorithmMaxGenerations = 1000;
 
-    private static int metaAlgorithmPopulationSize = 10;
-    private static int metaAlgorithmMaxGenerations = 15;
+    private static int metaAlgorithmPopulationSize = 20;
+    private static int metaAlgorithmMaxGenerations = 20;
     private static int metaProblemRepetitions = 3;
-    private static int baseAlgorithmMaxGenerations = 10;
+    private static int baseAlgorithmMaxGenerations = 100;
     private static double mutationProbability = 0.10;
 
@@ -82 +80 @@
       MetaOptimizationProblem metaOptimizationProblem = new MetaOptimizationProblem();
       metaOptimizationProblem.Repetitions = new IntValue(metaProblemRepetitions);
-      GeneticAlgorithm metaLevelAlgorithm = GetMetaGA(metaOptimizationProblem);
-      //GeneticAlgorithm metaLevelAlgorithm = GetParallelMetaGA(metaOptimizationProblem);
+      //GeneticAlgorithm metaLevelAlgorithm = GetMetaGA(metaOptimizationProblem);
+      GeneticAlgorithm metaLevelAlgorithm = GetParallelMetaGA(metaOptimizationProblem);
       //GeneticAlgorithm metaLevelAlgorithm = GetHiveParallelMetaGA(metaOptimizationProblem);
 
@@ -95 +93 @@
       //Console.WriteLine("Press enter to start");
       //Console.ReadLine();
-      TestConfiguration(algorithmVc, typeof(GeneticAlgorithm), metaOptimizationProblem.Problems.First());
-
-      Console.WriteLine("Press enter to start");
-      Console.ReadLine();
+      //TestConfiguration(algorithmVc, typeof(GeneticAlgorithm), metaOptimizationProblem.Problems.First());
+
+      //Console.WriteLine("Press enter to start");
+      //Console.ReadLine();
       TestOptimization(metaLevelAlgorithm);
 
@@ -111 +109 @@
       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);
-      }
+      //Console.WriteLine("========== Grammar2: ==========");
+      //PrintGrammar(grammar2);
+
+      var vc1 = new SymbolicExpressionGrammarValueConfiguration(grammar1);
+      ConfigureSymbolicExpressionGrammarVc(vc1);
+      var vc2 = new SymbolicExpressionGrammarValueConfiguration(grammar2);
+      ConfigureSymbolicExpressionGrammarVc(vc2);
+
+      vc1.Mutate(random, new MutateDelegate(ParameterConfigurationManipulator.Mutate), new UniformIntValueManipulator(), new UniformDoubleValueManipulator());
+      vc1.Parameterize(grammar1);
+
+      Console.WriteLine("========== Grammar1 (mutated): ==========");
+      PrintGrammar(grammar1);
+
+      vc1.Cross(random, vc2, new CrossDelegate(ParameterConfigurationCrossover.Cross), new DiscreteIntValueCrossover(), new AverageDoubleValueCrossover());
+      vc1.Parameterize(grammar1);
+
+      Console.WriteLine("========== Grammar1 (crossed): ==========");
+      PrintGrammar(grammar1);
+
+      //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);
+      //}
 
     }
@@ -421 +433 @@
 
       GeneticAlgorithm ga = new GeneticAlgorithm();
-      ga.PopulationSize.Value = 3;
-      ga.MaximumGenerations.Value = 1;
+      ga.PopulationSize.Value = 5;
+      ga.MaximumGenerations.Value = 5;
       ga.Engine = new SequentialEngine.SequentialEngine();
       ga.Problem = new SingleObjectiveTestFunctionProblem();
 
-      MetaOptimizationProblem metaOptimizationProblem = new MetaOptimizationProblem();
-      //metaOptimizationProblem.Repetitions = new IntValue(metaProblemRepetitions);
-      GeneticAlgorithm metaLevelAlgorithm = GetMetaGA(metaOptimizationProblem);
-      ParameterConfigurationTree algorithmVc = SetupGAAlgorithm(typeof(GeneticAlgorithm), metaOptimizationProblem);
-      algorithmVc.Randomize(random);
+      //MetaOptimizationProblem metaOptimizationProblem = new MetaOptimizationProblem();
+      ////metaOptimizationProblem.Repetitions = new IntValue(metaProblemRepetitions);
+      //GeneticAlgorithm metaLevelAlgorithm = GetMetaGA(metaOptimizationProblem);
+      //ParameterConfigurationTree algorithmVc = SetupGAAlgorithm(typeof(GeneticAlgorithm), metaOptimizationProblem);
+      //algorithmVc.Randomize(random);
       Stopwatch sw = new Stopwatch();
 
@@ -449 +461 @@
         algs.Enqueue(clonedGa);
 
-        if (algs.Count > 24)
-          algs.Dequeue();
+        //if (algs.Count > 24)
+        //  algs.Dequeue();
         clonedGa.StartSync();
         sw.Stop();
@@ -610 +622 @@
 
     private static void TestTypeDiscovery() {
-      PluginLoader.pluginAssemblies.Any();
-
       var items = ApplicationManager.Manager.GetInstances(typeof(DoubleArray)).ToArray();
 
@@ -667 +677 @@
     }
 
-    private static GeneticAlgorithm GetHiveParallelMetaGA(MetaOptimizationProblem metaOptimizationProblem) {
-      GeneticAlgorithm metaLevelAlgorithm = GetParallelMetaGA(metaOptimizationProblem);
-      metaLevelAlgorithm.Engine = new HiveEngine.HiveEngine();
-      ServiceLocator.Instance.ClientFacadePool.UserName = "cneumuel";
-      ServiceLocator.Instance.ClientFacadePool.Password = "cneumuel";
-      ServiceLocator.Instance.StreamedClientFacadePool.UserName = "cneumuel";
-      ServiceLocator.Instance.StreamedClientFacadePool.Password = "cneumuel";
-      return metaLevelAlgorithm;
-    }
+    //private static GeneticAlgorithm GetHiveParallelMetaGA(MetaOptimizationProblem metaOptimizationProblem) {
+    //  GeneticAlgorithm metaLevelAlgorithm = GetParallelMetaGA(metaOptimizationProblem);
+    //  metaLevelAlgorithm.Engine = new HiveEngine.HiveEngine();
+    //  ServiceLocator.Instance.ClientFacadePool.UserName = "cneumuel";
+    //  ServiceLocator.Instance.ClientFacadePool.Password = "cneumuel";
+    //  ServiceLocator.Instance.StreamedClientFacadePool.UserName = "cneumuel";
+    //  ServiceLocator.Instance.StreamedClientFacadePool.Password = "cneumuel";
+    //  return metaLevelAlgorithm;
+    //}
 
     private static EvolutionStrategy GetMetaES(MetaOptimizationProblem metaOptimizationProblem) {
@@ -714 +724 @@
 
       //ConfigurePopulationSize(algorithmVc, 15, 20, 1);
-      ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
-      ConfigureMutationOperator(algorithmVc);
+      //ConfigureMutationRate(algorithmVc, 0.0, 1.0, 0.01);
+      //ConfigureMutationOperator(algorithmVc);
       //ConfigureElites(algorithmVc, 0, 8, 1);
       //ConfigureSelectionOperator(algorithmVc, true);
@@ -736 +746 @@
         }
       }
+
+      ConfigureSymbolicExpressionGrammarVc(symbolicExpressionGrammarVc);
+    }
+
+    private static void ConfigureSymbolicExpressionGrammarVc(SymbolicExpressionGrammarValueConfiguration symbolicExpressionGrammarVc) {
       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;
+      foreach (var pc in symbolicExpressionGrammarVc.ParameterConfigurations) {
+        if (pc.Name != "Constant"
+          && pc.Name != "Variable" 
+          && pc.Name != "ProgramRootSymbol"
+          && pc.Name != "StartSymbol") {
+          pc.Optimize = true;
+          var initialFrequencyVc = ((ParameterizedValueConfiguration)pc.ValueConfigurations.First()).ParameterConfigurations.Single(x => x.Name == "InitialFrequency");
+          initialFrequencyVc.Optimize = true;
+        }
+      }
+      //var additionPc = symbolicExpressionGrammarVc.ParameterConfigurations.Single(x => x.Name == "Addition");
+      //additionPc.Optimize = true;
     }
 
@@ -896 +918 @@
       string resultPath = Path.Combine(path, string.Format("{0} - Result.hl", id));
       string outputPath = Path.Combine(path, string.Format("{0} - Console.txt", id));
+
+      ContentManager.Save((IStorableContent)metaLevelAlgorithm, resultPath+"-before.hl", true);
 
       using (var sw = new StreamWriter(outputPath)) {
@@ -946 +970 @@
                 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;
@@ -959 +983 @@
                     ,((DoubleArray)run.Results["RunsAverageQualities"]).ToString()
                     //,((IntValue)run.Parameters["PopulationSize"]).Value.ToString()
-                    ,((DoubleValue)run.Parameters["MutationProbability"]).Value.ToString("0.0000")
+                    //,((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)
+                    //,Shorten(run.Parameters.ContainsKey("Mutator") ? run.Parameters["Mutator"].ToString() : "null", 40)
                     //,((ISelector)run.Parameters["Selector"]).NumberOfSelectedSubScopesParameter.Value.ToString()
                     );