Changeset 15438 for branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis

Timestamp:

10/28/17 19:56:51 (7 years ago)

Author:

gkronber

Message:

#2796 refactoring to simplify the code

Location:

branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis/3.4

Files:

• HeuristicLab.Algorithms.DataAnalysis.MCTSSymbReg.csproj (modified) (3 diffs)
• Heuristics.cs (modified) (1 diff)
• MctsSymbolicRegression/Automaton.cs (modified) (18 diffs)
• MctsSymbolicRegression/ConstraintHandler.cs (deleted)
• MctsSymbolicRegression/EmptyConstraintHandler.cs (deleted)
• MctsSymbolicRegression/IConstraintHandler.cs (deleted)
• MctsSymbolicRegression/MctsSymbolicRegressionAlgorithm.cs (modified) (5 diffs)
• MctsSymbolicRegression/MctsSymbolicRegressionStatic.cs (modified) (23 diffs)
• MctsSymbolicRegression/Policies (deleted)
• MctsSymbolicRegression/SimpleConstraintHandler.cs (deleted)
• MctsSymbolicRegression/Tree.cs (modified) (1 diff)

branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis.MCTSSymbReg.csproj

@@ -100 +100 @@
     <Compile Include="Heuristics.cs" />
     <Compile Include="MctsSymbolicRegression\ApproximateDoubleEqualityComparer.cs" />
-    <Compile Include="MctsSymbolicRegression\IConstraintHandler.cs" />
     <Compile Include="MctsSymbolicRegression\Automaton.cs" />
     <Compile Include="MctsSymbolicRegression\CodeGenerator.cs" />
@@ -108 +107 @@
     <Compile Include="MctsSymbolicRegression\MctsSymbolicRegressionStatic.cs" />
     <Compile Include="MctsSymbolicRegression\OpCodes.cs" />
-    <Compile Include="MctsSymbolicRegression\Policies\EpsGreedy.cs" />
-    <Compile Include="MctsSymbolicRegression\Policies\IActionStatistics.cs" />
-    <Compile Include="MctsSymbolicRegression\Policies\IPolicy.cs" />
-    <Compile Include="MctsSymbolicRegression\Policies\PolicyBase.cs" />
     <Compile Include="MctsSymbolicRegression\ExprHash.cs" />
-    <Compile Include="MctsSymbolicRegression\EmptyConstraintHandler.cs" />
-    <Compile Include="MctsSymbolicRegression\SimpleConstraintHandler.cs" />
     <Compile Include="MctsSymbolicRegression\SymbolicExpressionGenerator.cs" />
     <Compile Include="MctsSymbolicRegression\Tree.cs" />
@@ -122 +115 @@
   <ItemGroup>
     <None Include="Plugin.cs.frame" />
+  </ItemGroup>
+  <ItemGroup>
+    <Folder Include="MctsSymbolicRegression\Policies\" />
   </ItemGroup>
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />

branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis/3.4/Heuristics.cs

@@ -22 +22 @@
   public static class Heuristics {
     public static double CorrelationForInteraction(double[] a, double[] b, double[] c, double[] target) {
-      return 0.0;
-    }
-    public static double CorrelationForInteraction(double[] a, double[] b, double[] z) {
-      // 
       var am = a.Average();
       var bm = b.Average();
+      var cm = c.Average();
       var p1 = Enumerable.Range(0, a.Length).Where(i => a[i] < am);
       var p2 = Enumerable.Range(0, a.Length).Where(i => a[i] > am);
       var p3 = Enumerable.Range(0, a.Length).Where(i => b[i] < bm);
       var p4 = Enumerable.Range(0, a.Length).Where(i => b[i] > bm);
+      var p5 = Enumerable.Range(0, a.Length).Where(i => c[i] < cm);
+      var p6 = Enumerable.Range(0, a.Length).Where(i => c[i] > cm);
+
+      return 1.0 / (p1.Count() + p2.Count() + p3.Count() + p4.Count() + p5.Count() + p6.Count()) * 
+        (
+        p1.Count() * CorrelationForInteraction(b, c, target, p1) +
+        p2.Count() * CorrelationForInteraction(b, c, target, p2) +
+        p3.Count() * CorrelationForInteraction(a, c, target, p3) +
+        p4.Count() * CorrelationForInteraction(a, c, target, p3) +
+        p5.Count() * CorrelationForInteraction(a, b, target, p5) +
+        p6.Count() * CorrelationForInteraction(a, b, target, p6)
+      );
+    }
+    public static double CorrelationForInteraction(double[] a, double[] b, double[] z) {
+      return CorrelationForInteraction(a, b, z, Enumerable.Range(0, a.Length));
+    }
+    public static double CorrelationForInteraction(double[] a, double[] b, double[] z, IEnumerable<int> idx) {
+      // 
+      var am = a.Average();
+      var bm = b.Average();
+      var p1 = idx.Where(i => a[i] < am);
+      var p2 = idx.Where(i => a[i] > am);
+      var p3 = idx.Where(i => b[i] < bm);
+      var p4 = idx.Where(i => b[i] > bm);
 
       return 1.0 / (p1.Count() + p2.Count() + p3.Count() + p4.Count()) *

branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis/3.4/MctsSymbolicRegression/Automaton.cs

@@ -118 +118 @@
     private List<string>[,] actionStrings; // just for printing 
     private readonly CodeGenerator codeGenerator;
-    private IConstraintHandler constraintHandler;
-
-    public Automaton(double[][] vars, IConstraintHandler constraintHandler,
+    private int numVarRefs;
+    private int maximumNumberOfVariables;
+
+    public Automaton(double[][] vars,
        bool allowProdOfVars = true,
        bool allowExp = true,
        bool allowLog = true,
        bool allowInv = true,
-       bool allowMultipleTerms = false) {
+       bool allowMultipleTerms = false,
+       int maxNumberOfVariables = 5) {
       int nVars = vars.Length;
+      this.maximumNumberOfVariables = maxNumberOfVariables;
       codeGenerator = new CodeGenerator();
-      this.constraintHandler = constraintHandler;
       BuildAutomaton(nVars, allowProdOfVars, allowExp, allowLog, allowInv, allowMultipleTerms);
 
@@ -165 +167 @@
         codeGenerator.Reset();
         codeGenerator.Emit1(OpCodes.LoadConst0);
-        constraintHandler.Reset();
+        numVarRefs = 0;
       }, "0");
       AddTransition(StateTermEnd, StateExprEnd, () => {
@@ -180 +182 @@
         () => {
           codeGenerator.Emit1(OpCodes.LoadParamN);
-          constraintHandler.StartTerm();
         },
         "c");
@@ -186 +187 @@
         () => {
           codeGenerator.Emit1(OpCodes.Mul);
-          constraintHandler.EndTerm();
         },
         "*");
@@ -198 +198 @@
       if (allowProdOfVars)
         AddTransition(StateFactorStart, StateVariableFactorStart, () => {
-          constraintHandler.StartFactor(StateVariableFactorStart);
         }, "");
       if (allowExp)
         AddTransition(StateFactorStart, StateExpFactorStart, () => {
-          constraintHandler.StartFactor(StateExpFactorStart);
         }, "");
       if (allowLog)
         AddTransition(StateFactorStart, StateLogFactorStart, () => {
-          constraintHandler.StartFactor(StateLogFactorStart);
         }, "");
       if (allowInv)
         AddTransition(StateFactorStart, StateInvFactorStart, () => {
-          constraintHandler.StartFactor(StateInvFactorStart);
         }, "");
-      AddTransition(StateVariableFactorEnd, StateFactorEnd, () => { constraintHandler.EndFactor(); }, "");
-      AddTransition(StateExpFactorEnd, StateFactorEnd, () => { constraintHandler.EndFactor(); }, "");
-      AddTransition(StateLogFactorEnd, StateFactorEnd, () => { constraintHandler.EndFactor(); }, "");
-      AddTransition(StateInvFactorEnd, StateFactorEnd, () => { constraintHandler.EndFactor(); }, "");
+      AddTransition(StateVariableFactorEnd, StateFactorEnd);
+      AddTransition(StateExpFactorEnd, StateFactorEnd);
+      AddTransition(StateLogFactorEnd, StateFactorEnd);
+      AddTransition(StateInvFactorEnd, StateFactorEnd);
 
       // VarFact -> var_1 ... var_n
@@ -227 +223 @@
           () => {
             codeGenerator.Emit2(OpCodes.LoadVar, varIdx);
-            constraintHandler.AddVarToCurrentFactor(varState);
+            numVarRefs++;
           },
           "var_" + varIdx + "");
@@ -259 +255 @@
           () => {
             codeGenerator.Emit2(OpCodes.LoadVar, varIdx);
-            constraintHandler.AddVarToCurrentFactor(varState);
+            numVarRefs++;
           },
           "var_" + varIdx + "");
@@ -274 +270 @@
         () => {
           codeGenerator.Emit1(OpCodes.LoadConst0);
-          constraintHandler.StartNewTermInPoly();
         },
         "0");
@@ -314 +309 @@
           () => {
             codeGenerator.Emit2(OpCodes.LoadVar, varIdx);
-            constraintHandler.AddVarToCurrentFactor(varState);
+            numVarRefs++;
           },
           "var_" + varIdx + "");
@@ -325 +320 @@
         () => {
           codeGenerator.Emit1(OpCodes.LoadConst1);
-          constraintHandler.StartNewTermInPoly();
         },
         "c");
@@ -363 +357 @@
           () => {
             codeGenerator.Emit2(OpCodes.LoadVar, varIdx);
-            constraintHandler.AddVarToCurrentFactor(varState);
+            numVarRefs++;
           },
           "var_" + varIdx + "");
@@ -401 +395 @@
       for (int i = 0; i < fs.Count; i++) {
         var s = fs[i];
-        if (constraintHandler.IsAllowedFollowState(state, s)) {
+        if (IsAllowedFollowState(state, s)) {
           buf[j++] = s;
         }
@@ -408 +402 @@
     }
 
+    private bool IsAllowedFollowState(int state, int nextState) {
+      // any state is allowed if we have not reached the max number of variable references
+      // otherwise we can only go towards the final state (smaller state numbers)
+      if (numVarRefs < maximumNumberOfVariables) return true;
+      else return state > nextState;
+    }
 
     public void Goto(int targetState) {
@@ -417 +417 @@
 
     public bool IsFinalState(int s) {
-      return s == StateExprEnd && !constraintHandler.IsInvalidExpression;
+      return s == StateExprEnd && numVarRefs <= maximumNumberOfVariables;
     }
 
@@ -434 +434 @@
     // After that state of the automaton is restored to the current state.
     public void GetCode(out byte[] code, out int nParams) {
-      IConstraintHandler storedConstraintHandler = null;
       int storedState = CurrentState;
       int storedPC = codeGenerator.ProgramCounter;
@@ -440 +439 @@
       if (!IsFinalState(CurrentState)) {
         // save state and code,
-        // constraints are ignored while completing the expression
-        storedConstraintHandler = constraintHandler;
-        constraintHandler = new EmptyConstraintHandler();
         storedState = CurrentState;
         storedPC = codeGenerator.ProgramCounter;
@@ -457 +453 @@
 
       // restore 
-      if (storedConstraintHandler != null) {
-        constraintHandler = storedConstraintHandler;
-        CurrentState = storedState;
-        codeGenerator.ProgramCounter = storedPC;
-      }
+      codeGenerator.ProgramCounter = storedPC;
+      CurrentState = storedState;
     }
 
@@ -467 +460 @@
       CurrentState = StartState;
       codeGenerator.Reset();
-      constraintHandler.Reset();
+      numVarRefs = 0;
     }
 

branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis/3.4/MctsSymbolicRegression/MctsSymbolicRegressionAlgorithm.cs

@@ -23 +23 @@
 using System.Linq;
 using System.Threading;
-using HeuristicLab.Algorithms.DataAnalysis.MctsSymbolicRegression.Policies;
 using HeuristicLab.Analysis;
 using HeuristicLab.Common;
@@ -75 +74 @@
     public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter {
       get { return (IFixedValueParameter<IntValue>)Parameters[ConstantOptimizationIterationsParameterName]; }
-    }
-    public IValueParameter<IPolicy> PolicyParameter {
-      get { return (IValueParameter<IPolicy>)Parameters[PolicyParameterName]; }
-    }
+    }                  
     public IFixedValueParameter<DoubleValue> PunishmentFactorParameter {
       get { return (IFixedValueParameter<DoubleValue>)Parameters[PunishmentFactorParameterName]; }
@@ -121 +117 @@
       get { return MaxVariableReferencesParameter.Value.Value; }
       set { MaxVariableReferencesParameter.Value.Value = value; }
-    }
-    public IPolicy Policy {
-      get { return PolicyParameter.Value; }
-      set { PolicyParameter.Value = value; }
     }
     public double PunishmentFactor {
@@ -183 +175 @@
       Parameters.Add(new FixedValueParameter<IntValue>(MaxVariablesParameterName,
         "Maximal number of variables references in the symbolic regression models (multiple usages of the same variable are counted)", new IntValue(5)));
-      // Parameters.Add(new FixedValueParameter<DoubleValue>(CParameterName,
-      //   "Balancing parameter in UCT formula (0 < c < 1000). Small values: greedy search. Large values: enumeration. Default: 1.0", new DoubleValue(1.0)));
-      Parameters.Add(new ValueParameter<IPolicy>(PolicyParameterName,
-        "The policy to use for selecting nodes in MCTS", new EpsilonGreedy()));
-      PolicyParameter.Hidden = true;
       Parameters.Add(new ValueParameter<ICheckedItemList<StringValue>>(AllowedFactorsParameterName,
         "Choose which expressions are allowed as factors in the model.", defaultFactorsList));
@@ -275 +262 @@
       var state = MctsSymbolicRegressionStatic.CreateState(problemData, (uint)Seed, MaxVariableReferences, ScaleVariables, 
         ConstantOptimizationIterations, Lambda,
-        Policy, collectPareto,
+        collectPareto,
         lowerLimit, upperLimit,
         allowProdOfVars: AllowedFactors.CheckedItems.Any(s => s.Value.Value == VariableProductFactorName),

branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis/3.4/MctsSymbolicRegression/MctsSymbolicRegressionStatic.cs

@@ -26 +26 @@
 using System.Linq;
 using System.Text;
-using HeuristicLab.Algorithms.DataAnalysis.MctsSymbolicRegression.Policies;
 using HeuristicLab.Core;
 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
@@ -99 +98 @@
       internal readonly Tree tree;
       internal readonly Func<byte[], int, double> evalFun;
-      internal readonly IPolicy treePolicy;
       // MCTS might get stuck. Track statistics on the number of effective rollouts
       internal int totalRollouts;
@@ -145 +143 @@
       public State(IRegressionProblemData problemData, uint randSeed, int maxVariables, bool scaleVariables,
         int constOptIterations, double lambda,
-        IPolicy treePolicy = null,
         bool collectParetoOptimalModels = false,
         double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue,
@@ -187 +184 @@
         this.testEvaluator = new ExpressionEvaluator(testY.Length, lowerEstimationLimit, upperEstimationLimit);
 
-        this.automaton = new Automaton(x, new SimpleConstraintHandler(maxVariables), allowProdOfVars, allowExp, allowLog, allowInv, allowMultipleTerms);
-        this.treePolicy = treePolicy ?? new EpsilonGreedy();
+        this.automaton = new Automaton(x, allowProdOfVars, allowExp, allowLog, allowInv, allowMultipleTerms, maxVariables);
         this.tree = new Tree() {
           state = automaton.CurrentState,
-          actionStatistics = treePolicy.CreateActionStatistics(),
           expr = "",
           level = 0
@@ -469 +464 @@
     public static IState CreateState(IRegressionProblemData problemData, uint randSeed, int maxVariables = 3,
       bool scaleVariables = true, int constOptIterations = -1, double lambda = 0.0,
-      IPolicy policy = null,
       bool collectParameterOptimalModels = false,
       double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue,
@@ -479 +473 @@
       ) {
       return new State(problemData, randSeed, maxVariables, scaleVariables, constOptIterations, lambda,
-        policy, collectParameterOptimalModels,
+        collectParameterOptimalModels,
         lowerEstimationLimit, upperEstimationLimit,
         allowProdOfVars, allowExp, allowLog, allowInv, allowMultipleTerms);
@@ -499 +493 @@
       var eval = mctsState.evalFun;
       var rand = mctsState.random;
-      var treePolicy = mctsState.treePolicy;
       double q = 0;
       bool success = false;
@@ -505 +498 @@
 
         automaton.Reset();
-        success = TryTreeSearchRec2(rand, tree, automaton, eval, treePolicy, mctsState, out q);
+        success = TryTreeSearchRec2(rand, tree, automaton, eval, mctsState, out q);
         mctsState.totalRollouts++;
       } while (!success && !tree.Done);
@@ -517 +510 @@
 
     // search forward
-    private static bool TryTreeSearchRec2(IRandom rand, Tree tree, Automaton automaton, Func<byte[], int, double> eval, IPolicy treePolicy,
+    private static bool TryTreeSearchRec2(IRandom rand, Tree tree, Automaton automaton, 
+      Func<byte[], int, double> eval,
       State state,
       out double q) {
@@ -545 +539 @@
           int selectedIdx = 0;
           if (state.children[tree].Count > 1) {
-            selectedIdx = treePolicy.Select(state.children[tree].Select(ch => ch.actionStatistics), rand);
+            selectedIdx = SelectInternal(state.children[tree], rand);
           }
 
@@ -579 +573 @@
               if (!state.nodes.TryGetValue(hc, out child)) {
                 child = new Tree() {
-                  children = null,
                   state = possibleFollowStates[i],
-                  actionStatistics = treePolicy.CreateActionStatistics(),
                   expr = actionString + automaton.GetActionString(automaton.CurrentState, possibleFollowStates[i]),
                   level = tree.level + 1
@@ -591 +583 @@
                 // whenever we join paths we need to propagate back the statistics of the existing node through the newly created link
                 // to all parents 
-                BackpropagateStatistics(child.actionStatistics, tree, state);
+                BackpropagateStatistics(tree, state, child.visits);
               } else {
                 // prevent cycles
@@ -599 +591 @@
             } else {
               child = new Tree() {
-                children = null,
                 state = possibleFollowStates[i],
-                actionStatistics = treePolicy.CreateActionStatistics(),
                 expr = actionString + automaton.GetActionString(automaton.CurrentState, possibleFollowStates[i]),
                 level = tree.level + 1
@@ -639 +629 @@
         automaton.GetCode(out code, out nParams);
         q = eval(code, nParams);
-        // Console.WriteLine("{0:N4}\t{1}", q*q, tree.expr);
         success = true;
-        BackpropagateQuality(tree, q, treePolicy, state);
+        BackpropagateQuality(tree, q, state); 
       } else {
         // we got stuck in roll-out (not evaluation necessary!)
-        // Console.WriteLine("\t" + ExprStr(automaton) + " STOP");
         q = 0.0;
         success = false;
@@ -659 +647 @@
     }
 
+    private static int SelectInternal(List<Tree> list, IRandom rand) {
+      // choose a random node.
+      Debug.Assert(list.Any(t => !t.Done));
+
+      var idx = rand.Next(list.Count);
+      while(list[idx].Done) { idx = rand.Next(list.Count); }
+      return idx;
+    }
+
     // backpropagate existing statistics to all parents
-    private static void BackpropagateStatistics(IActionStatistics stats, Tree tree, State state) {
-      tree.actionStatistics.Add(stats);
+    private static void BackpropagateStatistics(Tree tree, State state, int numVisits) {
+      tree.visits += numVisits;
+
       if (state.parents.ContainsKey(tree)) {
         foreach (var parent in state.parents[tree]) {
-          BackpropagateStatistics(stats, parent, state);
+          BackpropagateStatistics(parent, state, numVisits);
         }
       }
@@ -676 +674 @@
     }
 
-    private static void BackpropagateQuality(Tree tree, double q, IPolicy policy, State state) {
-      policy.Update(tree.actionStatistics, q);
+    private static void BackpropagateQuality(Tree tree, double q, State state) {
+      tree.visits++;
+      // TODO: q is ignored for now
 
       if (state.parents.ContainsKey(tree)) {
         foreach (var parent in state.parents[tree]) {
-          BackpropagateQuality(parent, q, policy, state);
+          BackpropagateQuality(parent, q, state);
         }
       }
@@ -718 +717 @@
       }
       return children[selectedChildIdx];
-    }
-
-    // tree search might fail because of constraints for expressions
-    // in this case we get stuck we just restart
-    // see ConstraintHandler.cs for more info
-    private static bool TryTreeSearchRec(IRandom rand, Tree tree, Automaton automaton, Func<byte[], int, double> eval, IPolicy treePolicy,
-      out double q) {
-      Tree selectedChild = null;
-      Contract.Assert(tree.state == automaton.CurrentState);
-      Contract.Assert(!tree.Done);
-      if (tree.children == null) {
-        if (automaton.IsFinalState(tree.state)) {
-          // final state
-          tree.Done = true;
-
-          // EVALUATE
-          byte[] code; int nParams;
-          automaton.GetCode(out code, out nParams);
-          q = eval(code, nParams);
-
-          treePolicy.Update(tree.actionStatistics, q);
-          return true; // we reached a final state
-        } else {
-          // EXPAND 
-          int[] possibleFollowStates = new int[1000];
-          int nFs;
-          automaton.FollowStates(automaton.CurrentState, ref possibleFollowStates, out nFs);
-          if (nFs == 0) {
-            // stuck in a dead end (no final state and no allowed follow states)
-            q = 0;
-            tree.Done = true;
-            tree.children = null;
-            return false;
-          }
-          tree.children = new Tree[nFs];
-          for (int i = 0; i < tree.children.Length; i++)
-            tree.children[i] = new Tree() {
-              children = null,
-              state = possibleFollowStates[i],
-              actionStatistics = treePolicy.CreateActionStatistics()
-            };
-
-          selectedChild = nFs > 1 ? SelectFinalOrRandom(automaton, tree, rand) : tree.children[0];
-        }
-      } else {
-        // tree.children != null
-        // UCT selection within tree
-        int selectedIdx = 0;
-        if (tree.children.Length > 1) {
-          selectedIdx = treePolicy.Select(tree.children.Select(ch => ch.actionStatistics), rand);
-        }
-        selectedChild = tree.children[selectedIdx];
-      }
-      // make selected step and recurse
-      automaton.Goto(selectedChild.state);
-      var success = TryTreeSearchRec(rand, selectedChild, automaton, eval, treePolicy, out q);
-      if (success) {
-        // only update if successful
-        treePolicy.Update(tree.actionStatistics, q);
-      }
-
-      tree.Done = tree.children.All(ch => ch.Done);
-      if (tree.Done) {
-        tree.children = null; // cut off the sub-branch if it has been fully explored
-      }
-      return success;
-    }
-
-    private static Tree SelectFinalOrRandom(Automaton automaton, Tree tree, IRandom rand) {
-      // if one of the new children leads to a final state then go there
-      // otherwise choose a random child
-      int selectedChildIdx = -1;
-      // find first final state if there is one
-      for (int i = 0; i < tree.children.Length; i++) {
-        if (automaton.IsFinalState(tree.children[i].state)) {
-          selectedChildIdx = i;
-          break;
-        }
-      }
-      // no final state -> select a the first child
-      if (selectedChildIdx == -1) {
-        selectedChildIdx = 0;
-      }
-      return tree.children[selectedChildIdx];
-    }
+    }                                            
 
     // scales data and extracts values from dataset into arrays
@@ -869 +784 @@
       automaton.GetCode(out code, out nParams);
       return Disassembler.CodeToString(code);
-    }
-
-
-    private static string WriteStatistics(Tree tree, State state) {
-      var sb = new System.IO.StringWriter();
-      sb.Write("{0}\t{1:N5}\t", tree.actionStatistics.Tries, tree.actionStatistics.AverageQuality);
-      if (state.children.ContainsKey(tree)) {
-        foreach (var ch in state.children[tree]) {
-          sb.Write("{0}\t{1:N5}\t", ch.actionStatistics.Tries, ch.actionStatistics.AverageQuality);
-        }
-      }
-      sb.WriteLine();
-      return sb.ToString();
     }
 
@@ -899 +801 @@
 
     private static void TraceTreeRec(Tree tree, int parentId, StringBuilder sb, ref int nextId, State state) {
-      var avgNodeQ = tree.actionStatistics.AverageQuality;
-      var tries = tree.actionStatistics.Tries;
-      if (double.IsNaN(avgNodeQ)) avgNodeQ = 0.0;
-      var hue = (1 - avgNodeQ) / 360.0 * 240.0; // 0 equals red, 240 equals blue
-      hue = 0.0;
-
-      sb.AppendFormat("{0} [label=\"{1:E3} {2}\" color=\"{3:N3} 0.999 0.999\"]; ", parentId, avgNodeQ, tries, hue).AppendLine();
+      var tries = tree.visits;
+
+      sb.AppendFormat("{0} [label=\"{1}\"]; ", parentId, tries).AppendLine();
 
       var list = new List<Tuple<int, int, Tree>>();
@@ -911 +809 @@
         foreach (var ch in state.children[tree]) {
           nextId++;
-          avgNodeQ = ch.actionStatistics.AverageQuality;
-          tries = ch.actionStatistics.Tries;
-          if (double.IsNaN(avgNodeQ)) avgNodeQ = 0.0;
-          hue = (1 - avgNodeQ) / 360.0 * 240.0; // 0 equals red, 240 equals blue
-          hue = 0.0;
-          sb.AppendFormat("{0} [label=\"{1:E3} {2}\" color=\"{3:N3} 0.999 0.999\"]; ", nextId, avgNodeQ, tries, hue).AppendLine();
-          sb.AppendFormat("{0} -> {1} [label=\"{3}\"]", parentId, nextId, avgNodeQ, ch.expr).AppendLine();
+          tries = ch.visits;
+          sb.AppendFormat("{0} [label=\"{1}\"]; ", nextId, tries).AppendLine();
+          sb.AppendFormat("{0} -> {1} [label=\"{2}\"]", parentId, nextId, ch.expr).AppendLine();
           list.Add(Tuple.Create(tries, nextId, ch));
         }
@@ -927 +821 @@
             var chch = state.children[ch].First();
             nextId++;
-            avgNodeQ = chch.actionStatistics.AverageQuality;
-            tries = chch.actionStatistics.Tries;
-            if (double.IsNaN(avgNodeQ)) avgNodeQ = 0.0;
-            hue = (1 - avgNodeQ) / 360.0 * 240.0; // 0 equals red, 240 equals blue
-            hue = 0.0;
-            sb.AppendFormat("{0} [label=\"{1:E3} {2}\" color=\"{3:N3} 0.999 0.999\"]; ", nextId, avgNodeQ, tries, hue).AppendLine();
-            sb.AppendFormat("{0} -> {1} [label=\"{3}\"]", chId, nextId, avgNodeQ, chch.expr).AppendLine();
+            tries = chch.visits;
+            sb.AppendFormat("{0} [label=\"{1}\"]; ", nextId, tries).AppendLine();
+            sb.AppendFormat("{0} -> {1} [label=\"{2}\"]", chId, nextId, chch.expr).AppendLine();
           }
         }
@@ -957 +847 @@
         if (!nodeIds.TryGetValue(parent, out parentId)) {
           parentId = nodeIds.Count + 1;
-          var avgNodeQ = parent.actionStatistics.AverageQuality;
-          var tries = parent.actionStatistics.Tries;
-          if (double.IsNaN(avgNodeQ)) avgNodeQ = 0.0;
-          var hue = (1 - avgNodeQ) / 360.0 * 240.0; // 0 equals red, 240 equals blue
-          hue = 0.0;
-          if (parent.actionStatistics.Tries > threshold)
-            sb.Write("{0} [label=\"{1:E3} {2}\" color=\"{3:N3} 0.999 0.999\"]; ", parentId, avgNodeQ, tries, hue);
+          var tries = parent.visits;
+          if (tries > threshold)
+            sb.Write("{0} [label=\"{1}\"]; ", parentId, tries);
           nodeIds.Add(parent, parentId);
         }
@@ -972 +858 @@
             nodeIds.Add(child, childId);
           }
-          var avgNodeQ = child.actionStatistics.AverageQuality;
-          var tries = child.actionStatistics.Tries;
+          var tries = child.visits;
           if (tries < 1) continue;
-          if (double.IsNaN(avgNodeQ)) avgNodeQ = 0.0;
-          var hue = (1 - avgNodeQ) / 360.0 * 240.0; // 0 equals red, 240 equals blue
-          hue = 0.0;
           if (tries > threshold) {
-            sb.Write("{0} [label=\"{1:E3} {2}\" color=\"{3:N3} 0.999 0.999\"]; ", childId, avgNodeQ, tries, hue);
+            sb.Write("{0} [label=\"{1}\"]; ", childId, tries);
             var edgeLabel = child.expr;
             // if (parent.expr.Length > 0) edgeLabel = edgeLabel.Replace(parent.expr, "");
-            sb.Write("{0} -> {1} [label=\"{3}\"]", parentId, childId, avgNodeQ, edgeLabel);
+            sb.Write("{0} -> {1} [label=\"{2}\"]", parentId, childId, edgeLabel);
           }
         }

branches/MCTS-SymbReg-2796/HeuristicLab.Algorithms.DataAnalysis/3.4/MctsSymbolicRegression/Tree.cs

@@ -19 +19 @@
  */
 #endregion
-
-using HeuristicLab.Algorithms.DataAnalysis.MctsSymbolicRegression.Policies;
-
+                                                      
 namespace HeuristicLab.Algorithms.DataAnalysis.MctsSymbolicRegression {
-  // represents tree nodes for the search tree in MCTS
   internal class Tree {
     public int state;
     public int level;
     public string expr;
-    public bool Done {
-      get { return actionStatistics.Done; }
-      set { actionStatistics.Done = value; }
-    }
-    public IActionStatistics actionStatistics;
-    public Tree[] children;
+    public bool Done { get; set; }
+    public int visits;
+    //   {
+    //   get { return actionStatistics.Done; }
+    //   set { actionStatistics.Done = value; }
+    // }
+    // public IActionStatistics actionStatistics;
+    // public Tree[] children;
   }
 }