Changeset 14258

Timestamp:

08/17/16 12:19:24 (8 years ago)

Author:

gkronber

Message:

#2657: added random restarts for NonlinearRegression (curve fitting) algorithm

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NonlinearRegression/NonlinearRegression.cs (modified) (6 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/NonlinearRegression/NonlinearRegression.cs

@@ -33 +33 @@
 using HeuristicLab.Problems.DataAnalysis.Symbolic;
 using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
+using HeuristicLab.Random;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
@@ -45 +46 @@
     private const string ModelStructureParameterName = "Model structure";
     private const string IterationsParameterName = "Iterations";
+    private const string RestartsParameterName = "Restarts";
+    private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
+    private const string SeedParameterName = "Seed";
 
     public IFixedValueParameter<StringValue> ModelStructureParameter {
@@ -51 +55 @@
     public IFixedValueParameter<IntValue> IterationsParameter {
       get { return (IFixedValueParameter<IntValue>)Parameters[IterationsParameterName]; }
+    }
+
+    public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
+      get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
+    }
+
+    public IFixedValueParameter<IntValue> SeedParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
+    }
+
+    public IFixedValueParameter<IntValue> RestartsParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[RestartsParameterName]; }
     }
 
@@ -63 +79 @@
     }
 
+    public int Restarts {
+      get { return RestartsParameter.Value.Value; }
+      set { RestartsParameter.Value.Value = value; }
+    }
+
+    public int Seed {
+      get { return SeedParameter.Value.Value; }
+      set { SeedParameter.Value.Value = value; }
+    }
+
+    public bool SetSeedRandomly {
+      get { return SetSeedRandomlyParameter.Value.Value; }
+      set { SetSeedRandomlyParameter.Value.Value = value; }
+    }
 
     [StorableConstructor]
@@ -74 +104 @@
       Parameters.Add(new FixedValueParameter<StringValue>(ModelStructureParameterName, "The function for which the parameters must be fit (only numeric constants are tuned).", new StringValue("1.0 * x*x + 0.0")));
       Parameters.Add(new FixedValueParameter<IntValue>(IterationsParameterName, "The maximum number of iterations for constants optimization.", new IntValue(200)));
+      Parameters.Add(new FixedValueParameter<IntValue>(RestartsParameterName, "The number of independent random restarts", new IntValue(10)));
+      Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The PRNG seed value.", new IntValue()));
+      Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "Switch to determine if the random number seed should be initialized randomly.", new BoolValue(true)));
     }
+
     [StorableHook(HookType.AfterDeserialization)]
-    private void AfterDeserialization() { }
+    private void AfterDeserialization() {
+      // BackwardsCompatibility3.3
+      #region Backwards compatible code, remove with 3.4
+      if (!Parameters.ContainsKey(RestartsParameterName))
+        Parameters.Add(new FixedValueParameter<IntValue>(RestartsParameterName, "The number of independent random restarts", new IntValue(1)));
+      if (!Parameters.ContainsKey(SeedParameterName))
+        Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The PRNG seed value.", new IntValue()));
+      if (!Parameters.ContainsKey(SetSeedRandomlyParameterName))
+        Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "Switch to determine if the random number seed should be initialized randomly.", new BoolValue(true)));
+      #endregion
+    }
 
     public override IDeepCloneable Clone(Cloner cloner) {
@@ -84 +128 @@
     #region nonlinear regression
     protected override void Run() {
-      var solution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations);
-      Results.Add(new Result(RegressionSolutionResultName, "The nonlinear regression solution.", solution));
-      Results.Add(new Result("Root mean square error (train)", "The root of the mean of squared errors of the regression solution on the training set.", new DoubleValue(solution.TrainingRootMeanSquaredError)));
-      Results.Add(new Result("Root mean square error (test)", "The root of the mean of squared errors of the regression solution on the test set.", new DoubleValue(solution.TestRootMeanSquaredError)));
+      if (SetSeedRandomly) Seed = (new System.Random()).Next();
+      var rand = new MersenneTwister((uint)Seed);
+      IRegressionSolution bestSolution = null;
+      for (int r = 0; r < Restarts; r++) {
+        var solution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, rand);
+        if (bestSolution == null || solution.TrainingRootMeanSquaredError < bestSolution.TrainingRootMeanSquaredError) {
+          bestSolution = solution;
+        }
+      }
+
+      Results.Add(new Result(RegressionSolutionResultName, "The nonlinear regression solution.", bestSolution));
+      Results.Add(new Result("Root mean square error (train)", "The root of the mean of squared errors of the regression solution on the training set.", new DoubleValue(bestSolution.TrainingRootMeanSquaredError)));
+      Results.Add(new Result("Root mean square error (test)", "The root of the mean of squared errors of the regression solution on the test set.", new DoubleValue(bestSolution.TestRootMeanSquaredError)));
+
     }
 
-    public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations) {
+    /// <summary>
+    /// Fits a model to the data by optimizing the numeric constants.
+    /// Model is specified as infix expression containing variable names and numbers. 
+    /// The starting point for the numeric constants is initialized randomly if a random number generator is specified (~N(0,1)). Otherwise the user specified constants are
+    /// used as a starting point. 
+    /// </summary>
+    /// <param name="problemData">Training and test data</param>
+    /// <param name="modelStructure">The function as infix expression</param>
+    /// <param name="maxIterations">Number of constant optimization iterations (using Levenberg-Marquardt algorithm)</param>
+    /// <param name="random">Optional random number generator for random initialization of numeric constants.</param>
+    /// <returns></returns>
+    public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations, IRandom random = null) {
       var parser = new InfixExpressionParser();
       var tree = parser.Parse(modelStructure);
-      var simplifier = new SymbolicDataAnalysisExpressionTreeSimplifier();
-      
+
       if (!SymbolicRegressionConstantOptimizationEvaluator.CanOptimizeConstants(tree)) throw new ArgumentException("The optimizer does not support the specified model structure.");
 
+      // initialize constants randomly
+      if (random != null) {
+        foreach (var node in tree.IterateNodesPrefix().OfType<ConstantTreeNode>()) {
+          node.Value = NormalDistributedRandom.NextDouble(random, 0, 1);
+        }
+      }
       var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();
-      SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices, 
+
+      SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices,
         applyLinearScaling: false, maxIterations: maxIterations,
         updateVariableWeights: false, updateConstantsInTree: true);
-
 
       var scaledModel = new SymbolicRegressionModel(problemData.TargetVariable, tree, (ISymbolicDataAnalysisExpressionTreeInterpreter)interpreter.Clone());