source: branches/symbreg-factors-2650/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionVarianceModelEvaluator.cs @ 14542

#region License Information
/* HeuristicLab
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#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  [Item("Variance Model Evaluator", "Can be used for modeling variance of a variable. Assumes that the variable values are sampled from a zero mean Gaussian. Use a model for the target variable to calculate the residuals. In a second step use the residuals as the target variable and use this evaluator to create the model for the conditional variance.")]
  [StorableClass]
  public class SymbolicRegressionVarianceModelEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
    [StorableConstructor]
    protected SymbolicRegressionVarianceModelEvaluator(bool deserializing) : base(deserializing) { }
    protected SymbolicRegressionVarianceModelEvaluator(SymbolicRegressionVarianceModelEvaluator original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicRegressionVarianceModelEvaluator(this, cloner);
    }

    public SymbolicRegressionVarianceModelEvaluator() : base() { }

    public override bool Maximization { get { return true; } }

    public override IOperation InstrumentedApply() {
      var solution = SymbolicExpressionTreeParameter.ActualValue;
      IEnumerable<int> rows = GenerateRowsToEvaluate();

      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
      QualityParameter.ActualValue = new DoubleValue(quality);

      return base.InstrumentedApply();
    }

    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
      IEnumerable<double> targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);

      // assumes residuals follow a zero-mean Gaussian distribution where the variance is a function of the inputs

      // boundedEstimatedValues is the estimator for std.dev.
      // log likelihood for N(target_i | 0, boundesEstimatedValues)
      var l2pi = Math.Log(2.0 * Math.PI);
      var ll = -0.5 *
               boundedEstimatedValues.Zip(targetValues, (s, t) =>
                 +l2pi
                 + Math.Log(s * s)
                 + (t * t) / (s * s)
                 ).Sum();

      return ll;
    }

    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
      EstimationLimitsParameter.ExecutionContext = context;

      double ll = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);

      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
      EstimationLimitsParameter.ExecutionContext = null;

      return ll;
    }
  }
}