source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/SingleObjective/Evaluators/SymbolicRegressionLogResidualEvaluator.cs @ 12973

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
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 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
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 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
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 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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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("Log Residual Evaluator", "Evaluator for symbolic regression models that calculates the mean of logarithmic absolute residuals avg(log( 1 + abs(y' - y)))" +
                                  "This evaluator does not perform linear scaling!" +
                                  "This evaluator can be useful if the modeled function contains discontinuities (e.g. 1/x). " +
                                  "For some data sets (e.g. Korns benchmark instances containing inverses of near zero values) the squared error or absolute " +
                                  "error put too much emphasis on modeling the outlier values. Using log-residuals instead has the " +
                                  "effect that smaller residuals have a stronger impact on the total quality compared to the large residuals." +
                                  "This effects GP convergence because functional fragments which are necessary to explain small variations are also more likely" +
                                  " to stay in the population. This is useful even when the actual objective function is mean of squared errors.")]
  [StorableClass]
  public class SymbolicRegressionLogResidualEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
    [StorableConstructor]
    protected SymbolicRegressionLogResidualEvaluator(bool deserializing) : base(deserializing) { }
    protected SymbolicRegressionLogResidualEvaluator(SymbolicRegressionLogResidualEvaluator original, Cloner cloner)
      : base(original, cloner) {
    }
    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicRegressionLogResidualEvaluator(this, cloner);
    }

    public SymbolicRegressionLogResidualEvaluator() : base() { }

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

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

      var problemData = ProblemDataParameter.ActualValue;
      var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
      var estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows).ToArray();
      var targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
      var estimationLimits = EstimationLimitsParameter.ActualValue;

      if (SaveEstimatedValuesToScope) {
        var boundedValues = estimatedValues.LimitToRange(estimationLimits.Lower, estimationLimits.Upper).ToArray();
        var scope = ExecutionContext.Scope;
        if (scope.Variables.ContainsKey("EstimatedValues"))
          scope.Variables["EstimatedValues"].Value = new DoubleArray(boundedValues);
        else
          scope.Variables.Add(new Core.Variable("EstimatedValues", new DoubleArray(boundedValues)));
      }

      double quality = Calculate(targetValues, estimatedValues, estimationLimits.Lower, estimationLimits.Upper);
      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);
      return Calculate(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit);
    }

    private static double Calculate(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues, double lowerEstimationLimit, double upperEstimationLimit) {
      IEnumerable<double> boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);

      var logRes = boundedEstimatedValues.Zip(targetValues, (e, t) => Math.Log(1.0 + Math.Abs(e - t)));

      OnlineCalculatorError errorState;
      OnlineCalculatorError varErrorState;
      double mlr;
      double variance;
      OnlineMeanAndVarianceCalculator.Calculate(logRes, out mlr, out variance, out errorState, out varErrorState);
      if (errorState != OnlineCalculatorError.None) return double.NaN;
      return mlr;
    }

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

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

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

      return mlr;
    }
  }
}