#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* 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.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
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("Pearson R² & Tree Complexity Evaluator", "Calculates the Pearson R² and the tree complexity of a symbolic regression solution.")]
[StorableClass("D8DB8549-2982-48DF-B33F-53506D9917C1")]
public class PearsonRSquaredTreeComplexityEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
[StorableConstructor]
protected PearsonRSquaredTreeComplexityEvaluator(bool deserializing) : base(deserializing) { }
protected PearsonRSquaredTreeComplexityEvaluator(PearsonRSquaredTreeComplexityEvaluator original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new PearsonRSquaredTreeComplexityEvaluator(this, cloner);
}
public PearsonRSquaredTreeComplexityEvaluator() : base() { }
public override IEnumerable Maximization { get { return new bool[2] { true, false }; } } // maximize R² and minimize model complexity
public override IOperation InstrumentedApply() {
IEnumerable rows = GenerateRowsToEvaluate();
var solution = SymbolicExpressionTreeParameter.ActualValue;
var problemData = ProblemDataParameter.ActualValue;
var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
var estimationLimits = EstimationLimitsParameter.ActualValue;
var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value;
if (UseConstantOptimization) {
SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, estimationLimits.Upper, estimationLimits.Lower);
}
double[] qualities = Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling, DecimalPlaces);
QualitiesParameter.ActualValue = new DoubleArray(qualities);
return base.InstrumentedApply();
}
public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable rows, bool applyLinearScaling, int decimalPlaces) {
double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
if (decimalPlaces >= 0)
r2 = Math.Round(r2, decimalPlaces);
return new double[2] { r2, SymbolicDataAnalysisModelComplexityCalculator.CalculateComplexity(solution) };
}
public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) {
SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
EstimationLimitsParameter.ExecutionContext = context;
ApplyLinearScalingParameter.ExecutionContext = context;
// DecimalPlaces parameter is a FixedValueParameter and doesn't need the context.
double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value, DecimalPlaces);
SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
EstimationLimitsParameter.ExecutionContext = null;
ApplyLinearScalingParameter.ExecutionContext = null;
return quality;
}
}
}