#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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.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.Classification { [Item("Pearson R² & Tree size Evaluator", "Calculates the Pearson R² and the tree size of a symbolic classification solution.")] [StorableClass] public class SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator : SymbolicClassificationMultiObjectiveEvaluator { [StorableConstructor] protected SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator(bool deserializing) : base(deserializing) { } protected SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator(SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator(this, cloner); } public SymbolicClassificationMultiObjectivePearsonRSquaredTreeSizeEvaluator() : base() { } public override IEnumerable Maximization { get { return new bool[2] { true, false }; } } public override IOperation InstrumentedApply() { IEnumerable rows = GenerateRowsToEvaluate(); var solution = SymbolicExpressionTreeParameter.ActualValue; double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value); QualitiesParameter.ActualValue = new DoubleArray(qualities); return base.InstrumentedApply(); } public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IClassificationProblemData problemData, IEnumerable rows, bool applyLinearScaling) { IEnumerable estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows); IEnumerable targetValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows); OnlineCalculatorError errorState; double r; if (applyLinearScaling) { var rCalculator = new OnlinePearsonsRCalculator(); CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, rCalculator, problemData.Dataset.Rows); errorState = rCalculator.ErrorState; r = rCalculator.R; } else { IEnumerable boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); r = OnlinePearsonsRCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState); } if (errorState != OnlineCalculatorError.None) r = double.NaN; return new double[2] { r*r, solution.Length }; } public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value); SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; return quality; } } }