#region License Information /* HeuristicLab * Copyright (C) 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 HEAL.Attic; namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression { [Item("Pearson R² Evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic regression solution.")] [StorableType("6FAEC6C2-C747-452A-A60D-29AE37898A90")] public class SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator : SymbolicRegressionSingleObjectiveEvaluator { [StorableConstructor] protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(StorableConstructorFlag _) : base(_) { } protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(this, cloner); } public SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator() : base() { } public override bool Maximization { get { return true; } } public override IOperation InstrumentedApply() { var solution = SymbolicExpressionTreeParameter.ActualValue; IEnumerable rows = GenerateRowsToEvaluate(); double quality = Calculate( solution, ProblemDataParameter.ActualValue, rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, ApplyLinearScalingParameter.ActualValue.Value, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); QualityParameter.ActualValue = new DoubleValue(quality); return base.InstrumentedApply(); } //TODO: refactor like evaluate method public static double Calculate( ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, bool applyLinearScaling, double lowerEstimationLimit, double upperEstimationLimit) { IEnumerable estimatedValues = interpreter.GetSymbolicExpressionTreeValues(tree, 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) return double.NaN; return r*r; } public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; ApplyLinearScalingParameter.ExecutionContext = context; double r2 = Calculate( tree, problemData, rows, SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, ApplyLinearScalingParameter.ActualValue.Value, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper); SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; ApplyLinearScalingParameter.ExecutionContext = null; return r2; } public override double Evaluate( ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, bool applyLinearScaling = true, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue) { return Calculate( tree, problemData, rows, interpreter, applyLinearScaling, lowerEstimationLimit, upperEstimationLimit); } } }