#region License Information /* HeuristicLab * Copyright (C) 2002-2018 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.Classification { [Item("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic classification solution.")] [StorableClass] public class SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator : SymbolicClassificationSingleObjectiveEvaluator { [StorableConstructor] protected SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { } protected SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator(this, cloner); } public SymbolicClassificationSingleObjectiveMeanSquaredErrorEvaluator() : base() { } public override bool Maximization { get { return false; } } public override IOperation InstrumentedApply() { IEnumerable rows = GenerateRowsToEvaluate(); var solution = SymbolicExpressionTreeParameter.ActualValue; double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows, ApplyLinearScalingParameter.ActualValue.Value); QualityParameter.ActualValue = new DoubleValue(quality); 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 mse; if (applyLinearScaling) { var mseCalculator = new OnlineMeanSquaredErrorCalculator(); CalculateWithScaling(targetValues, estimatedValues, lowerEstimationLimit, upperEstimationLimit, mseCalculator, problemData.Dataset.Rows); errorState = mseCalculator.ErrorState; mse = mseCalculator.MeanSquaredError; } else { IEnumerable boundedEstimatedValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit); mse = OnlineMeanSquaredErrorCalculator.Calculate(targetValues, boundedEstimatedValues, out errorState); } if (errorState != OnlineCalculatorError.None) return Double.NaN; return mse; } public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IClassificationProblemData problemData, IEnumerable rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; ApplyLinearScalingParameter.ExecutionContext = context; double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value); SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; ApplyLinearScalingParameter.ExecutionContext = null; return mse; } } }