#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;
}
}
}