#region License Information
/* HeuristicLab
* Copyright (C) 2002-2011 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.Regression {
[Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic regression solution.")]
[StorableClass]
public class SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
[StorableConstructor]
protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(bool deserializing) : base(deserializing) { }
protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator original, Cloner cloner)
: base(original, cloner) {
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(this, cloner);
}
public SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator() : base() { }
public override IEnumerable Maximization { get { return new bool[2] { false, false }; } }
public override Core.IOperation Apply() {
IEnumerable rows = GenerateRowsToEvaluate();
double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, SymbolicExpressionTreeParameter.ActualValue, LowerEstimationLimit.Value, UpperEstimationLimit.Value, ProblemData, rows);
QualitiesParameter.ActualValue = new DoubleArray(qualities);
return base.Apply();
}
public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable rows) {
IEnumerable estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
IEnumerable originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
IEnumerable boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues);
return new double[2] { mse, solution.Length };
}
public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) {
return Calculate(SymbolicDataAnalysisTreeInterpreter, tree, LowerEstimationLimit.Value, UpperEstimationLimit.Value, problemData, rows);
}
}
}