#region License Information
/* HeuristicLab
* Copyright (C) 2002-2010 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.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
[Item("MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator", "Calculates the correlation coefficient rē and the number of variables of a symbolic regression solution.")]
[StorableClass]
public sealed class MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator : MultiObjectiveSymbolicRegressionEvaluator {
private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
#region parameter properties
public IValueLookupParameter UpperEstimationLimitParameter {
get { return (IValueLookupParameter)Parameters[UpperEstimationLimitParameterName]; }
}
public IValueLookupParameter LowerEstimationLimitParameter {
get { return (IValueLookupParameter)Parameters[LowerEstimationLimitParameterName]; }
}
#endregion
#region properties
public DoubleValue UpperEstimationLimit {
get { return UpperEstimationLimitParameter.ActualValue; }
}
public DoubleValue LowerEstimationLimit {
get { return LowerEstimationLimitParameter.ActualValue; }
}
#endregion
[StorableConstructor]
private MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator(bool deserializing) : base(deserializing) { }
private MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator(MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator original, Cloner cloner)
: base(original, cloner) {
}
public MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator()
: base() {
Parameters.Add(new ValueLookupParameter(UpperEstimationLimitParameterName, "The upper limit that should be used as cut off value for the output values of symbolic expression trees."));
Parameters.Add(new ValueLookupParameter(LowerEstimationLimitParameterName, "The lower limit that should be used as cut off value for the output values of symbolic expression trees."));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new MultiObjectiveSymbolicRegressionPearsonsRSquaredEvaluator(this, cloner);
}
protected override double[] Evaluate(ISymbolicExpressionTreeInterpreter interpreter, SymbolicExpressionTree solution, Dataset dataset, StringValue targetVariable, IEnumerable rows) {
double r2 = SymbolicRegressionPearsonsRSquaredEvaluator.Calculate(interpreter, solution, LowerEstimationLimit.Value, UpperEstimationLimit.Value, dataset, targetVariable.Value, rows);
List vars = new List();
solution.Root.ForEachNodePostfix(n => {
var varNode = n as VariableTreeNode;
if (varNode != null && !vars.Contains(varNode.VariableName)) {
vars.Add(varNode.VariableName);
}
});
return new double[2] { r2, vars.Count };
}
}
}