#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;
using System.Collections.Generic;
using System.Linq;
using System.Drawing;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Operators;
namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic {
[Item("SymbolicRegressionEvaluator", "Evaluates a symbolic regression solution.")]
[StorableClass]
public abstract class SymbolicRegressionEvaluator : SingleSuccessorOperator, ISymbolicRegressionEvaluator {
#region ISymbolicRegressionEvaluator Members
public ILookupParameter QualityParameter {
get { return (ILookupParameter)Parameters["Quality"]; }
}
public ILookupParameter FunctionTreeParameter {
get { return (ILookupParameter)Parameters["FunctionTree"]; }
}
public ILookupParameter DatasetParameter {
get { return (ILookupParameter)Parameters["Dataset"]; }
}
public ILookupParameter TargetVariableParameter {
get { return (ILookupParameter)Parameters["TargetVariable"]; }
}
public ILookupParameter SamplesStartParameter {
get { return (ILookupParameter)Parameters["SamplesStart"]; }
}
public ILookupParameter SamplesEndParameter {
get { return (ILookupParameter)Parameters["SamplesEnd"]; }
}
public ILookupParameter NumberOfEvaluatedNodesParameter {
get { return (ILookupParameter)Parameters["NumberOfEvaluatedNodes"]; }
}
#endregion
public SymbolicRegressionEvaluator()
: base() {
Parameters.Add(new LookupParameter("Quality", "The quality of the evaluated symbolic regression solution."));
Parameters.Add(new LookupParameter("FunctionTree", "The symbolic regression solution encoded as a symbolic expression tree."));
Parameters.Add(new LookupParameter("Dataset", "The data set on which the symbolic regression solution should be evaluated."));
Parameters.Add(new LookupParameter("TargetVariable", "The target variable of the symbolic regression solution."));
Parameters.Add(new LookupParameter("SamplesStart", "The start index of the partition of the data set on which the symbolic regression solution should be evaluated."));
Parameters.Add(new LookupParameter("SamplesEnd", "The end index of the partition of the data set on which the symbolic regression solution should be evaluated."));
Parameters.Add(new LookupParameter("NumberOfEvaluatedNodes", "The number of evaluated nodes so far (for performance measurements.)"));
}
public override IOperation Apply() {
SymbolicExpressionTree solution = FunctionTreeParameter.ActualValue;
Dataset dataset = DatasetParameter.ActualValue;
StringValue targetVariable = TargetVariableParameter.ActualValue;
IntValue samplesStart = SamplesStartParameter.ActualValue;
IntValue samplesEnd = SamplesEndParameter.ActualValue;
DoubleValue numberOfEvaluatedNodes = NumberOfEvaluatedNodesParameter.ActualValue;
QualityParameter.ActualValue = new DoubleValue(Evaluate(solution, dataset, targetVariable, samplesStart, samplesEnd, numberOfEvaluatedNodes));
return null;
}
protected abstract double Evaluate(SymbolicExpressionTree solution, Dataset dataset, StringValue targetVariable, IntValue samplesStart, IntValue samplesEnd, DoubleValue numberOfEvaluatedNodes);
}
}