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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.Regression/3.4/MultiObjective/SymbolicRegressionMultiObjectiveMeanSquaredErrorTreeSizeEvaluator.cs @ 5851

Last change on this file since 5851 was 5851, checked in by gkronber, 13 years ago

#1411 added evaluated nodes parameter to symbolic data analysis evaluators.

File size: 4.4 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System.Linq;
23using System.Collections.Generic;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
31  [Item("Mean squared error & Tree size Evaluator", "Calculates the mean squared error and the tree size of a symbolic regression solution.")]
32  [StorableClass]
33  public class SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator : SymbolicRegressionMultiObjectiveEvaluator {
34    [StorableConstructor]
35    protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(bool deserializing) : base(deserializing) { }
36    protected SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator original, Cloner cloner)
37      : base(original, cloner) {
38    }
39    public override IDeepCloneable Clone(Cloner cloner) {
40      return new SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator(this, cloner);
41    }
42
43    public SymbolicRegressionMultiObjectiveMeanSquaredErrorSolutionSizeEvaluator() : base() { }
44
45    public override IEnumerable<bool> Maximization { get { return new bool[2] { false, false }; } }
46
47    public override Core.IOperation Apply() {
48      IEnumerable<int> rows = GenerateRowsToEvaluate();
49      var solution = SymbolicExpressionTreeParameter.ActualValue;
50      double[] qualities = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
51      QualitiesParameter.ActualValue = new DoubleArray(qualities);
52      AddEvaluatedNodes(solution.Length * rows.Count());
53      return base.Apply();
54    }
55
56    public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable<int> rows) {
57      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
58      IEnumerable<double> originalValues = problemData.Dataset.GetEnumeratedVariableValues(problemData.TargetVariable, rows);
59      IEnumerable<double> boundedEstimationValues = estimatedValues.LimitToRange(lowerEstimationLimit, upperEstimationLimit);
60      double mse = OnlineMeanSquaredErrorEvaluator.Calculate(originalValues, boundedEstimationValues);
61      return new double[2] { mse, solution.Length };
62    }
63
64    public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
65      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
66      EstimationLimitsParameter.ExecutionContext = context;
67      EvaluatedNodesParameter.ExecutionContext = context;
68
69      double[] quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
70      AddEvaluatedNodes(tree.Length * rows.Count());
71
72      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
73      EstimationLimitsParameter.ExecutionContext = null;
74      EvaluatedNodesParameter.ExecutionContext = null;
75
76      return quality;
77    }
78  }
79}
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