source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/BestSymbolicRegressionSolutionAnalyzer.cs @ 4068

Last change on this file since 4068 was 4068, checked in by swagner, 9 years ago

Sorted usings and removed unused usings in entire solution (#1094)

File size: 7.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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 HeuristicLab.Analysis;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Optimization;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Problems.DataAnalysis.Symbolic;
31
32namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
33  [Item("BestSymbolicRegressionSolutionAnalyzer", "An operator for analyzing the best solution of symbolic regression problems given in symbolic expression tree encoding.")]
34  [StorableClass]
35  public sealed class BestSymbolicRegressionSolutionAnalyzer : RegressionSolutionAnalyzer, ISymbolicRegressionAnalyzer {
36    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
37    private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
38    private const string BestSolutionInputvariableCountResultName = "Variables used by best solution";
39    private const string VariableFrequenciesParameterName = "VariableFrequencies";
40    private const string VariableImpactsResultName = "Integrated variable frequencies";
41    private const string BestSolutionParameterName = "BestSolution";
42
43    #region parameter properties
44    public ScopeTreeLookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
45      get { return (ScopeTreeLookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
46    }
47    public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
48      get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
49    }
50    public ILookupParameter<SymbolicRegressionSolution> BestSolutionParameter {
51      get { return (ILookupParameter<SymbolicRegressionSolution>)Parameters[BestSolutionParameterName]; }
52    }
53    public ILookupParameter<DataTable> VariableFrequenciesParameter {
54      get { return (ILookupParameter<DataTable>)Parameters[VariableFrequenciesParameterName]; }
55    }
56    #endregion
57    #region properties
58    public ISymbolicExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
59      get { return SymbolicExpressionTreeInterpreterParameter.ActualValue; }
60    }
61    public ItemArray<SymbolicExpressionTree> SymbolicExpressionTree {
62      get { return SymbolicExpressionTreeParameter.ActualValue; }
63    }
64    public DataTable VariableFrequencies {
65      get { return VariableFrequenciesParameter.ActualValue; }
66    }
67    #endregion
68
69    public BestSymbolicRegressionSolutionAnalyzer()
70      : base() {
71      Parameters.Add(new ScopeTreeLookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees to analyze."));
72      Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used for the analysis of symbolic expression trees."));
73      Parameters.Add(new LookupParameter<DataTable>(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts"));
74      Parameters.Add(new LookupParameter<SymbolicRegressionSolution>(BestSolutionParameterName, "The best symbolic regression solution."));
75    }
76
77    [StorableHook(HookType.AfterDeserialization)]
78    private void Initialize() {
79      if (!Parameters.ContainsKey(VariableFrequenciesParameterName)) {
80        Parameters.Add(new LookupParameter<DataTable>(VariableFrequenciesParameterName, "The variable frequencies table to use for the calculation of variable impacts"));
81      }
82    }
83
84    protected override DataAnalysisSolution UpdateBestSolution() {
85      double upperEstimationLimit = UpperEstimationLimit != null ? UpperEstimationLimit.Value : double.PositiveInfinity;
86      double lowerEstimationLimit = LowerEstimationLimit != null ? LowerEstimationLimit.Value : double.NegativeInfinity;
87
88      int i = Quality.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index;
89
90      if (BestSolutionQualityParameter.ActualValue == null || BestSolutionQualityParameter.ActualValue.Value > Quality[i].Value) {
91        var model = new SymbolicRegressionModel((ISymbolicExpressionTreeInterpreter)SymbolicExpressionTreeInterpreter.Clone(),
92          SymbolicExpressionTree[i]);
93        var solution = new SymbolicRegressionSolution(ProblemData, model, lowerEstimationLimit, upperEstimationLimit);
94        solution.Name = BestSolutionParameterName;
95        solution.Description = "Best solution on validation partition found over the whole run.";
96        BestSolutionParameter.ActualValue = solution;
97        BestSolutionQualityParameter.ActualValue = Quality[i];
98        BestSymbolicRegressionSolutionAnalyzer.UpdateSymbolicRegressionBestSolutionResults(solution, ProblemData, Results, VariableFrequencies);
99      }
100      return BestSolutionParameter.ActualValue;
101    }
102
103    public static void UpdateBestSolutionResults(SymbolicRegressionSolution bestSolution, DataAnalysisProblemData problemData, ResultCollection results, IntValue currentGeneration, DataTable variableFrequencies) {
104      RegressionSolutionAnalyzer.UpdateBestSolutionResults(bestSolution, problemData, results, currentGeneration);
105      UpdateSymbolicRegressionBestSolutionResults(bestSolution, problemData, results, variableFrequencies);
106    }
107
108    private static void UpdateSymbolicRegressionBestSolutionResults(SymbolicRegressionSolution bestSolution, DataAnalysisProblemData problemData, ResultCollection results, DataTable variableFrequencies) {
109      if (results.ContainsKey(BestSolutionInputvariableCountResultName)) {
110        results[BestSolutionInputvariableCountResultName].Value = new IntValue(bestSolution.Model.InputVariables.Count());
111        results[VariableImpactsResultName].Value = CalculateVariableImpacts(variableFrequencies);
112      } else {
113        results.Add(new Result(BestSolutionInputvariableCountResultName, new IntValue(bestSolution.Model.InputVariables.Count())));
114        results.Add(new Result(VariableImpactsResultName, CalculateVariableImpacts(variableFrequencies)));
115      }
116    }
117
118
119    private static DoubleMatrix CalculateVariableImpacts(DataTable variableFrequencies) {
120      if (variableFrequencies != null) {
121        var impacts = new DoubleMatrix(variableFrequencies.Rows.Count, 1, new string[] { "Impact" }, variableFrequencies.Rows.Select(x => x.Name));
122        impacts.SortableView = true;
123        int rowIndex = 0;
124        foreach (var dataRow in variableFrequencies.Rows) {
125          string variableName = dataRow.Name;
126          double integral = 0;
127          if (dataRow.Values.Count > 1) {
128            double baseline = dataRow.Values.First();
129            integral = (from value in dataRow.Values
130                        select value - baseline)
131                              .Sum();
132            integral /= dataRow.Values.Count;
133          }
134          impacts[rowIndex++, 0] = integral;
135        }
136        return impacts;
137      } else return new DoubleMatrix(1, 1);
138    }
139  }
140}
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