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source: tags/3.3.5/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/SymbolicRegressionModelQualityCalculator.cs @ 13077

Last change on this file since 13077 was 5863, checked in by mkommend, 13 years ago

#1418: Added NonDiscoverableType attribute to outdated analyzers.
File size: 9.3 KB
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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 HeuristicLab.Common;
23using HeuristicLab.Core;
24using HeuristicLab.Data;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Operators;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis.Evaluators;
30using HeuristicLab.Problems.DataAnalysis.Symbolic;
31
32namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
33  /// <summary>
34  /// "An operator to calculate the quality values of a symbolic regression solution symbolic expression tree encoding."
35  /// </summary>
36  [Item("SymbolicRegressionModelQualityCalculator", "An operator to calculate the quality values of a symbolic regression solution symbolic expression tree encoding.")]
37  [StorableClass]
38  public sealed class SymbolicRegressionModelQualityCalculator : AlgorithmOperator {
39    private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
40    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
41    private const string ProblemDataParameterName = "ProblemData";
42    private const string ValuesParameterName = "Values";
43    private const string RSQuaredQualityParameterName = "R-squared";
44    private const string MeanSquaredErrorQualityParameterName = "Mean Squared Error";
45    private const string RelativeErrorQualityParameterName = "Relative Error";
46    private const string SamplesStartParameterName = "SamplesStart";
47    private const string SamplesEndParameterName = "SamplesEnd";
48    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
49    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
50
51    #region parameter properties
52    public ILookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
53      get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
54    }
55    public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
56      get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
57    }
58    public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
59      get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
60    }
61    public IValueLookupParameter<IntValue> SamplesStartParameter {
62      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesStartParameterName]; }
63    }
64    public IValueLookupParameter<IntValue> SamplesEndParameter {
65      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesEndParameterName]; }
66    }
67    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
68      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
69    }
70    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
71      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
72    }
73    public ILookupParameter<DoubleValue> RSquaredQualityParameter {
74      get { return (ILookupParameter<DoubleValue>)Parameters[RSQuaredQualityParameterName]; }
75    }
76    public ILookupParameter<DoubleValue> AverageRelativeErrorQualityParameter {
77      get { return (ILookupParameter<DoubleValue>)Parameters[RelativeErrorQualityParameterName]; }
78    }
79    public ILookupParameter<DoubleValue> MeanSquaredErrorQualityParameter {
80      get { return (ILookupParameter<DoubleValue>)Parameters[MeanSquaredErrorQualityParameterName]; }
81    }
82    #endregion
83
84    [StorableConstructor]
85    private SymbolicRegressionModelQualityCalculator(bool deserializing) : base(deserializing) { }
86    private SymbolicRegressionModelQualityCalculator(SymbolicRegressionModelQualityCalculator original, Cloner cloner) : base(original, cloner) { }
87    public SymbolicRegressionModelQualityCalculator()
88      : base() {
89      Parameters.Add(new LookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression tree to analyze."));
90      Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic expression tree."));
91      Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data containing the input varaibles for the symbolic regression problem."));
92      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesStartParameterName, "The first index of the data set partition on which the model quality values should be calculated."));
93      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesEndParameterName, "The first index of the data set partition on which the model quality values should be calculated."));
94      Parameters.Add(new ValueLookupParameter<DoubleValue>(UpperEstimationLimitParameterName, "The upper limit that should be used as cut off value for the output values of symbolic expression trees."));
95      Parameters.Add(new ValueLookupParameter<DoubleValue>(LowerEstimationLimitParameterName, "The lower limit that should be used as cut off value for the output values of symbolic expression trees."));
96      Parameters.Add(new ValueParameter<DoubleMatrix>(ValuesParameterName, "The matrix of original target values and estimated values of the model."));
97      Parameters.Add(new ValueLookupParameter<DoubleValue>(MeanSquaredErrorQualityParameterName, "The mean squared error value of the output of the model."));
98      Parameters.Add(new ValueLookupParameter<DoubleValue>(RSQuaredQualityParameterName, "The R² correlation coefficient of the output of the model and the original target values."));
99      Parameters.Add(new ValueLookupParameter<DoubleValue>(RelativeErrorQualityParameterName, "The average relative percentage error of the output of the model."));
100
101      #region operator initialization
102      SimpleSymbolicRegressionEvaluator simpleEvaluator = new SimpleSymbolicRegressionEvaluator();
103      SimpleRSquaredEvaluator simpleR2Evalator = new SimpleRSquaredEvaluator();
104      SimpleMeanAbsolutePercentageErrorEvaluator simpleRelErrorEvaluator = new SimpleMeanAbsolutePercentageErrorEvaluator();
105      SimpleMSEEvaluator simpleMseEvaluator = new SimpleMSEEvaluator();
106      Assigner clearValues = new Assigner();
107      #endregion
108
109      #region parameter wiring
110      simpleEvaluator.SymbolicExpressionTreeParameter.ActualName = SymbolicExpressionTreeParameter.Name;
111      simpleEvaluator.RegressionProblemDataParameter.ActualName = ProblemDataParameter.Name;
112      simpleEvaluator.SamplesStartParameter.ActualName = SamplesStartParameter.Name;
113      simpleEvaluator.SamplesEndParameter.ActualName = SamplesEndParameter.Name;
114      simpleEvaluator.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameter.Name;
115      simpleEvaluator.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameter.Name;
116      simpleEvaluator.SymbolicExpressionTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name;
117      simpleEvaluator.ValuesParameter.ActualName = ValuesParameterName;
118
119      simpleR2Evalator.ValuesParameter.ActualName = ValuesParameterName;
120      simpleR2Evalator.RSquaredParameter.ActualName = RSquaredQualityParameter.Name;
121
122      simpleMseEvaluator.ValuesParameter.ActualName = ValuesParameterName;
123      simpleMseEvaluator.MeanSquaredErrorParameter.ActualName = MeanSquaredErrorQualityParameter.Name;
124
125      simpleRelErrorEvaluator.ValuesParameter.ActualName = ValuesParameterName;
126      simpleRelErrorEvaluator.AverageRelativeErrorParameter.ActualName = AverageRelativeErrorQualityParameter.Name;
127
128      clearValues.LeftSideParameter.ActualName = ValuesParameterName;
129      clearValues.RightSideParameter.Value = new DoubleMatrix();
130      #endregion
131
132      #region operator graph
133      OperatorGraph.InitialOperator = simpleEvaluator;
134      simpleEvaluator.Successor = simpleR2Evalator;
135      simpleR2Evalator.Successor = simpleRelErrorEvaluator;
136      simpleRelErrorEvaluator.Successor = simpleMseEvaluator;
137      simpleMseEvaluator.Successor = clearValues;
138      clearValues.Successor = null;
139      #endregion
140
141    }
142    public override IDeepCloneable Clone(Cloner cloner) {
143      return new SymbolicRegressionModelQualityCalculator(this, cloner);
144    }
145  }
146}
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