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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Regression/3.3/Symbolic/Analyzers/SymbolicRegressionModelQualityCalculator.cs @ 3915

Last change on this file since 3915 was 3710, checked in by gkronber, 15 years ago

Implemented reviewer comments. #893 (HeuristicLab 3.3.0 application review)
File size: 9.1 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.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Operators;
27using HeuristicLab.Optimization;
28using HeuristicLab.Parameters;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
31using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
32using HeuristicLab.Problems.DataAnalysis.Symbolic;
33using System.Collections.Generic;
34using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
35using HeuristicLab.Problems.DataAnalysis;
36using HeuristicLab.Analysis;
37using HeuristicLab.Problems.DataAnalysis.Evaluators;
38
39namespace HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Analyzers {
40  /// <summary>
41  /// "An operator to calculate the quality values of a symbolic regression solution symbolic expression tree encoding."
42  /// </summary>
43  [Item("SymbolicRegressionModelQualityCalculator", "An operator to calculate the quality values of a symbolic regression solution symbolic expression tree encoding.")]
44  [StorableClass]
45  public sealed class SymbolicRegressionModelQualityCalculator : AlgorithmOperator {
46    private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
47    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
48    private const string ProblemDataParameterName = "ProblemData";
49    private const string ValuesParameterName = "Values";
50    private const string RSQuaredQualityParameterName = "R-squared";
51    private const string MeanSquaredErrorQualityParameterName = "Mean Squared Error";
52    private const string RelativeErrorQualityParameterName = "Relative Error";
53    private const string SamplesStartParameterName = "SamplesStart";
54    private const string SamplesEndParameterName = "SamplesEnd";
55    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
56    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
57
58    #region parameter properties
59    public ILookupParameter<SymbolicExpressionTree> SymbolicExpressionTreeParameter {
60      get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
61    }
62    public IValueLookupParameter<ISymbolicExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
63      get { return (IValueLookupParameter<ISymbolicExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
64    }
65    public IValueLookupParameter<DataAnalysisProblemData> ProblemDataParameter {
66      get { return (IValueLookupParameter<DataAnalysisProblemData>)Parameters[ProblemDataParameterName]; }
67    }
68    public IValueLookupParameter<IntValue> SamplesStartParameter {
69      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesStartParameterName]; }
70    }
71    public IValueLookupParameter<IntValue> SamplesEndParameter {
72      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesEndParameterName]; }
73    }
74    public IValueLookupParameter<DoubleValue> UpperEstimationLimitParameter {
75      get { return (IValueLookupParameter<DoubleValue>)Parameters[UpperEstimationLimitParameterName]; }
76    }
77    public IValueLookupParameter<DoubleValue> LowerEstimationLimitParameter {
78      get { return (IValueLookupParameter<DoubleValue>)Parameters[LowerEstimationLimitParameterName]; }
79    }
80    public ILookupParameter<DoubleValue> RSquaredQualityParameter {
81      get { return (ILookupParameter<DoubleValue>)Parameters[RSQuaredQualityParameterName]; }
82    }
83    public ILookupParameter<DoubleValue> AverageRelativeErrorQualityParameter {
84      get { return (ILookupParameter<DoubleValue>)Parameters[RelativeErrorQualityParameterName]; }
85    }
86    public ILookupParameter<DoubleValue> MeanSquaredErrorQualityParameter {
87      get { return (ILookupParameter<DoubleValue>)Parameters[MeanSquaredErrorQualityParameterName]; }
88    }
89    #endregion
90
91    public SymbolicRegressionModelQualityCalculator()
92      : base() {
93      Parameters.Add(new LookupParameter<SymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression tree to analyze."));
94      Parameters.Add(new ValueLookupParameter<ISymbolicExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic expression tree."));
95      Parameters.Add(new ValueLookupParameter<DataAnalysisProblemData>(ProblemDataParameterName, "The problem data containing the input varaibles for the symbolic regression problem."));
96      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesStartParameterName, "The first index of the data set partition on which the model quality values should be calculated."));
97      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesEndParameterName, "The first index of the data set partition on which the model quality values should be calculated."));
98      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."));
99      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."));
100      Parameters.Add(new ValueParameter<DoubleMatrix>(ValuesParameterName, "The matrix of original target values and estimated values of the model."));
101      Parameters.Add(new ValueLookupParameter<DoubleValue>(MeanSquaredErrorQualityParameterName, "The mean squared error value of the output of the model."));
102      Parameters.Add(new ValueLookupParameter<DoubleValue>(RSQuaredQualityParameterName, "The R² correlation coefficient of the output of the model and the original target values."));
103      Parameters.Add(new ValueLookupParameter<DoubleValue>(RelativeErrorQualityParameterName, "The average relative percentage error of the output of the model."));
104     
105      #region operator initialization
106      SimpleSymbolicRegressionEvaluator simpleEvaluator = new SimpleSymbolicRegressionEvaluator();
107      SimpleRSquaredEvaluator simpleR2Evalator = new SimpleRSquaredEvaluator();
108      SimpleMeanAbsolutePercentageErrorEvaluator simpleRelErrorEvaluator = new SimpleMeanAbsolutePercentageErrorEvaluator();
109      SimpleMSEEvaluator simpleMseEvaluator = new SimpleMSEEvaluator();
110      Assigner clearValues = new Assigner();
111      #endregion
112
113      #region parameter wiring
114      simpleEvaluator.SymbolicExpressionTreeParameter.ActualName = SymbolicExpressionTreeParameter.Name;
115      simpleEvaluator.RegressionProblemDataParameter.ActualName = ProblemDataParameter.Name;
116      simpleEvaluator.SamplesStartParameter.ActualName = SamplesStartParameter.Name;
117      simpleEvaluator.SamplesEndParameter.ActualName = SamplesEndParameter.Name;
118      simpleEvaluator.LowerEstimationLimitParameter.ActualName = LowerEstimationLimitParameter.Name;
119      simpleEvaluator.UpperEstimationLimitParameter.ActualName = UpperEstimationLimitParameter.Name;
120      simpleEvaluator.SymbolicExpressionTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name;
121      simpleEvaluator.ValuesParameter.ActualName = ValuesParameterName;
122
123      simpleR2Evalator.ValuesParameter.ActualName = ValuesParameterName;
124      simpleR2Evalator.RSquaredParameter.ActualName = RSquaredQualityParameter.Name;
125
126      simpleMseEvaluator.ValuesParameter.ActualName = ValuesParameterName;
127      simpleMseEvaluator.MeanSquaredErrorParameter.ActualName = MeanSquaredErrorQualityParameter.Name;
128
129      simpleRelErrorEvaluator.ValuesParameter.ActualName = ValuesParameterName;
130      simpleRelErrorEvaluator.AverageRelativeErrorParameter.ActualName = AverageRelativeErrorQualityParameter.Name;
131
132      clearValues.LeftSideParameter.ActualName = ValuesParameterName;
133      clearValues.RightSideParameter.Value = new DoubleMatrix();
134      #endregion
135
136      #region operator graph
137      OperatorGraph.InitialOperator = simpleEvaluator;
138      simpleEvaluator.Successor = simpleR2Evalator;
139      simpleR2Evalator.Successor = simpleRelErrorEvaluator;
140      simpleRelErrorEvaluator.Successor = simpleMseEvaluator;
141      simpleMseEvaluator.Successor = clearValues;
142      clearValues.Successor = null;
143      #endregion
144
145    }
146  }
147}
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