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source: branches/PersistenceReintegration/HeuristicLab.Problems.DataAnalysis.Symbolic.Classification/3.4/SymbolicClassificationPhenotypicDiversityAnalyzer.cs @ 15018

Last change on this file since 15018 was 15018, checked in by gkronber, 7 years ago

#2520 introduced StorableConstructorFlag type for StorableConstructors

File size: 7.8 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 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.Collections.Generic;
23using System.Linq;
24using HeuristicLab.Analysis;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Optimization;
30using HeuristicLab.Parameters;
31using HeuristicLab.Persistence;
32
33namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
34  [Item("SymbolicClassificationPhenotypicDiversityAnalyzer", "An analyzer which calculates diversity based on the phenotypic distance between trees")]
35  [StorableType("77d4fe6a-242a-4f36-b30d-a81b8e8a708c")]
36  public class SymbolicClassificationPhenotypicDiversityAnalyzer : PopulationSimilarityAnalyzer,
37    ISymbolicDataAnalysisBoundedOperator, ISymbolicDataAnalysisInterpreterOperator, ISymbolicExpressionTreeAnalyzer {
38    #region parameter names
39    private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
40    private const string EvaluatedValuesParameterName = "EstimatedValues";
41    private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
42    private const string ProblemDataParameterName = "ProblemData";
43    private const string ModelCreatorParameterName = "ModelCreator";
44    private const string EstimationLimitsParameterName = "EstimationLimits";
45    private const string UseClassValuesParameterName = "UseClassValues";
46    #endregion
47
48    #region parameter properties
49    public IScopeTreeLookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
50      get { return (IScopeTreeLookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
51    }
52    private IScopeTreeLookupParameter<DoubleArray> EvaluatedValuesParameter {
53      get { return (IScopeTreeLookupParameter<DoubleArray>)Parameters[EvaluatedValuesParameterName]; }
54    }
55    public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
56      get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
57    }
58    public ILookupParameter<ISymbolicDiscriminantFunctionClassificationModelCreator> ModelCreatorParameter {
59      get { return (ILookupParameter<ISymbolicDiscriminantFunctionClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
60    }
61    public IValueLookupParameter<IClassificationProblemData> ProblemDataParameter {
62      get { return (IValueLookupParameter<IClassificationProblemData>)Parameters[ProblemDataParameterName]; }
63    }
64    public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
65      get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
66    }
67    public IFixedValueParameter<BoolValue> UseClassValuesParameter {
68      get { return (IFixedValueParameter<BoolValue>)Parameters[UseClassValuesParameterName]; }
69    }
70    #endregion
71
72    #region properties
73    public bool UseClassValues {
74      get { return UseClassValuesParameter.Value.Value; }
75      set { UseClassValuesParameter.Value.Value = value; }
76    }
77    #endregion
78
79    public SymbolicClassificationPhenotypicDiversityAnalyzer(IEnumerable<ISolutionSimilarityCalculator> validSimilarityCalculators)
80      : base(validSimilarityCalculators) {
81      #region add parameters
82      Parameters.Add(new ScopeTreeLookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic expression trees."));
83      Parameters.Add(new ScopeTreeLookupParameter<DoubleArray>(EvaluatedValuesParameterName, "Intermediate estimated values to be saved in the scopes."));
84      Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic data analysis tree."));
85      Parameters.Add(new ValueLookupParameter<IClassificationProblemData>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
86      Parameters.Add(new LookupParameter<ISymbolicDiscriminantFunctionClassificationModelCreator>(ModelCreatorParameterName, "The model creator for creating discriminant function classification models."));
87      Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The upper and lower limit that should be used as cut off value for the output values of symbolic data analysis trees."));
88      Parameters.Add(new FixedValueParameter<BoolValue>(UseClassValuesParameterName, "Specifies whether the raw estimated values of the tree or the corresponding class values should be used for similarity calculation.", new BoolValue(false)));
89      #endregion
90
91      UpdateCounterParameter.ActualName = "PhenotypicDiversityAnalyzerUpdateCounter";
92      DiversityResultName = "Phenotypic Similarity";
93    }
94
95    [StorableConstructor]
96    protected SymbolicClassificationPhenotypicDiversityAnalyzer(StorableConstructorFlag deserializing)
97      : base(deserializing) {
98    }
99
100    public override IDeepCloneable Clone(Cloner cloner) {
101      return new SymbolicClassificationPhenotypicDiversityAnalyzer(this, cloner);
102    }
103
104    protected SymbolicClassificationPhenotypicDiversityAnalyzer(SymbolicClassificationPhenotypicDiversityAnalyzer original, Cloner cloner)
105      : base(original, cloner) {
106    }
107
108    public override IOperation Apply() {
109      int updateInterval = UpdateIntervalParameter.Value.Value;
110      IntValue updateCounter = UpdateCounterParameter.ActualValue;
111
112      if (updateCounter == null) {
113        updateCounter = new IntValue(updateInterval);
114        UpdateCounterParameter.ActualValue = updateCounter;
115      }
116
117      if (updateCounter.Value == updateInterval) {
118        var trees = SymbolicExpressionTreeParameter.ActualValue;
119        var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue;
120        var problemData = ProblemDataParameter.ActualValue;
121        var ds = ProblemDataParameter.ActualValue.Dataset;
122        var rows = ProblemDataParameter.ActualValue.TrainingIndices;
123        var modelCreator = ModelCreatorParameter.ActualValue;
124        var estimationLimits = EstimationLimitsParameter.ActualValue;
125        var evaluatedValues = new ItemArray<DoubleArray>(trees.Length);
126        for (int i = 0; i < trees.Length; ++i) {
127          var model = (IDiscriminantFunctionClassificationModel)modelCreator.CreateSymbolicDiscriminantFunctionClassificationModel(problemData.TargetVariable, trees[i], interpreter, estimationLimits.Lower, estimationLimits.Upper);
128          model.RecalculateModelParameters(problemData, rows);
129          var values = UseClassValues ? model.GetEstimatedClassValues(ds, rows) : model.GetEstimatedValues(ds, rows);
130          evaluatedValues[i] = new DoubleArray(values.ToArray());
131        }
132        EvaluatedValuesParameter.ActualValue = evaluatedValues;
133      }
134      return base.Apply();
135    }
136  }
137}
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