[12029] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[14186] | 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[12029] | 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 |
|
---|
[12075] | 22 | using System.Collections.Generic;
|
---|
[12029] | 23 | using System.Linq;
|
---|
| 24 | using HeuristicLab.Analysis;
|
---|
| 25 | using HeuristicLab.Common;
|
---|
| 26 | using HeuristicLab.Core;
|
---|
| 27 | using HeuristicLab.Data;
|
---|
| 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
|
---|
[12075] | 29 | using HeuristicLab.Optimization;
|
---|
[12029] | 30 | using HeuristicLab.Parameters;
|
---|
| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
|
---|
| 32 |
|
---|
[12049] | 33 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Classification {
|
---|
[12030] | 34 | [Item("SymbolicClassificationPhenotypicDiversityAnalyzer", "An analyzer which calculates diversity based on the phenotypic distance between trees")]
|
---|
[12029] | 35 | [StorableClass]
|
---|
[12103] | 36 | public class SymbolicClassificationPhenotypicDiversityAnalyzer : PopulationSimilarityAnalyzer,
|
---|
[12706] | 37 | ISymbolicDataAnalysisBoundedOperator, ISymbolicDataAnalysisInterpreterOperator, ISymbolicExpressionTreeAnalyzer {
|
---|
[12029] | 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";
|
---|
[12049] | 43 | private const string ModelCreatorParameterName = "ModelCreator";
|
---|
[12029] | 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 | }
|
---|
[12049] | 58 | public ILookupParameter<ISymbolicDiscriminantFunctionClassificationModelCreator> ModelCreatorParameter {
|
---|
| 59 | get { return (ILookupParameter<ISymbolicDiscriminantFunctionClassificationModelCreator>)Parameters[ModelCreatorParameterName]; }
|
---|
| 60 | }
|
---|
[12029] | 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
|
---|
[12064] | 73 | public bool UseClassValues {
|
---|
[12029] | 74 | get { return UseClassValuesParameter.Value.Value; }
|
---|
| 75 | set { UseClassValuesParameter.Value.Value = value; }
|
---|
| 76 | }
|
---|
| 77 | #endregion
|
---|
| 78 |
|
---|
[12086] | 79 | public SymbolicClassificationPhenotypicDiversityAnalyzer(IEnumerable<ISolutionSimilarityCalculator> validSimilarityCalculators)
|
---|
[12075] | 80 | : base(validSimilarityCalculators) {
|
---|
[12029] | 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."));
|
---|
[12049] | 86 | Parameters.Add(new LookupParameter<ISymbolicDiscriminantFunctionClassificationModelCreator>(ModelCreatorParameterName, "The model creator for creating discriminant function classification models."));
|
---|
[12029] | 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."));
|
---|
[12049] | 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)));
|
---|
[12029] | 89 | #endregion
|
---|
[12075] | 90 |
|
---|
| 91 | UpdateCounterParameter.ActualName = "PhenotypicDiversityAnalyzerUpdateCounter";
|
---|
[12103] | 92 | DiversityResultName = "Phenotypic Similarity";
|
---|
[12029] | 93 | }
|
---|
| 94 |
|
---|
| 95 | [StorableConstructor]
|
---|
| 96 | protected SymbolicClassificationPhenotypicDiversityAnalyzer(bool deserializing)
|
---|
| 97 | : base(deserializing) {
|
---|
| 98 | }
|
---|
| 99 |
|
---|
| 100 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 101 | return new SymbolicClassificationPhenotypicDiversityAnalyzer(this, cloner);
|
---|
| 102 | }
|
---|
| 103 |
|
---|
[12103] | 104 | protected SymbolicClassificationPhenotypicDiversityAnalyzer(SymbolicClassificationPhenotypicDiversityAnalyzer original, Cloner cloner)
|
---|
[12029] | 105 | : base(original, cloner) {
|
---|
| 106 | }
|
---|
| 107 |
|
---|
| 108 | public override IOperation Apply() {
|
---|
[12075] | 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;
|
---|
[12029] | 115 | }
|
---|
[12075] | 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) {
|
---|
[14027] | 127 | var model = (IDiscriminantFunctionClassificationModel)modelCreator.CreateSymbolicDiscriminantFunctionClassificationModel(problemData.TargetVariable, trees[i], interpreter, estimationLimits.Lower, estimationLimits.Upper);
|
---|
[12075] | 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 | }
|
---|
[12029] | 134 | return base.Apply();
|
---|
| 135 | }
|
---|
| 136 | }
|
---|
| 137 | }
|
---|