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source: stable/HeuristicLab.Problems.GrammaticalEvolution/3.4/SymbolicRegression/GESymbolicDataAnalysisProblem.cs @ 13531

Last change on this file since 13531 was 12012, checked in by ascheibe, 10 years ago

#2212 merged r12008, r12009, r12010 back into trunk

File size: 17.1 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 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 * Author: Sabine Winkler
21 */
22
23#endregion
24
25using System;
26using System.Drawing;
27using System.Linq;
28using HeuristicLab.Common;
29using HeuristicLab.Common.Resources;
30using HeuristicLab.Core;
31using HeuristicLab.Data;
32using HeuristicLab.Encodings.IntegerVectorEncoding;
33using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
34using HeuristicLab.Optimization;
35using HeuristicLab.Parameters;
36using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
37using HeuristicLab.PluginInfrastructure;
38using HeuristicLab.Problems.DataAnalysis;
39using HeuristicLab.Problems.DataAnalysis.Symbolic;
40using HeuristicLab.Problems.GrammaticalEvolution.Mappers;
41using HeuristicLab.Problems.Instances;
42
43namespace HeuristicLab.Problems.GrammaticalEvolution {
44  [StorableClass]
45  public abstract class GESymbolicDataAnalysisProblem<T, U, V> : HeuristicOptimizationProblem<U, V>, IDataAnalysisProblem<T>,
46                                                                 IGESymbolicDataAnalysisProblem, IStorableContent,
47                                                                 IProblemInstanceConsumer<T>, IProblemInstanceExporter<T>
48    where T : class, IDataAnalysisProblemData
49    where U : class, IGESymbolicDataAnalysisEvaluator<T>
50    where V : class, IIntegerVectorCreator {
51
52    #region parameter names & descriptions
53    private const string ProblemDataParameterName = "ProblemData";
54    private const string SymbolicExpressionTreeGrammarParameterName = "SymbolicExpressionTreeGrammar";
55    private const string SymbolicExpressionTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
56    private const string MaximumSymbolicExpressionTreeLengthParameterName = "MaximumSymbolicExpressionTreeLength";
57    private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
58    private const string FitnessCalculationPartitionParameterName = "FitnessCalculationPartition";
59    private const string ValidationPartitionParameterName = "ValidationPartition";
60    private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
61    private const string BoundsParameterName = "Bounds";
62    private const string GenotypeToPhenotypeMapperParameterName = "GenotypeToPhenotypeMapper";
63    private const string ProblemDataParameterDescription = "";
64    private const string SymbolicExpressionTreeGrammarParameterDescription = "The grammar that should be used for symbolic expression tree.";
65    private const string SymbolicExpressionTreeInterpreterParameterDescription = "The interpreter that should be used to evaluate the symbolic expression tree.";
66    private const string MaximumSymbolicExpressionTreeLengthParameterDescription = "Maximal length of the symbolic expression.";
67    private const string RelativeNumberOfEvaluatedSamplesParameterDescription = "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation.";
68    private const string FitnessCalculationPartitionParameterDescription = "The partition of the problem data training partition, that should be used to calculate the fitness of an individual.";
69    private const string ValidationPartitionParameterDescription = "The partition of the problem data training partition, that should be used to select the best model from (optional).";
70    private const string ApplyLinearScalingParameterDescription = "Flag that indicates if the individual should be linearly scaled before evaluating.";
71    private const string BoundsParameterDescription = "The integer number range in which the single genomes of a genotype are created.";
72    private const string GenotypeToPhenotypeMapperParameterDescription = "Maps the genotype (an integer vector) to the phenotype (a symbolic expression tree).";
73    #endregion
74
75    #region parameter properties
76    IParameter IDataAnalysisProblem.ProblemDataParameter {
77      get { return ProblemDataParameter; }
78    }
79    public IValueParameter<T> ProblemDataParameter {
80      get { return (IValueParameter<T>)Parameters[ProblemDataParameterName]; }
81    }
82    public IValueParameter<ISymbolicDataAnalysisGrammar> SymbolicExpressionTreeGrammarParameter {
83      get { return (IValueParameter<ISymbolicDataAnalysisGrammar>)Parameters[SymbolicExpressionTreeGrammarParameterName]; }
84    }
85    public IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicExpressionTreeInterpreterParameter {
86      get { return (IValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicExpressionTreeInterpreterParameterName]; }
87    }
88    public IFixedValueParameter<IntValue> MaximumSymbolicExpressionTreeLengthParameter {
89      get { return (IFixedValueParameter<IntValue>)Parameters[MaximumSymbolicExpressionTreeLengthParameterName]; }
90    }
91    public IFixedValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
92      get { return (IFixedValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
93    }
94    public IFixedValueParameter<IntRange> FitnessCalculationPartitionParameter {
95      get { return (IFixedValueParameter<IntRange>)Parameters[FitnessCalculationPartitionParameterName]; }
96    }
97    public IFixedValueParameter<IntRange> ValidationPartitionParameter {
98      get { return (IFixedValueParameter<IntRange>)Parameters[ValidationPartitionParameterName]; }
99    }
100    public IFixedValueParameter<BoolValue> ApplyLinearScalingParameter {
101      get { return (IFixedValueParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
102    }
103    public IValueParameter<IntMatrix> BoundsParameter {
104      get { return (IValueParameter<IntMatrix>)Parameters[BoundsParameterName]; }
105    }
106    public IValueParameter<IGenotypeToPhenotypeMapper> GenotypeToPhenotypeMapperParameter {
107      get { return (IValueParameter<IGenotypeToPhenotypeMapper>)Parameters[GenotypeToPhenotypeMapperParameterName]; }
108    }
109    #endregion
110
111    #region properties
112    public string Filename { get; set; }
113    public static new Image StaticItemImage { get { return VSImageLibrary.Type; } }
114
115    IDataAnalysisProblemData IDataAnalysisProblem.ProblemData {
116      get { return ProblemData; }
117    }
118    public T ProblemData {
119      get { return ProblemDataParameter.Value; }
120      set { ProblemDataParameter.Value = value; }
121    }
122
123    public ISymbolicDataAnalysisGrammar SymbolicExpressionTreeGrammar {
124      get { return SymbolicExpressionTreeGrammarParameter.Value; }
125      set { SymbolicExpressionTreeGrammarParameter.Value = value; }
126    }
127    public ISymbolicDataAnalysisExpressionTreeInterpreter SymbolicExpressionTreeInterpreter {
128      get { return SymbolicExpressionTreeInterpreterParameter.Value; }
129      set { SymbolicExpressionTreeInterpreterParameter.Value = value; }
130    }
131
132    public IntValue MaximumSymbolicExpressionTreeLength {
133      get { return MaximumSymbolicExpressionTreeLengthParameter.Value; }
134    }
135
136    public PercentValue RelativeNumberOfEvaluatedSamples {
137      get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
138    }
139
140    public IntRange FitnessCalculationPartition {
141      get { return FitnessCalculationPartitionParameter.Value; }
142    }
143    public IntRange ValidationPartition {
144      get { return ValidationPartitionParameter.Value; }
145    }
146    public BoolValue ApplyLinearScaling {
147      get { return ApplyLinearScalingParameter.Value; }
148    }
149    #endregion
150
151    [StorableConstructor]
152    protected GESymbolicDataAnalysisProblem(bool deserializing) : base(deserializing) { }
153    [StorableHook(HookType.AfterDeserialization)]
154    private void AfterDeserialization() {
155      RegisterEventHandlers();
156    }
157    protected GESymbolicDataAnalysisProblem(GESymbolicDataAnalysisProblem<T, U, V> original, Cloner cloner)
158      : base(original, cloner) {
159      RegisterEventHandlers();
160    }
161
162    protected GESymbolicDataAnalysisProblem(T problemData, U evaluator, V solutionCreator)
163      : base(evaluator, solutionCreator) {
164      Parameters.Add(new ValueParameter<T>(ProblemDataParameterName, ProblemDataParameterDescription, problemData));
165      Parameters.Add(new ValueParameter<ISymbolicDataAnalysisGrammar>(SymbolicExpressionTreeGrammarParameterName, SymbolicExpressionTreeGrammarParameterDescription));
166      Parameters.Add(new ValueParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicExpressionTreeInterpreterParameterName, SymbolicExpressionTreeInterpreterParameterDescription));
167      Parameters.Add(new FixedValueParameter<IntValue>(MaximumSymbolicExpressionTreeLengthParameterName, MaximumSymbolicExpressionTreeLengthParameterDescription));
168      Parameters.Add(new FixedValueParameter<IntRange>(FitnessCalculationPartitionParameterName, FitnessCalculationPartitionParameterDescription));
169      Parameters.Add(new FixedValueParameter<IntRange>(ValidationPartitionParameterName, ValidationPartitionParameterDescription));
170      Parameters.Add(new FixedValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, RelativeNumberOfEvaluatedSamplesParameterDescription, new PercentValue(1)));
171      Parameters.Add(new FixedValueParameter<BoolValue>(ApplyLinearScalingParameterName, ApplyLinearScalingParameterDescription, new BoolValue(false)));
172      IntMatrix m = new IntMatrix(new int[,] { { 0, 100 } });
173      Parameters.Add(new ValueParameter<IntMatrix>(BoundsParameterName, BoundsParameterDescription, m));
174      Parameters.Add(new ValueParameter<IGenotypeToPhenotypeMapper>(GenotypeToPhenotypeMapperParameterName, GenotypeToPhenotypeMapperParameterDescription, new DepthFirstMapper()));
175
176      SymbolicExpressionTreeInterpreterParameter.Hidden = true;
177      ApplyLinearScalingParameter.Hidden = true;
178
179      SymbolicExpressionTreeGrammar = new GESymbolicExpressionGrammar(problemData.AllowedInputVariables, problemData.AllowedInputVariables.Count() * 3);
180      SymbolicExpressionTreeInterpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();
181
182      FitnessCalculationPartition.Start = ProblemData.TrainingPartition.Start;
183      FitnessCalculationPartition.End = ProblemData.TrainingPartition.End;
184
185      InitializeOperators();
186
187      UpdateGrammar();
188      RegisterEventHandlers();
189    }
190
191    private void DeregisterGrammarHandler() {
192      SymbolicExpressionTreeGrammarParameter.ValueChanged -= SymbolicExpressionTreeGrammarParameter_ValueChanged;
193    }
194    private void RegisterGrammarHandler() {
195      SymbolicExpressionTreeGrammarParameter.ValueChanged += SymbolicExpressionTreeGrammarParameter_ValueChanged;
196    }
197
198    private void UpdateGrammar() {
199      DeregisterGrammarHandler();
200      // create a new grammar instance with the correct allowed input variables
201      SymbolicExpressionTreeGrammarParameter.Value =
202        new GESymbolicExpressionGrammar(ProblemData.AllowedInputVariables, ProblemData.AllowedInputVariables.Count() * 3);
203      RegisterGrammarHandler();
204    }
205
206    private void InitializeOperators() {
207      Operators.AddRange(ApplicationManager.Manager.GetInstances<IIntegerVectorOperator>());
208      Operators.Add(new SymbolicExpressionSymbolFrequencyAnalyzer());
209      Operators.Add(new SymbolicDataAnalysisVariableFrequencyAnalyzer());
210      Operators.Add(new MinAverageMaxSymbolicExpressionTreeLengthAnalyzer());
211      Operators.Add(new SymbolicExpressionTreeLengthAnalyzer());
212      ParameterizeOperators();
213    }
214
215    #region events
216    private void RegisterEventHandlers() {
217      ProblemDataParameter.ValueChanged += new EventHandler(ProblemDataParameter_ValueChanged);
218      ProblemDataParameter.Value.Changed += (object sender, EventArgs e) => OnProblemDataChanged();
219
220      RegisterGrammarHandler();
221    }
222
223    private void ProblemDataParameter_ValueChanged(object sender, EventArgs e) {
224      ValidationPartition.Start = 0;
225      ValidationPartition.End = 0;
226      ProblemDataParameter.Value.Changed += (object s, EventArgs args) => OnProblemDataChanged();
227      OnProblemDataChanged();
228    }
229
230    private void SymbolicExpressionTreeGrammarParameter_ValueChanged(object sender, EventArgs e) {
231      UpdateGrammar();
232    }
233
234    protected override void OnEvaluatorChanged() {
235      base.OnEvaluatorChanged();
236      Evaluator.SymbolicExpressionTreeParameter.ActualNameChanged += new EventHandler(Evaluator_SymbolicExpressionTreeParameter_ActualNameChanged);
237      ParameterizeOperators();
238    }
239
240    private void Evaluator_SymbolicExpressionTreeParameter_ActualNameChanged(object sender, EventArgs e) {
241      ParameterizeOperators();
242    }
243
244    public event EventHandler ProblemDataChanged;
245    protected virtual void OnProblemDataChanged() {
246      FitnessCalculationPartition.Start = ProblemData.TrainingPartition.Start;
247      FitnessCalculationPartition.End = ProblemData.TrainingPartition.End;
248
249      UpdateGrammar();
250      ParameterizeOperators();
251
252      var handler = ProblemDataChanged;
253      if (handler != null) handler(this, EventArgs.Empty);
254
255      OnReset();
256    }
257    #endregion
258
259    protected virtual void ParameterizeOperators() {
260      var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators).ToList();
261
262      foreach (var op in operators.OfType<ISymbolicExpressionTreeGrammarBasedOperator>()) {
263        op.SymbolicExpressionTreeGrammarParameter.ActualName = SymbolicExpressionTreeGrammarParameter.Name;
264      }
265      foreach (var op in operators.OfType<IGESymbolicDataAnalysisEvaluator<T>>()) {
266        op.ProblemDataParameter.ActualName = ProblemDataParameterName;
267        op.SymbolicExpressionTreeParameter.ActualName = Evaluator.SymbolicExpressionTreeParameter.ActualName;
268        op.EvaluationPartitionParameter.ActualName = FitnessCalculationPartitionParameter.Name;
269        op.RelativeNumberOfEvaluatedSamplesParameter.ActualName = RelativeNumberOfEvaluatedSamplesParameter.Name;
270        op.ApplyLinearScalingParameter.ActualName = ApplyLinearScalingParameter.Name;
271        op.IntegerVectorParameter.ActualName = SolutionCreator.IntegerVectorParameter.Name;
272        op.GenotypeToPhenotypeMapperParameter.ActualName = GenotypeToPhenotypeMapperParameter.Name;
273        op.SymbolicExpressionTreeGrammarParameter.ActualName = SymbolicExpressionTreeGrammarParameter.Name;
274      }
275      foreach (var op in operators.OfType<IIntegerVectorCrossover>()) {
276        op.ParentsParameter.ActualName = SolutionCreator.IntegerVectorParameter.ActualName;
277        op.ChildParameter.ActualName = SolutionCreator.IntegerVectorParameter.ActualName;
278      }
279      foreach (var op in operators.OfType<IIntegerVectorManipulator>()) {
280        op.IntegerVectorParameter.ActualName = SolutionCreator.IntegerVectorParameter.ActualName;
281      }
282      foreach (var op in operators.OfType<IIntegerVectorCreator>()) {
283        op.BoundsParameter.ActualName = BoundsParameter.Name;
284        op.LengthParameter.ActualName = MaximumSymbolicExpressionTreeLengthParameter.Name;
285      }
286      foreach (var op in operators.OfType<ISymbolicExpressionTreeAnalyzer>()) {
287        op.SymbolicExpressionTreeParameter.ActualName = Evaluator.SymbolicExpressionTreeParameter.ActualName;
288      }
289      foreach (var op in operators.OfType<ISymbolicDataAnalysisSingleObjectiveAnalyzer>()) {
290        op.ApplyLinearScalingParameter.ActualName = ApplyLinearScalingParameter.Name;
291      }
292      foreach (var op in operators.OfType<ISymbolicDataAnalysisMultiObjectiveAnalyzer>()) {
293        op.ApplyLinearScalingParameter.ActualName = ApplyLinearScalingParameter.Name;
294      }
295      foreach (var op in operators.OfType<ISymbolicDataAnalysisAnalyzer>()) {
296        op.SymbolicExpressionTreeParameter.ActualName = Evaluator.SymbolicExpressionTreeParameter.ActualName;
297      }
298      foreach (var op in operators.OfType<IGESymbolicDataAnalysisValidationAnalyzer<U, T>>()) {
299        op.RelativeNumberOfEvaluatedSamplesParameter.ActualName = RelativeNumberOfEvaluatedSamplesParameter.Name;
300        op.ValidationPartitionParameter.ActualName = ValidationPartitionParameter.Name;
301      }
302      foreach (var op in operators.OfType<ISymbolicDataAnalysisInterpreterOperator>()) {
303        op.SymbolicDataAnalysisTreeInterpreterParameter.ActualName = SymbolicExpressionTreeInterpreterParameter.Name;
304      }
305    }
306
307    #region Import & Export
308    public virtual void Load(T data) {
309      Name = data.Name;
310      Description = data.Description;
311      ProblemData = data;
312    }
313
314    public virtual T Export() {
315      return ProblemData;
316    }
317    #endregion
318  }
319}
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