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source: branches/sluengo/HeuristicLab.Problems.TradeRules/TradeRulesProblem.cs @ 10662

Last change on this file since 10662 was 9139, checked in by sluengo, 12 years ago
File size: 6.8 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2012 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.Parameters;
26using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
27using HeuristicLab.Problems.DataAnalysis.Symbolic;
28using HeuristicLab.Problems.DataAnalysis;
29using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
30using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
31 
32
33namespace HeuristicLab.Problems.TradeRules
34{
35    [Item("TradeRules", "Represents a trade rules in a symbolic regression problem.")]
36    [StorableClass]
37    [Creatable("Problems")]
38    public class TradeRulesProblem : TradeRulesAbstractProblem<IRegressionProblemData, ISymbolicRegressionSingleObjectiveEvaluator, ISymbolicDataAnalysisSolutionCreator>, IRegressionProblem
39    {
40        private const double PunishmentFactor = 10;
41        private const int InitialMaximumTreeDepth = 8;
42        private const int InitialMaximumTreeLength = 25;
43        private const string EstimationLimitsParameterName = "EstimationLimits";
44        private const string EstimationLimitsParameterDescription = "The limits for the estimated value that can be returned by the symbolic regression model.";
45
46        #region parameter properties
47        public IFixedValueParameter<DoubleLimit> EstimationLimitsParameter
48        {
49            get { return (IFixedValueParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
50        }
51        #endregion
52        #region properties
53        public DoubleLimit EstimationLimits
54        {
55            get { return EstimationLimitsParameter.Value; }
56        }
57        #endregion
58        [StorableConstructor]
59        protected TradeRulesProblem(bool deserializing) : base(deserializing) { }
60        protected TradeRulesProblem(TradeRulesProblem original, Cloner cloner)
61            : base(original, cloner)
62        {
63            RegisterEventHandlers();
64        }
65        public override IDeepCloneable Clone(Cloner cloner) { return new TradeRulesProblem(this, cloner); }
66
67        public TradeRulesProblem()
68            : base(new RegressionProblemData(), new EvaluatorTradeRules(), new SymbolicDataAnalysisExpressionTreeCreator())
69        {
70            Parameters.Add(new FixedValueParameter<DoubleLimit>(EstimationLimitsParameterName, EstimationLimitsParameterDescription));
71
72            EstimationLimitsParameter.Hidden = true;
73
74            Maximization.Value = true;
75            MaximumSymbolicExpressionTreeDepth.Value = InitialMaximumTreeDepth;
76            MaximumSymbolicExpressionTreeLength.Value = InitialMaximumTreeLength;
77
78            RegisterEventHandlers();
79            ConfigureGrammarSymbols();
80            InitializeOperators();
81            UpdateEstimationLimits();
82        }
83
84        [StorableHook(HookType.AfterDeserialization)]
85        private void AfterDeserialization()
86        {
87            RegisterEventHandlers();
88            // compatibility
89            bool changed = false;
90            if (!Operators.OfType<SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer>().Any())
91            {
92                Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
93                changed = true;
94            }
95            if (!Operators.OfType<SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer>().Any())
96            {
97                Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
98                changed = true;
99            }
100            if (changed)
101            {
102                ParameterizeOperators();
103            }
104        }
105
106        private void RegisterEventHandlers()
107        {
108            SymbolicExpressionTreeGrammarParameter.ValueChanged += (o, e) => ConfigureGrammarSymbols();
109        }
110
111        private void ConfigureGrammarSymbols()
112        {
113            var grammar = SymbolicExpressionTreeGrammar as Grammar;
114            if (grammar != null) grammar.ConfigureAsDefaultRegressionGrammar();
115        }
116
117        private void InitializeOperators()
118        {
119            Operators.Add(new SymbolicRegressionSingleObjectiveTrainingBestSolutionAnalyzer());
120            Operators.Add(new SymbolicRegressionSingleObjectiveValidationBestSolutionAnalyzer());
121            Operators.Add(new SymbolicRegressionSingleObjectiveOverfittingAnalyzer());
122            Operators.Add(new SymbolicRegressionSingleObjectiveTrainingParetoBestSolutionAnalyzer());
123            Operators.Add(new SymbolicRegressionSingleObjectiveValidationParetoBestSolutionAnalyzer());
124
125            ParameterizeOperators();
126        }
127
128        private void UpdateEstimationLimits()
129        {
130            if (ProblemData.TrainingIndices.Any())
131            {
132                var targetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
133                var mean = targetValues.Average();
134                var range = targetValues.Max() - targetValues.Min();
135                EstimationLimits.Upper = mean + PunishmentFactor * range;
136                EstimationLimits.Lower = mean - PunishmentFactor * range;
137            }
138            else
139            {
140                EstimationLimits.Upper = double.MaxValue;
141                EstimationLimits.Lower = double.MinValue;
142            }
143        }
144
145        protected override void OnProblemDataChanged()
146        {
147            base.OnProblemDataChanged();
148            UpdateEstimationLimits();
149        }
150
151        protected override void ParameterizeOperators()
152        {
153            base.ParameterizeOperators();
154            if (Parameters.ContainsKey(EstimationLimitsParameterName))
155            {
156                var operators = Parameters.OfType<IValueParameter>().Select(p => p.Value).OfType<IOperator>().Union(Operators);
157                foreach (var op in operators.OfType<ISymbolicDataAnalysisBoundedOperator>())
158                {
159                    op.EstimationLimitsParameter.ActualName = EstimationLimitsParameter.Name;
160                }
161            }
162        }
163    }
164}
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