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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis/3.3/Symbolic/Evaluators/SymbolicTimeSeriesPrognosisNormalizedMseEvaluator.cs @ 4113

Last change on this file since 4113 was 4113, checked in by gkronber, 14 years ago

Added plugin for time series prognosis. #1081
File size: 10.4 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;
23using System.Linq;
24using HeuristicLab.Common;
25using HeuristicLab.Core;
26using HeuristicLab.Data;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28using HeuristicLab.Problems.DataAnalysis.SupportVectorMachine;
29using HeuristicLab.Problems.DataAnalysis;
30using HeuristicLab.Problems.DataAnalysis.Evaluators;
31using HeuristicLab.Parameters;
32using HeuristicLab.Optimization;
33using HeuristicLab.Operators;
34using HeuristicLab.Problems.DataAnalysis.Regression.Symbolic;
35using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
36using System.Collections.Generic;
37using HeuristicLab.Problems.DataAnalysis.Regression;
38using HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic.Interfaces;
39
40namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis.Symbolic.Evaluators {
41  [Item("SymbolicTimeSeriesPrognosisNormalizedMseEvaluator", "")]
42  [StorableClass]
43  public class SymbolicTimeSeriesPrognosisNormalizedMseEvaluator : SingleSuccessorOperator, ISingleObjectiveSymbolicTimeSeriesPrognosisEvaluator {
44    private const string RandomParameterName = "Random";
45    private const string DataAnalysisProblemDataParameterName = "MultiVariateDataAnalysisProblemData";
46    private const string TimeSeriesExpressionInterpreterParameterName = "TimeSeriesExpressionInterpreter";
47    private const string TimeSeriesPrognosisModelParameterName = "TimeSeriesPrognosisModel";
48    private const string PredictionHorizontParameterName = "PredictionHorizon";
49    private const string LowerEstimationLimitParameterName = "LowerEstimationLimit";
50    private const string UpperEstimationLimitParameterName = "UpperEstimationLimit";
51    private const string ConditionVariableParameterName = "ConditionVariableName";
52    private const string SamplesStartParameterName = "SamplesStart";
53    private const string SamplesEndParameterName = "SamplesEnd";
54    private const string QualityParameterName = "Quality";
55    private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
56
57    #region parameter properties
58    public ILookupParameter<IRandom> RandomParameter {
59      get { return (ILookupParameter<IRandom>)Parameters[RandomParameterName]; }
60    }
61    public ILookupParameter<MultiVariateDataAnalysisProblemData> ProblemDataParameter {
62      get { return (ILookupParameter<MultiVariateDataAnalysisProblemData>)Parameters[DataAnalysisProblemDataParameterName]; }
63    }
64    public ILookupParameter<ISymbolicTimeSeriesExpressionInterpreter> TimeSeriesExpressionInterpreterParameter {
65      get { return (ILookupParameter<ISymbolicTimeSeriesExpressionInterpreter>)Parameters[TimeSeriesExpressionInterpreterParameterName]; }
66    }
67    public IValueLookupParameter<IntValue> PredictionHorizonParameter {
68      get { return (IValueLookupParameter<IntValue>)Parameters[PredictionHorizontParameterName]; }
69    }
70    public OptionalValueParameter<StringValue> ConditionVariableNameParameter {
71      get { return (OptionalValueParameter<StringValue>)Parameters[ConditionVariableParameterName]; }
72    }
73    public IValueLookupParameter<IntValue> SamplesStartParameter {
74      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesStartParameterName]; }
75    }
76    public IValueLookupParameter<IntValue> SamplesEndParameter {
77      get { return (IValueLookupParameter<IntValue>)Parameters[SamplesEndParameterName]; }
78    }
79    public IValueLookupParameter<DoubleArray> LowerEstimationLimitParameter {
80      get { return (IValueLookupParameter<DoubleArray>)Parameters[LowerEstimationLimitParameterName]; }
81    }
82    public IValueLookupParameter<DoubleArray> UpperEstimationLimitParameter {
83      get { return (IValueLookupParameter<DoubleArray>)Parameters[UpperEstimationLimitParameterName]; }
84    }
85    public ILookupParameter<SymbolicExpressionTree> TimeSeriesPrognosisModelParameter {
86      get { return (ILookupParameter<SymbolicExpressionTree>)Parameters[TimeSeriesPrognosisModelParameterName]; }
87    }
88    public ILookupParameter<DoubleValue> QualityParameter {
89      get { return (ILookupParameter<DoubleValue>)Parameters[QualityParameterName]; }
90    }
91    public IValueParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
92      get { return (IValueParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
93    }
94    #endregion
95    #region
96    public IRandom Random {
97      get { return RandomParameter.ActualValue; }
98    }
99    public MultiVariateDataAnalysisProblemData ProblemData {
100      get { return ProblemDataParameter.ActualValue; }
101    }
102    public ISymbolicTimeSeriesExpressionInterpreter TimeSeriesExpressionInterpreter {
103      get { return TimeSeriesExpressionInterpreterParameter.ActualValue; }
104    }
105    public IntValue PredictionHorizon {
106      get { return PredictionHorizonParameter.ActualValue; }
107    }
108    public StringValue ConditionVariableName {
109      get { return ConditionVariableNameParameter.Value; }
110    }
111    public IntValue SamplesStart {
112      get { return SamplesStartParameter.ActualValue; }
113    }
114    public IntValue SamplesEnd {
115      get { return SamplesEndParameter.ActualValue; }
116    }
117    public DoubleArray LowerEstimationLimit {
118      get { return LowerEstimationLimitParameter.ActualValue; }
119    }
120    public DoubleArray UpperEstimationLimit {
121      get { return UpperEstimationLimitParameter.ActualValue; }
122    }
123    public SymbolicExpressionTree TimeSeriesPrognosisModel {
124      get { return TimeSeriesPrognosisModelParameter.ActualValue; }
125    }
126    public PercentValue RelativeNumberOfEvaluatedSamples {
127      get { return RelativeNumberOfEvaluatedSamplesParameter.Value; }
128    }
129    #endregion
130
131    public SymbolicTimeSeriesPrognosisNormalizedMseEvaluator()
132      : base() {
133      Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "A random number generator."));
134      Parameters.Add(new LookupParameter<MultiVariateDataAnalysisProblemData>(DataAnalysisProblemDataParameterName, "The data analysis problem data to use for training."));
135      Parameters.Add(new LookupParameter<ISymbolicTimeSeriesExpressionInterpreter>(TimeSeriesExpressionInterpreterParameterName, "The interpreter that should be used to evaluate the time series model represented as a symbolic expression tree."));
136      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesStartParameterName, "The first index of the data set partition to use for training."));
137      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesEndParameterName, "The last index of the data set partition to use for training."));
138      Parameters.Add(new ValueLookupParameter<IntValue>(PredictionHorizontParameterName, "The number of time steps for which to create a forecast."));
139      Parameters.Add(new ValueLookupParameter<DoubleArray>(LowerEstimationLimitParameterName, "The lower limit for estimated values."));
140      Parameters.Add(new ValueLookupParameter<DoubleArray>(UpperEstimationLimitParameterName, "The upper limit for estimated values."));
141      Parameters.Add(new OptionalValueParameter<StringValue>(ConditionVariableParameterName, "The name of the condition variable indicating if a row should be considered for evaluation or not."));
142      Parameters.Add(new LookupParameter<SymbolicExpressionTree>(TimeSeriesPrognosisModelParameterName, "The time series prognosis model encoded as a symbolic expression tree."));
143      Parameters.Add(new LookupParameter<DoubleValue>(QualityParameterName, "The quality of the time series prognosis model encoded as a symbolic expression tree."));
144      Parameters.Add(new ValueParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index.", new PercentValue(1)));
145    }
146
147    public override IOperation Apply() {
148      double quality;
149      string conditionVariableName = ConditionVariableName == null ? null : ConditionVariableName.Value;
150      int nRows = (int)Math.Ceiling((SamplesEnd.Value - SamplesStart.Value) * RelativeNumberOfEvaluatedSamples.Value);
151      IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers((uint)Random.Next(), SamplesStart.Value, SamplesEnd.Value, nRows);
152
153      quality = Evaluate(TimeSeriesPrognosisModel, ProblemData, TimeSeriesExpressionInterpreter,
154        conditionVariableName, rows, PredictionHorizon.Value, LowerEstimationLimit, UpperEstimationLimit);
155      QualityParameter.ActualValue = new DoubleValue(quality);
156      return base.Apply();
157    }
158
159    public static double Evaluate(SymbolicExpressionTree tree, MultiVariateDataAnalysisProblemData problemData,
160      ISymbolicTimeSeriesExpressionInterpreter interpreter,
161     IEnumerable<int> rows, int predictionHorizon, DoubleArray lowerEstimationLimit, DoubleArray upperEstimationLimit) {
162      return Evaluate(tree, problemData, interpreter, null, rows, predictionHorizon, lowerEstimationLimit, upperEstimationLimit);
163    }
164
165    public static double Evaluate(SymbolicExpressionTree tree, MultiVariateDataAnalysisProblemData problemData,
166      ISymbolicTimeSeriesExpressionInterpreter interpreter, string conditionVariableName,
167      IEnumerable<int> rows, int predictionHorizon, DoubleArray lowerEstimationLimit, DoubleArray upperEstimationLimit) {
168      double[] zeros = new double[problemData.TargetVariables.CheckedItems.Count()];
169      double[] ones = Enumerable.Repeat(1.0, zeros.Length).ToArray();
170      return SymbolicTimeSeriesPrognosisScaledNormalizedMseEvaluator.Evaluate(tree, problemData, interpreter, conditionVariableName, rows,
171        predictionHorizon, lowerEstimationLimit, upperEstimationLimit, ones, zeros);
172    }
173  }
174}
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