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source: branches/DataAnalysis/HeuristicLab.Problems.DataAnalysis.MultiVariate.TimeSeriesPrognosis/3.3/Symbolic/Evaluators/SymbolicTimeSeriesPrognosisEvaluator.cs @ 9674

Last change on this file since 9674 was 5275, checked in by gkronber, 14 years ago

Merged changes from trunk to data analysis exploration branch and added fractional distance metric evaluator. #1142
File size: 10.0 KB
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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("SymbolicTimeSeriesPrognosisEvaluator", "")]
42  [StorableClass]
43  public abstract class SymbolicTimeSeriesPrognosisEvaluator : 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 properties
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    [StorableConstructor]
131    protected SymbolicTimeSeriesPrognosisEvaluator(bool deserializing) : base(deserializing) { }
132    protected SymbolicTimeSeriesPrognosisEvaluator(SymbolicTimeSeriesPrognosisEvaluator original, Cloner cloner)
133      : base(original, cloner) {
134    }
135    public SymbolicTimeSeriesPrognosisEvaluator()
136      : base() {
137      Parameters.Add(new LookupParameter<IRandom>(RandomParameterName, "A random number generator."));
138      Parameters.Add(new LookupParameter<MultiVariateDataAnalysisProblemData>(DataAnalysisProblemDataParameterName, "The data analysis problem data to use for training."));
139      Parameters.Add(new LookupParameter<ISymbolicTimeSeriesExpressionInterpreter>(TimeSeriesExpressionInterpreterParameterName, "The interpreter that should be used to evaluate the time series model represented as a symbolic expression tree."));
140      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesStartParameterName, "The first index of the data set partition to use for training."));
141      Parameters.Add(new ValueLookupParameter<IntValue>(SamplesEndParameterName, "The last index of the data set partition to use for training."));
142      Parameters.Add(new ValueLookupParameter<IntValue>(PredictionHorizontParameterName, "The number of time steps for which to create a forecast."));
143      Parameters.Add(new ValueLookupParameter<DoubleArray>(LowerEstimationLimitParameterName, "The lower limit for estimated values."));
144      Parameters.Add(new ValueLookupParameter<DoubleArray>(UpperEstimationLimitParameterName, "The upper limit for estimated values."));
145      Parameters.Add(new OptionalValueParameter<StringValue>(ConditionVariableParameterName, "The name of the condition variable indicating if a row should be considered for evaluation or not."));
146      Parameters.Add(new LookupParameter<SymbolicExpressionTree>(TimeSeriesPrognosisModelParameterName, "The time series prognosis model encoded as a symbolic expression tree."));
147      Parameters.Add(new LookupParameter<DoubleValue>(QualityParameterName, "The quality of the time series prognosis model encoded as a symbolic expression tree."));
148      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)));
149    }
150
151    public override IOperation Apply() {
152      double quality;
153      string conditionVariableName = ConditionVariableName == null ? null : ConditionVariableName.Value;
154      int nRows = (int)Math.Ceiling((SamplesEnd.Value - SamplesStart.Value) * RelativeNumberOfEvaluatedSamples.Value);
155      IEnumerable<int> rows = RandomEnumerable.SampleRandomNumbers(Random.Next(), SamplesStart.Value, SamplesEnd.Value, nRows);
156      if (conditionVariableName != null) {
157        rows = from row in rows
158               where !ProblemData.Dataset[conditionVariableName, row].IsAlmost(0.0)
159               select row;
160      }
161
162      quality = Evaluate(TimeSeriesPrognosisModel, ProblemData, TimeSeriesExpressionInterpreter,
163        rows, PredictionHorizon.Value, LowerEstimationLimit, UpperEstimationLimit);
164      QualityParameter.ActualValue = new DoubleValue(quality);
165      return base.Apply();
166    }
167
168    public abstract double Evaluate(SymbolicExpressionTree tree, MultiVariateDataAnalysisProblemData problemData,
169      ISymbolicTimeSeriesExpressionInterpreter interpreter,
170      IEnumerable<int> rows, int predictionHorizon, DoubleArray lowerEstimationLimit, DoubleArray upperEstimationLimit);
171  }
172}
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