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source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator.cs @ 7100

Last change on this file since 7100 was 7100, checked in by gkronber, 12 years ago

#1081 worked on multi-variate time series prognosis

File size: 4.2 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 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.Collections.Generic;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
30  [Item("Pearson R² Evaluator", "Calculates the square of the pearson correlation coefficient (also known as coefficient of determination) of a symbolic time-series prognosis solution.")]
31  [StorableClass]
32  public class SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator : SymbolicTimeSeriesPrognosisSingleObjectiveEvaluator {
33    [StorableConstructor]
34    protected SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
35    protected SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
36      : base(original, cloner) {
37    }
38    public override IDeepCloneable Clone(Cloner cloner) {
39      return new SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(this, cloner);
40    }
41
42    public SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator() : base() { }
43
44    public override bool Maximization { get { return true; } }
45
46    public override IOperation Apply() {
47      var solution = SymbolicExpressionTreeParameter.ActualValue;
48      IEnumerable<int> rows = GenerateRowsToEvaluate();
49
50      double quality = Calculate(SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, rows);
51      QualityParameter.ActualValue = new DoubleValue(quality);
52
53      return base.Apply();
54    }
55
56    public static double Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
57      IEnumerable<double> estimatedValues = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, rows);
58      var meanCalculator = new OnlineMeanAndVarianceCalculator();
59      foreach (var targetVariable in problemData.TargetVariables) {
60        IEnumerable<double> originalValues = problemData.Dataset.GetDoubleValues(targetVariable, rows);
61        OnlineCalculatorError errorState;
62        meanCalculator.Add(OnlinePearsonsRSquaredCalculator.Calculate(estimatedValues, originalValues, out errorState));
63        if (errorState != OnlineCalculatorError.None) return 0.0;
64      }
65      return meanCalculator.Mean;
66    }
67
68    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
69      SymbolicTimeSeriesPrognosisInterpreterParameter.ExecutionContext = context;
70      EstimationLimitsParameter.ExecutionContext = context;
71
72      double r2 = Calculate(SymbolicTimeSeriesPrognosisInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows);
73
74      SymbolicTimeSeriesPrognosisInterpreterParameter.ExecutionContext = null;
75      EstimationLimitsParameter.ExecutionContext = null;
76
77      return r2;
78    }
79  }
80}
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