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source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SingleObjective/SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator.cs @ 7120

Last change on this file since 7120 was 7120, checked in by gkronber, 12 years ago

#1081 implemented multi-variate symbolic expression tree interpreter for time series prognosis.

File size: 4.9 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;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
30
31namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
32  [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.")]
33  [StorableClass]
34  public class SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator : SymbolicTimeSeriesPrognosisSingleObjectiveEvaluator {
35    [StorableConstructor]
36    protected SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
37    protected SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
38      : base(original, cloner) {
39    }
40    public override IDeepCloneable Clone(Cloner cloner) {
41      return new SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(this, cloner);
42    }
43
44    public SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator() : base() { }
45
46    public override bool Maximization { get { return true; } }
47
48    public override IOperation Apply() {
49      var solution = SymbolicExpressionTreeParameter.ActualValue;
50      IEnumerable<int> rows = GenerateRowsToEvaluate();
51
52      double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution,
53        EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
54        ProblemDataParameter.ActualValue,
55        rows, HorizonParameter.ActualValue.Value);
56      QualityParameter.ActualValue = new DoubleValue(quality);
57
58      return base.Apply();
59    }
60
61    public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows, int horizon) {
62      var allPredictedContinuations =
63        interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, problemData.TargetVariables.ToArray(),
64                                                    rows, horizon).ToArray();
65
66      var meanCalculator = new OnlineMeanAndVarianceCalculator();
67      int i = 0;
68      foreach (var targetVariable in problemData.TargetVariables) {
69        var actualContinuations = from r in rows
70                                  select problemData.Dataset.GetDoubleValues(targetVariable, Enumerable.Range(r, horizon));
71        var startValues = problemData.Dataset.GetDoubleValues(targetVariable, rows.Select(r => r - 1));
72        OnlineCalculatorError errorState;
73        meanCalculator.Add(OnlineTheilsUStatisticCalculator.Calculate(
74          startValues,
75          allPredictedContinuations.Select(v => v.ElementAt(i)),
76          actualContinuations, out errorState));
77        if (errorState != OnlineCalculatorError.None) return double.NaN;
78        i++;
79      }
80      return meanCalculator.Mean;
81    }
82
83    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
84      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
85      EstimationLimitsParameter.ExecutionContext = context;
86      HorizonParameter.ExecutionContext = context;
87
88      double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, HorizonParameter.ActualValue.Value);
89
90      HorizonParameter.ExecutionContext = null;
91      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
92      EstimationLimitsParameter.ExecutionContext = null;
93
94      return r2;
95    }
96  }
97}
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