[8798] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8798] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 30 |
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| 31 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
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| 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.")]
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| 33 | [StorableClass]
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| 34 | public class SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator : SymbolicTimeSeriesPrognosisSingleObjectiveEvaluator {
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| 35 | [StorableConstructor]
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| 36 | protected SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(bool deserializing) : base(deserializing) { }
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| 37 | protected SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner)
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| 38 | : base(original, cloner) {
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| 39 | }
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| 40 | public override IDeepCloneable Clone(Cloner cloner) {
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| 41 | return new SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator(this, cloner);
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| 42 | }
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| 43 |
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| 44 | public SymbolicTimeSeriesPrognosisSingleObjectivePearsonRSquaredEvaluator() : base() { }
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| 45 |
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| 46 | public override bool Maximization { get { return true; } }
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| 47 |
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| 48 | public override IOperation Apply() {
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| 49 | var solution = SymbolicExpressionTreeParameter.ActualValue;
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| 50 | IEnumerable<int> rows = GenerateRowsToEvaluate();
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| 51 |
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| 52 | double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution,
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| 53 | EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
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| 54 | ProblemDataParameter.ActualValue,
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| 55 | rows, HorizonParameter.ActualValue.Value);
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| 56 | QualityParameter.ActualValue = new DoubleValue(quality);
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| 57 |
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| 58 | return base.Apply();
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| 59 | }
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| 60 |
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| 61 | public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows, int horizon) {
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| 62 | var allPredictedContinuations =
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| 63 | interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, problemData.TargetVariables.ToArray(),
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| 64 | rows, horizon).ToArray();
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| 65 |
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| 66 | var meanCalculator = new OnlineMeanAndVarianceCalculator();
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| 67 | int i = 0;
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| 68 | foreach (var targetVariable in problemData.TargetVariables) {
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| 69 | var actualContinuations = from r in rows
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| 70 | select problemData.Dataset.GetDoubleValues(targetVariable, Enumerable.Range(r, horizon));
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| 71 | var startValues = problemData.Dataset.GetDoubleValues(targetVariable, rows.Select(r => r - 1));
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| 72 | OnlineCalculatorError errorState;
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| 73 | meanCalculator.Add(OnlineTheilsUStatisticCalculator.Calculate(
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| 74 | startValues,
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| 75 | allPredictedContinuations.Select(v => v.ElementAt(i)),
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| 76 | actualContinuations, out errorState));
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| 77 | if (errorState != OnlineCalculatorError.None) return double.NaN;
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| 78 | i++;
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| 79 | }
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| 80 | return meanCalculator.Mean;
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| 81 | }
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| 82 |
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| 83 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
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| 84 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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| 85 | EstimationLimitsParameter.ExecutionContext = context;
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| 86 | HorizonParameter.ExecutionContext = context;
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| 87 |
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| 88 | double r2 = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, HorizonParameter.ActualValue.Value);
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| 89 |
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| 90 | HorizonParameter.ExecutionContext = null;
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| 91 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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| 92 | EstimationLimitsParameter.ExecutionContext = null;
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| 93 |
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| 94 | return r2;
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| 95 | }
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| 96 | }
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| 97 | }
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