[6802] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 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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[7120] | 22 | using System;
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[6802] | 23 | using System.Collections.Generic;
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[7120] | 24 | using System.Linq;
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[6802] | 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("Mean squared error Evaluator", "Calculates the mean squared error of a symbolic time-series prognosis solution.")]
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| 33 | [StorableClass]
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| 34 | public class SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator : SymbolicTimeSeriesPrognosisSingleObjectiveEvaluator {
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| 35 | [StorableConstructor]
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| 36 | protected SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(bool deserializing) : base(deserializing) { }
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| 37 | protected SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator 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 SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator(this, cloner);
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| 42 | }
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| 43 |
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| 44 | public SymbolicTimeSeriesPrognosisSingleObjectiveMeanSquaredErrorEvaluator() : base() { }
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| 45 |
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| 46 | public override bool Maximization { get { return false; } }
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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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[7120] | 52 | double quality = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue,
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| 53 | solution,
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| 54 | EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper,
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| 55 | ProblemDataParameter.ActualValue,
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| 56 | rows, HorizonParameter.ActualValue.Value);
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[6802] | 57 | QualityParameter.ActualValue = new DoubleValue(quality);
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| 58 |
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| 59 | return base.Apply();
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| 60 | }
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| 61 |
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[7120] | 62 | public static double Calculate(ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows, int horizon) {
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| 63 | var allPredictedContinuations = interpreter.GetSymbolicExpressionTreeValues(solution, problemData.Dataset, problemData.TargetVariables.ToArray(), rows, horizon);
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| 64 | var meanCalculator = new OnlineMeanAndVarianceCalculator();
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| 65 | var allPredictedContinuationsEnumerator = allPredictedContinuations.GetEnumerator();
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[7100] | 66 | foreach (var targetVariable in problemData.TargetVariables) {
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[7120] | 67 | if (!allPredictedContinuationsEnumerator.MoveNext()) throw new InvalidOperationException();
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| 68 | var actualContinuations = from r in rows
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| 69 | select problemData.Dataset.GetDoubleValues(targetVariable, Enumerable.Range(r, horizon));
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| 70 | var actualContinuationsEnumerator = actualContinuations.GetEnumerator();
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| 71 | var predictedContinuationsEnumerator = allPredictedContinuationsEnumerator.Current.GetEnumerator();
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| 72 | while (actualContinuationsEnumerator.MoveNext() & predictedContinuationsEnumerator.MoveNext()) {
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| 73 | OnlineCalculatorError errorState;
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| 74 | meanCalculator.Add(OnlineMeanSquaredErrorCalculator.Calculate(predictedContinuationsEnumerator.Current.LimitToRange(lowerEstimationLimit, upperEstimationLimit),
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| 75 | actualContinuationsEnumerator.Current, out errorState));
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| 76 | if (errorState != OnlineCalculatorError.None) return double.NaN;
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| 77 | }
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[7100] | 78 | }
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| 79 | return meanCalculator.Mean;
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[6802] | 80 | }
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| 81 |
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| 82 | public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, ITimeSeriesPrognosisProblemData problemData, IEnumerable<int> rows) {
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[7120] | 83 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
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[6802] | 84 | EstimationLimitsParameter.ExecutionContext = context;
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[7120] | 85 | HorizonParameter.ExecutionContext = context;
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[6802] | 86 |
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[7120] | 87 | double mse = Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, HorizonParameter.ActualValue.Value);
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[6802] | 88 |
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[7120] | 89 | HorizonParameter.ExecutionContext = null;
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| 90 | SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
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[6802] | 91 | EstimationLimitsParameter.ExecutionContext = null;
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| 92 |
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| 93 | return mse;
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| 94 | }
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| 95 | }
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| 96 | }
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