#region License Information /* HeuristicLab * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Data; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis { [StorableClass] public abstract class TimeSeriesPrognosisSolutionBase : RegressionSolutionBase, ITimeSeriesPrognosisSolution { #region result names protected const string TrainingDirectionalSymmetryResultName = "Average directional symmetry (training)"; protected const string TestDirectionalSymmetryResultName = "Average directional symmetry (test)"; protected const string TrainingWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (training)"; protected const string TestWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (test)"; protected const string TrainingTheilsUStatisticAR1ResultName = "Theil's U2 (AR1) (training)"; protected const string TestTheilsUStatisticLastResultName = "Theil's U2 (AR1) (test)"; protected const string TrainingTheilsUStatisticMeanResultName = "Theil's U2 (mean) (training)"; protected const string TestTheilsUStatisticMeanResultName = "Theil's U2 (mean) (test)"; protected const string TimeSeriesPrognosisResultName = "Prognosis Results"; #endregion #region result descriptions protected const string TrainingDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the training partition"; protected const string TestDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the test partition"; protected const string TrainingWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the training partition"; protected const string TestWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the test partition"; protected const string TrainingTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the training partition"; protected const string TestTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the test partition"; protected const string TrainingTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean model) of the forecasts of the model on the training partition"; protected const string TestTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean value) of the forecasts of the model on the test partition"; protected const string TimeSeriesPrognosisResultDescription = "The calculated results of predictions in the future."; #endregion public new ITimeSeriesPrognosisModel Model { get { return (ITimeSeriesPrognosisModel)base.Model; } protected set { base.Model = value; } } public new ITimeSeriesPrognosisProblemData ProblemData { get { return (ITimeSeriesPrognosisProblemData)base.ProblemData; } set { base.ProblemData = value; } } public abstract IEnumerable PrognosedTestValues { get; } public abstract IEnumerable> GetPrognosedValues(IEnumerable rows, IEnumerable horizon); #region Results public double TrainingDirectionalSymmetry { get { return ((DoubleValue)this[TrainingDirectionalSymmetryResultName].Value).Value; } private set { ((DoubleValue)this[TrainingDirectionalSymmetryResultName].Value).Value = value; } } public double TestDirectionalSymmetry { get { return ((DoubleValue)this[TestDirectionalSymmetryResultName].Value).Value; } private set { ((DoubleValue)this[TestDirectionalSymmetryResultName].Value).Value = value; } } public double TrainingWeightedDirectionalSymmetry { get { return ((DoubleValue)this[TrainingWeightedDirectionalSymmetryResultName].Value).Value; } private set { ((DoubleValue)this[TrainingWeightedDirectionalSymmetryResultName].Value).Value = value; } } public double TestWeightedDirectionalSymmetry { get { return ((DoubleValue)this[TestWeightedDirectionalSymmetryResultName].Value).Value; } private set { ((DoubleValue)this[TestWeightedDirectionalSymmetryResultName].Value).Value = value; } } public double TrainingTheilsUStatisticAR1 { get { return ((DoubleValue)this[TrainingTheilsUStatisticAR1ResultName].Value).Value; } private set { ((DoubleValue)this[TrainingTheilsUStatisticAR1ResultName].Value).Value = value; } } public double TestTheilsUStatisticAR1 { get { return ((DoubleValue)this[TestTheilsUStatisticLastResultName].Value).Value; } private set { ((DoubleValue)this[TestTheilsUStatisticLastResultName].Value).Value = value; } } public double TrainingTheilsUStatisticMean { get { return ((DoubleValue)this[TrainingTheilsUStatisticMeanResultName].Value).Value; } private set { ((DoubleValue)this[TrainingTheilsUStatisticMeanResultName].Value).Value = value; } } public double TestTheilsUStatisticMean { get { return ((DoubleValue)this[TestTheilsUStatisticMeanResultName].Value).Value; } private set { ((DoubleValue)this[TestTheilsUStatisticMeanResultName].Value).Value = value; } } public TimeSeriesPrognosisResults TimeSeriesPrognosisResults { get { if (!ContainsKey(TimeSeriesPrognosisResultName)) return null; return (TimeSeriesPrognosisResults)this[TimeSeriesPrognosisResultName]; } set { if (ContainsKey(TimeSeriesPrognosisResultName)) Remove(TimeSeriesPrognosisResultName); Add(new Result(TimeSeriesPrognosisResultName, TimeSeriesPrognosisResultDescription, value)); } } #endregion public override IEnumerable EstimatedValues { get { return GetEstimatedValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); } } public override IEnumerable EstimatedTrainingValues { get { return GetEstimatedValues(ProblemData.TrainingIndices); } } public override IEnumerable EstimatedTestValues { get { return GetEstimatedValues(ProblemData.TestIndices); } } public override IEnumerable GetEstimatedValues(IEnumerable rows) { return Model.GetEstimatedValues(ProblemData.Dataset, rows); } [StorableConstructor] protected TimeSeriesPrognosisSolutionBase(bool deserializing) : base(deserializing) { } protected TimeSeriesPrognosisSolutionBase(TimeSeriesPrognosisSolutionBase original, Cloner cloner) : base(original, cloner) { } protected TimeSeriesPrognosisSolutionBase(ITimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData) : base(model, problemData) { Add(new Result(TrainingDirectionalSymmetryResultName, TrainingDirectionalSymmetryResultDescription, new DoubleValue())); Add(new Result(TestDirectionalSymmetryResultName, TestDirectionalSymmetryResultDescription, new DoubleValue())); Add(new Result(TrainingWeightedDirectionalSymmetryResultName, TrainingWeightedDirectionalSymmetryResultDescription, new DoubleValue())); Add(new Result(TestWeightedDirectionalSymmetryResultName, TestWeightedDirectionalSymmetryResultDescription, new DoubleValue())); Add(new Result(TrainingTheilsUStatisticAR1ResultName, TrainingTheilsUStatisticAR1ResultDescription, new DoubleValue())); Add(new Result(TestTheilsUStatisticLastResultName, TestTheilsUStatisticAR1ResultDescription, new DoubleValue())); Add(new Result(TrainingTheilsUStatisticMeanResultName, TrainingTheilsUStatisticMeanResultDescription, new DoubleValue())); Add(new Result(TestTheilsUStatisticMeanResultName, TestTheilsUStatisticMeanResultDescription, new DoubleValue())); } protected override void RecalculateResults() { base.RecalculateResults(); CalculateTimeSeriesResults(); CalculateTimeSeriesResults(ProblemData.TrainingHorizon, ProblemData.TestHorizon); } protected void CalculateTimeSeriesResults() { OnlineCalculatorError errorState; double trainingMean = ProblemData.TrainingIndices.Any() ? ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average() : double.NaN; var meanModel = new ConstantModel(trainingMean, ProblemData.TargetVariable); double alpha, beta; IEnumerable trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList(); OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState); var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(ProblemData.TargetVariable, new double[] { beta }, alpha); #region Calculate training quality measures if (ProblemData.TrainingIndices.Any()) { IEnumerable trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList(); IEnumerable trainingEstimatedValues = EstimatedTrainingValues.ToList(); IEnumerable trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList(); IEnumerable trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList(); TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState); TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0; TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState); TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0; TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState); TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity; TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState); TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity; } #endregion #region Calculate test quality measures if (ProblemData.TestIndices.Any()) { IEnumerable testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList(); IEnumerable testEstimatedValues = EstimatedTestValues.ToList(); IEnumerable testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList(); IEnumerable testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList(); TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState); TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0; TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState); TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0; TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState); TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity; TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState); TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity; } #endregion } protected void CalculateTimeSeriesResults(int trainingHorizon, int testHorizon) { TimeSeriesPrognosisResults = new TimeSeriesPrognosisResults(trainingHorizon, testHorizon, this); } } }