source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs @ 8460

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2011 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
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 {
    private const string TrainingDirectionalSymmetryResultName = "Average directional symmetry (training)";
    private const string TestDirectionalSymmetryResultName = "Average directional symmetry (test)";
    private const string TrainingWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (training)";
    private const string TestWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (test)";
    private const string TrainingTheilsUStatisticLastResultName = "Average Theil's U (last) (training)";
    private const string TestTheilsUStatisticLastResultName = "Average Theil's U (last) (test)";
    private const string TrainingTheilsUStatisticMeanResultName = "Average Theil's U (mean) (training)";
    private const string TestTheilsUStatisticMeanResultName = "Average Theil's U (mean) (test)";
    private const string TrainingTheilsUStatisticMaResultName = "Average Theil's U (moving average) (training)";
    private const string TestTheilsUStatisticMaResultName = "Average Theil's U (moving average) (test)";

    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; }
    }

    [Storable]
    private int horizon;
    public int Horizon {
      get { return horizon; }
      set {
        if (horizon != value) {
          horizon = value;
          RecalculateResults();
        }
      }
    }

    public abstract IEnumerable<IEnumerable<double>> GetPrognosedValues(IEnumerable<int> rows, IEnumerable<int> 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 TrainingTheilsUStatisticLast {
      get { return ((DoubleValue)this[TrainingTheilsUStatisticLastResultName].Value).Value; }
      private set { ((DoubleValue)this[TrainingTheilsUStatisticLastResultName].Value).Value = value; }
    }
    public double TestTheilsUStatisticLast {
      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 double TrainingTheilsUStatisticMovingAverage {
      get { return ((DoubleValue)this[TrainingTheilsUStatisticMaResultName].Value).Value; }
      private set { ((DoubleValue)this[TrainingTheilsUStatisticMaResultName].Value).Value = value; }
    }
    public double TestTheilsUStatisticMovingAverage {
      get { return ((DoubleValue)this[TestTheilsUStatisticMaResultName].Value).Value; }
      private set { ((DoubleValue)this[TestTheilsUStatisticMaResultName].Value).Value = value; }
    }
    #endregion

    public override IEnumerable<double> EstimatedValues {
      get { return GetEstimatedValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
    }
    public override IEnumerable<double> EstimatedTrainingValues {
      get { return GetEstimatedValues(ProblemData.TrainingIndices); }
    }
    public override IEnumerable<double> EstimatedTestValues {
      get { return GetEstimatedValues(ProblemData.TestIndices); }
    }
    public override IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
      return Model.GetEstimatedValues(ProblemData.Dataset, rows);
    }

    [StorableConstructor]
    protected TimeSeriesPrognosisSolutionBase(bool deserializing) : base(deserializing) { }
    protected TimeSeriesPrognosisSolutionBase(TimeSeriesPrognosisSolutionBase original, Cloner cloner)
      : base(original, cloner) {
      this.horizon = original.horizon;
    }
    protected TimeSeriesPrognosisSolutionBase(ITimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData)
      : base(model, problemData) {
      Add(new Result(TrainingDirectionalSymmetryResultName, "The average directional symmetry of the forecasts of the model on the training partition", new DoubleValue()));
      Add(new Result(TestDirectionalSymmetryResultName, "The average directional symmetry of the forecasts of the model on the test partition", new DoubleValue()));
      Add(new Result(TrainingWeightedDirectionalSymmetryResultName, "The average weighted directional symmetry of the forecasts of the model on the training partition", new DoubleValue()));
      Add(new Result(TestWeightedDirectionalSymmetryResultName, "The average weighted directional symmetry of the forecasts of the model on the test partition", new DoubleValue()));
      Add(new Result(TrainingTheilsUStatisticLastResultName, "The average Theil's U statistic (reference: previous value) of the forecasts of the model on the training partition", new DoubleValue()));
      Add(new Result(TestTheilsUStatisticLastResultName, "The average Theil's U statistic (reference: previous value) of the forecasts of the model on the test partition", new DoubleValue()));
      Add(new Result(TrainingTheilsUStatisticMeanResultName, "The average Theil's U statistic (reference: mean value) of the forecasts of the model on the training partition", new DoubleValue()));
      Add(new Result(TestTheilsUStatisticMeanResultName, "The average Theil's U statistic (reference: mean value) of the forecasts of the model on the test partition", new DoubleValue()));
      Add(new Result(TrainingTheilsUStatisticMaResultName, "The average Theil's U statistic (reference: moving average) of the forecasts of the model on the training partition", new DoubleValue()));
      Add(new Result(TestTheilsUStatisticMaResultName, "The average Theil's U statistic (reference: moving average) of the forecasts of the model on the test partition", new DoubleValue()));
      horizon = 1;
    }

    protected override void RecalculateResults() {
      base.RecalculateResults();
      CalculateTimeSeriesResults();
    }

    private void CalculateTimeSeriesResults() {
      OnlineCalculatorError errorState;
      //mean model
      double trainingMean = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average();
      var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);

      //AR1 model
      double alpha, beta;
      IEnumerable<double> 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(alpha, beta, ProblemData.TargetVariable);

      //MA model
      int movingAverageWindowSize = 10 + horizon;
      var MovingAverageModel = new TimeSeriesPrognosisMovingAverageModel(movingAverageWindowSize, ProblemData.TargetVariable);

      #region Calculate training quality measures
      var trainingHorizions = ProblemData.TrainingIndices.Select(r => Math.Min(horizon, ProblemData.TrainingPartition.End - r)).ToList();
      IEnumerable<IEnumerable<double>> trainingTargetValues = ProblemData.TrainingIndices.Zip(trainingHorizions, Enumerable.Range).Select(r => ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, r)).ToList();
      IEnumerable<IEnumerable<double>> trainingEstimatedValues = Model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions).ToList();
      IEnumerable<IEnumerable<double>> trainingMeanModelPredictions = meanModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions);
      IEnumerable<IEnumerable<double>> trainingAR1ModelPredictions = AR1model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions);
      IEnumerable<IEnumerable<double>> trainingMovingAverageModelPredictions = MovingAverageModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions);

      TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
      TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
      TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
      TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
      TrainingTheilsUStatisticLast = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
      TrainingTheilsUStatisticLast = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticLast : double.PositiveInfinity; ;
      TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
      TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity; ;
      TrainingTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMovingAverageModelPredictions, trainingEstimatedValues, out errorState);
      TrainingTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMovingAverage : double.PositiveInfinity; ; ;
      #endregion

      #region  Calculate test quality measures
      var testHorizions = ProblemData.TestIndices.Select(r => Math.Min(horizon, ProblemData.TestPartition.End - r)).ToList();
      IEnumerable<IEnumerable<double>> testTargetValues = ProblemData.TestIndices.Zip(testHorizions, Enumerable.Range).Select(r => ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, r)).ToList();
      IEnumerable<IEnumerable<double>> testEstimatedValues = Model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions).ToList();
      IEnumerable<double> testStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
      IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions);
      IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions);
      IEnumerable<IEnumerable<double>> testMovingAverageModelPredictions = MovingAverageModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions);

      TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
      TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
      TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
      TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
      TestTheilsUStatisticLast = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
      TestTheilsUStatisticLast = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticLast : double.PositiveInfinity; ;
      TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
      TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity; ;
      TestTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMovingAverageModelPredictions, testEstimatedValues, out errorState);
      TestTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMovingAverage : double.PositiveInfinity; ; ;
      #endregion
    }
  }
}