#region License Information
/* HeuristicLab
* Copyright (C) 2002-2019 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 HEAL.Fossil;
namespace HeuristicLab.Problems.DataAnalysis {
[StorableType("DF08D8C4-90F8-456E-855B-C8F487FF7B51")]
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(StorableConstructorFlag _) : base(_) { }
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);
}
}
}