| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2018 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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| 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.Optimization;
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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 {
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| 32 | [StorableClass]
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| 33 | [Item("Prognosis Results", "Represents a collection of time series prognosis results.")]
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| 34 | public class TimeSeriesPrognosisResults : ResultCollection {
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| 35 | #region result names
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| 36 | protected const string PrognosisTrainingMeanSquaredErrorResultName = "Mean squared error (training)";
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| 37 | protected const string PrognosisTestMeanSquaredErrorResultName = "Mean squared error (test)";
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| 38 | protected const string PrognosisTrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
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| 39 | protected const string PrognosisTestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
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| 40 | protected const string PrognosisTrainingSquaredCorrelationResultName = "Pearson's R² (training)";
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| 41 | protected const string PrognosisTestSquaredCorrelationResultName = "Pearson's R² (test)";
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| 42 | protected const string PrognosisTrainingRelativeErrorResultName = "Average relative error (training)";
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| 43 | protected const string PrognosisTestRelativeErrorResultName = "Average relative error (test)";
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| 44 | protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
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| 45 | protected const string PrognosisTestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
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| 46 | protected const string PrognosisTrainingMeanErrorResultName = "Mean error (training)";
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| 47 | protected const string PrognosisTestMeanErrorResultName = "Mean error (test)";
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| 48 |
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| 49 | protected const string PrognosisTrainingDirectionalSymmetryResultName = "Average directional symmetry (training)";
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| 50 | protected const string PrognosisTestDirectionalSymmetryResultName = "Average directional symmetry (test)";
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| 51 | protected const string PrognosisTrainingWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (training)";
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| 52 | protected const string PrognosisTestWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (test)";
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| 53 | protected const string PrognosisTrainingTheilsUStatisticAR1ResultName = "Theil's U2 (AR1) (training)";
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| 54 | protected const string PrognosisTestTheilsUStatisticAR1ResultName = "Theil's U2 (AR1) (test)";
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| 55 | protected const string PrognosisTrainingTheilsUStatisticMeanResultName = "Theil's U2 (mean) (training)";
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| 56 | protected const string PrognosisTestTheilsUStatisticMeanResultName = "Theil's U2 (mean) (test)";
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| 57 | #endregion
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| 58 |
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| 59 | #region result descriptions
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| 60 | protected const string PrognosisTrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
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| 61 | protected const string PrognosisTestMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the test partition";
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| 62 | protected const string PrognosisTrainingMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the training partition";
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| 63 | protected const string PrognosisTestMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the test partition";
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| 64 | protected const string PrognosisTrainingSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition";
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| 65 | protected const string PrognosisTestSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition";
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| 66 | protected const string PrognosisTrainingRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the training partition";
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| 67 | protected const string PrognosisTestRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the test partition";
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| 68 | protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
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| 69 | protected const string PrognosisTestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
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| 70 | protected const string PrognosisTrainingMeanErrorResultDescription = "Mean of errors of the model on the training partition";
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| 71 | protected const string PrognosisTestMeanErrorResultDescription = "Mean of errors of the model on the test partition";
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| 72 |
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| 73 | protected const string PrognosisTrainingDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the training partition";
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| 74 | protected const string PrognosisTestDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the test partition";
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| 75 | protected const string PrognosisTrainingWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the training partition";
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| 76 | protected const string PrognosisTestWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the test partition";
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| 77 | protected const string PrognosisTrainingTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the training partition";
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| 78 | protected const string PrognosisTestTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the test partition";
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| 79 | protected const string PrognosisTrainingTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean model) of the forecasts of the model on the training partition";
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| 80 | protected const string PrognosisTestTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean value) of the forecasts of the model on the test partition";
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| 81 | #endregion
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| 82 |
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| 83 | #region result properties
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| 84 | //prognosis results for different horizons
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| 85 | public double PrognosisTrainingMeanSquaredError {
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| 86 | get {
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| 87 | if (!ContainsKey(PrognosisTrainingMeanSquaredErrorResultName)) return double.NaN;
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| 88 | return ((DoubleValue)this[PrognosisTrainingMeanSquaredErrorResultName].Value).Value;
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| 89 | }
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| 90 | private set {
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| 91 | if (!ContainsKey(PrognosisTrainingMeanSquaredErrorResultName)) Add(new Result(PrognosisTrainingMeanSquaredErrorResultName, PrognosisTrainingMeanSquaredErrorResultDescription, new DoubleValue()));
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| 92 | ((DoubleValue)this[PrognosisTrainingMeanSquaredErrorResultName].Value).Value = value;
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| 93 | }
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| 94 | }
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| 95 |
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| 96 | public double PrognosisTestMeanSquaredError {
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| 97 | get {
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| 98 | if (!ContainsKey(PrognosisTestMeanSquaredErrorResultName)) return double.NaN;
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| 99 | return ((DoubleValue)this[PrognosisTestMeanSquaredErrorResultName].Value).Value;
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| 100 | }
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| 101 | private set {
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| 102 | if (!ContainsKey(PrognosisTestMeanSquaredErrorResultName)) Add(new Result(PrognosisTestMeanSquaredErrorResultName, PrognosisTestMeanSquaredErrorResultDescription, new DoubleValue()));
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| 103 | ((DoubleValue)this[PrognosisTestMeanSquaredErrorResultName].Value).Value = value;
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| 104 | }
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| 105 | }
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| 106 |
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| 107 | public double PrognosisTrainingMeanAbsoluteError {
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| 108 | get {
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| 109 | if (!ContainsKey(PrognosisTrainingMeanAbsoluteErrorResultName)) return double.NaN;
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| 110 | return ((DoubleValue)this[PrognosisTrainingMeanAbsoluteErrorResultName].Value).Value;
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| 111 | }
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| 112 | private set {
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| 113 | if (!ContainsKey(PrognosisTrainingMeanAbsoluteErrorResultName)) Add(new Result(PrognosisTrainingMeanAbsoluteErrorResultName, PrognosisTrainingMeanAbsoluteErrorResultDescription, new DoubleValue()));
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| 114 | ((DoubleValue)this[PrognosisTrainingMeanAbsoluteErrorResultName].Value).Value = value;
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| 115 | }
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| 116 | }
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| 117 |
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| 118 | public double PrognosisTestMeanAbsoluteError {
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| 119 | get {
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| 120 | if (!ContainsKey(PrognosisTestMeanAbsoluteErrorResultName)) return double.NaN;
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| 121 | return ((DoubleValue)this[PrognosisTestMeanAbsoluteErrorResultName].Value).Value;
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| 122 | }
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| 123 | private set {
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| 124 | if (!ContainsKey(PrognosisTestMeanAbsoluteErrorResultName)) Add(new Result(PrognosisTestMeanAbsoluteErrorResultName, PrognosisTestMeanAbsoluteErrorResultDescription, new DoubleValue()));
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| 125 | ((DoubleValue)this[PrognosisTestMeanAbsoluteErrorResultName].Value).Value = value;
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| 126 | }
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| 127 | }
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| 128 |
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| 129 | public double PrognosisTrainingRSquared {
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| 130 | get {
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| 131 | if (!ContainsKey(PrognosisTrainingSquaredCorrelationResultName)) return double.NaN;
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| 132 | return ((DoubleValue)this[PrognosisTrainingSquaredCorrelationResultName].Value).Value;
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| 133 | }
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| 134 | private set {
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| 135 | if (!ContainsKey(PrognosisTrainingSquaredCorrelationResultName)) Add(new Result(PrognosisTrainingSquaredCorrelationResultName, PrognosisTrainingSquaredCorrelationResultDescription, new DoubleValue()));
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| 136 | ((DoubleValue)this[PrognosisTrainingSquaredCorrelationResultName].Value).Value = value;
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| 137 | }
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| 138 | }
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| 139 |
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| 140 | public double PrognosisTestRSquared {
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| 141 | get {
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| 142 | if (!ContainsKey(PrognosisTestSquaredCorrelationResultName)) return double.NaN;
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| 143 | return ((DoubleValue)this[PrognosisTestSquaredCorrelationResultName].Value).Value;
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| 144 | }
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| 145 | private set {
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| 146 | if (!ContainsKey(PrognosisTestSquaredCorrelationResultName)) Add(new Result(PrognosisTestSquaredCorrelationResultName, PrognosisTestSquaredCorrelationResultDescription, new DoubleValue()));
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| 147 | ((DoubleValue)this[PrognosisTestSquaredCorrelationResultName].Value).Value = value;
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| 148 | }
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| 149 | }
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| 150 |
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| 151 | public double PrognosisTrainingRelativeError {
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| 152 | get {
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| 153 | if (!ContainsKey(PrognosisTrainingRelativeErrorResultName)) return double.NaN;
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| 154 | return ((DoubleValue)this[PrognosisTrainingRelativeErrorResultName].Value).Value;
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| 155 | }
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| 156 | private set {
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| 157 | if (!ContainsKey(PrognosisTrainingRelativeErrorResultName)) Add(new Result(PrognosisTrainingRelativeErrorResultName, PrognosisTrainingRelativeErrorResultDescription, new DoubleValue()));
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| 158 | ((DoubleValue)this[PrognosisTrainingRelativeErrorResultName].Value).Value = value;
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| 159 | }
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| 160 | }
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| 161 |
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| 162 | public double PrognosisTestRelativeError {
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| 163 | get {
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| 164 | if (!ContainsKey(PrognosisTestRelativeErrorResultName)) return double.NaN;
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| 165 | return ((DoubleValue)this[PrognosisTestRelativeErrorResultName].Value).Value;
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| 166 | }
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| 167 | private set {
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| 168 | if (!ContainsKey(PrognosisTestRelativeErrorResultName)) Add(new Result(PrognosisTestRelativeErrorResultName, PrognosisTestRelativeErrorResultDescription, new DoubleValue()));
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| 169 | ((DoubleValue)this[PrognosisTestRelativeErrorResultName].Value).Value = value;
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| 170 | }
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| 171 | }
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| 172 |
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| 173 | public double PrognosisTrainingNormalizedMeanSquaredError {
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| 174 | get {
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| 175 | if (!ContainsKey(PrognosisTrainingNormalizedMeanSquaredErrorResultName)) return double.NaN;
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| 176 | return ((DoubleValue)this[PrognosisTrainingNormalizedMeanSquaredErrorResultName].Value).Value;
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| 177 | }
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| 178 | private set {
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| 179 | if (!ContainsKey(PrognosisTrainingNormalizedMeanSquaredErrorResultName)) Add(new Result(PrognosisTrainingNormalizedMeanSquaredErrorResultName, PrognosisTrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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| 180 | ((DoubleValue)this[PrognosisTrainingNormalizedMeanSquaredErrorResultName].Value).Value = value;
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| 181 | }
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| 182 | }
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| 183 |
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| 184 | public double PrognosisTestNormalizedMeanSquaredError {
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| 185 | get {
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| 186 | if (!ContainsKey(PrognosisTestNormalizedMeanSquaredErrorResultName)) return double.NaN;
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| 187 | return ((DoubleValue)this[PrognosisTestNormalizedMeanSquaredErrorResultName].Value).Value;
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| 188 | }
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| 189 | private set {
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| 190 | if (!ContainsKey(PrognosisTestNormalizedMeanSquaredErrorResultName)) Add(new Result(PrognosisTestNormalizedMeanSquaredErrorResultName, PrognosisTestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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| 191 | ((DoubleValue)this[PrognosisTestNormalizedMeanSquaredErrorResultName].Value).Value = value;
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| 192 | }
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| 193 | }
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| 194 |
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| 195 | public double PrognosisTrainingMeanError {
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| 196 | get {
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| 197 | if (!ContainsKey(PrognosisTrainingMeanErrorResultName)) return double.NaN;
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| 198 | return ((DoubleValue)this[PrognosisTrainingMeanErrorResultName].Value).Value;
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| 199 | }
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| 200 | private set {
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| 201 | if (!ContainsKey(PrognosisTrainingMeanErrorResultName)) Add(new Result(PrognosisTrainingMeanErrorResultName, PrognosisTrainingMeanErrorResultDescription, new DoubleValue()));
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| 202 | ((DoubleValue)this[PrognosisTrainingMeanErrorResultName].Value).Value = value;
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| 203 | }
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| 204 | }
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| 205 |
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| 206 | public double PrognosisTestMeanError {
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| 207 | get {
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| 208 | if (!ContainsKey(PrognosisTestMeanErrorResultName)) return double.NaN;
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| 209 | return ((DoubleValue)this[PrognosisTestMeanErrorResultName].Value).Value;
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| 210 | }
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| 211 | private set {
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| 212 | if (!ContainsKey(PrognosisTestMeanErrorResultName)) Add(new Result(PrognosisTestMeanErrorResultName, PrognosisTestMeanErrorResultDescription, new DoubleValue()));
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| 213 | ((DoubleValue)this[PrognosisTestMeanErrorResultName].Value).Value = value;
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| 214 | }
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| 215 | }
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| 216 |
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| 217 |
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| 218 | public double PrognosisTrainingDirectionalSymmetry {
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| 219 | get {
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| 220 | if (!ContainsKey(PrognosisTrainingDirectionalSymmetryResultName)) return double.NaN;
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| 221 | return ((DoubleValue)this[PrognosisTrainingDirectionalSymmetryResultName].Value).Value;
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| 222 | }
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| 223 | private set {
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| 224 | if (!ContainsKey(PrognosisTrainingDirectionalSymmetryResultName)) Add(new Result(PrognosisTrainingDirectionalSymmetryResultName, PrognosisTrainingDirectionalSymmetryResultDescription, new DoubleValue()));
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| 225 | ((DoubleValue)this[PrognosisTrainingDirectionalSymmetryResultName].Value).Value = value;
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| 226 | }
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| 227 | }
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| 228 | public double PrognosisTestDirectionalSymmetry {
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| 229 | get {
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| 230 | if (!ContainsKey(PrognosisTestDirectionalSymmetryResultName)) return double.NaN;
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| 231 | return ((DoubleValue)this[PrognosisTestDirectionalSymmetryResultName].Value).Value;
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| 232 | }
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| 233 | private set {
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| 234 | if (!ContainsKey(PrognosisTestDirectionalSymmetryResultName)) Add(new Result(PrognosisTestDirectionalSymmetryResultName, PrognosisTestDirectionalSymmetryResultDescription, new DoubleValue()));
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| 235 | ((DoubleValue)this[PrognosisTestDirectionalSymmetryResultName].Value).Value = value;
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| 236 | }
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| 237 | }
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| 238 | public double PrognosisTrainingWeightedDirectionalSymmetry {
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| 239 | get {
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| 240 | if (!ContainsKey(PrognosisTrainingWeightedDirectionalSymmetryResultName)) return double.NaN;
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| 241 | return ((DoubleValue)this[PrognosisTrainingWeightedDirectionalSymmetryResultName].Value).Value;
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| 242 | }
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| 243 | private set {
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| 244 | if (!ContainsKey(PrognosisTrainingWeightedDirectionalSymmetryResultName)) Add(new Result(PrognosisTrainingWeightedDirectionalSymmetryResultName, PrognosisTrainingWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
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| 245 | ((DoubleValue)this[PrognosisTrainingWeightedDirectionalSymmetryResultName].Value).Value = value;
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| 246 | }
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| 247 | }
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| 248 | public double PrognosisTestWeightedDirectionalSymmetry {
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| 249 | get {
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| 250 | if (!ContainsKey(PrognosisTestWeightedDirectionalSymmetryResultName)) return double.NaN;
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| 251 | return ((DoubleValue)this[PrognosisTestWeightedDirectionalSymmetryResultName].Value).Value;
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| 252 | }
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| 253 | private set {
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| 254 | if (!ContainsKey(PrognosisTestWeightedDirectionalSymmetryResultName)) Add(new Result(PrognosisTestWeightedDirectionalSymmetryResultName, PrognosisTestWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
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| 255 | ((DoubleValue)this[PrognosisTestWeightedDirectionalSymmetryResultName].Value).Value = value;
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| 256 | }
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| 257 | }
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| 258 | public double PrognosisTrainingTheilsUStatisticAR1 {
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| 259 | get {
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| 260 | if (!ContainsKey(PrognosisTrainingTheilsUStatisticAR1ResultName)) return double.NaN;
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| 261 | return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticAR1ResultName].Value).Value;
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| 262 | }
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| 263 | private set {
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| 264 | if (!ContainsKey(PrognosisTrainingTheilsUStatisticAR1ResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticAR1ResultName, PrognosisTrainingTheilsUStatisticAR1ResultDescription, new DoubleValue()));
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| 265 | ((DoubleValue)this[PrognosisTrainingTheilsUStatisticAR1ResultName].Value).Value = value;
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| 266 | }
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| 267 | }
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| 268 | public double PrognosisTestTheilsUStatisticAR1 {
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| 269 | get {
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| 270 | if (!ContainsKey(PrognosisTestTheilsUStatisticAR1ResultName)) return double.NaN;
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| 271 | return ((DoubleValue)this[PrognosisTestTheilsUStatisticAR1ResultName].Value).Value;
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| 272 | }
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| 273 | private set {
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| 274 | if (!ContainsKey(PrognosisTestTheilsUStatisticAR1ResultName)) Add(new Result(PrognosisTestTheilsUStatisticAR1ResultName, PrognosisTestTheilsUStatisticAR1ResultDescription, new DoubleValue()));
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| 275 | ((DoubleValue)this[PrognosisTestTheilsUStatisticAR1ResultName].Value).Value = value;
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| 276 | }
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| 277 | }
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| 278 | public double PrognosisTrainingTheilsUStatisticMean {
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| 279 | get {
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| 280 | if (!ContainsKey(PrognosisTrainingTheilsUStatisticMeanResultName)) return double.NaN;
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| 281 | return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMeanResultName].Value).Value;
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| 282 | }
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| 283 | private set {
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| 284 | if (!ContainsKey(PrognosisTrainingTheilsUStatisticMeanResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticMeanResultName, PrognosisTrainingTheilsUStatisticMeanResultDescription, new DoubleValue()));
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| 285 | ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMeanResultName].Value).Value = value;
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| 286 | }
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| 287 | }
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| 288 | public double PrognosisTestTheilsUStatisticMean {
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| 289 | get {
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| 290 | if (!ContainsKey(PrognosisTestTheilsUStatisticMeanResultName)) return double.NaN;
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| 291 | return ((DoubleValue)this[PrognosisTestTheilsUStatisticMeanResultName].Value).Value;
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| 292 | }
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| 293 | private set {
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| 294 | if (!ContainsKey(PrognosisTestTheilsUStatisticMeanResultName)) Add(new Result(PrognosisTestTheilsUStatisticMeanResultName, PrognosisTestTheilsUStatisticMeanResultDescription, new DoubleValue()));
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| 295 | ((DoubleValue)this[PrognosisTestTheilsUStatisticMeanResultName].Value).Value = value;
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| 296 | }
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| 297 | }
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| 298 | #endregion
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| 299 |
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| 300 | [Storable]
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| 301 | private int trainingHorizon;
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| 302 | public int TrainingHorizon {
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| 303 | get { return trainingHorizon; }
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| 304 | set {
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| 305 | if (trainingHorizon != value) {
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| 306 | trainingHorizon = value;
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| 307 | OnTrainingHorizonChanged();
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| 308 | }
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| 309 | }
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| 310 | }
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| 311 |
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| 312 | [Storable]
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| 313 | private int testHorizon;
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| 314 | public int TestHorizon {
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| 315 | get { return testHorizon; }
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| 316 | set {
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| 317 | if (testHorizon != value) {
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| 318 | testHorizon = value;
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| 319 | OnTestHorizonChanged();
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| 320 | }
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| 321 | }
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| 322 | }
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| 323 |
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| 324 | private ITimeSeriesPrognosisSolution solution;
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| 325 | [Storable]
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| 326 | public ITimeSeriesPrognosisSolution Solution {
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| 327 | get { return solution; }
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| 328 | private set { solution = value; } //necessary for persistence
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| 329 | }
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| 330 |
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| 331 | [StorableConstructor]
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| 332 | public TimeSeriesPrognosisResults(bool deserializing) : base(deserializing) { }
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| 333 | protected TimeSeriesPrognosisResults(TimeSeriesPrognosisResults original, Cloner cloner)
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| 334 | : base(original, cloner) {
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| 335 | this.trainingHorizon = original.trainingHorizon;
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| 336 | this.testHorizon = original.testHorizon;
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| 337 | this.solution = cloner.Clone(original.solution);
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| 338 | }
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| 339 | public override IDeepCloneable Clone(Cloner cloner) {
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| 340 | return new TimeSeriesPrognosisResults(this, cloner);
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| 341 | }
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| 342 |
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| 343 | public TimeSeriesPrognosisResults(int trainingHorizon, int testHorizon, ITimeSeriesPrognosisSolution solution)
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| 344 | : base() {
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| 345 | this.trainingHorizon = trainingHorizon;
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| 346 | this.testHorizon = testHorizon;
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| 347 | this.solution = solution;
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| 348 | CalculateTrainingPrognosisResults();
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| 349 | CalculateTestPrognosisResults();
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| 350 | }
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| 351 |
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| 352 | #region events
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| 353 | public event EventHandler TrainingHorizonChanged;
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| 354 | protected virtual void OnTrainingHorizonChanged() {
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| 355 | CalculateTrainingPrognosisResults();
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| 356 | var handler = TrainingHorizonChanged;
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| 357 | if (handler != null) handler(this, EventArgs.Empty);
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| 358 | }
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| 359 |
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| 360 | public event EventHandler TestHorizonChanged;
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| 361 | protected virtual void OnTestHorizonChanged() {
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| 362 | CalculateTestPrognosisResults();
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| 363 | var handler = TestHorizonChanged;
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| 364 | if (handler != null) handler(this, EventArgs.Empty);
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| 365 | }
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| 366 | #endregion
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| 367 |
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| 368 | private void CalculateTrainingPrognosisResults() {
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| 369 | OnlineCalculatorError errorState;
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| 370 | var problemData = Solution.ProblemData;
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| 371 | if (!problemData.TrainingIndices.Any()) return;
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| 372 | var model = Solution.Model;
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| 373 | //mean model
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| 374 | double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
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| 375 | var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
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| 376 |
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| 377 | //AR1 model
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| 378 | double alpha, beta;
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| 379 | IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
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| 380 | OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
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| 381 | var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
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| 382 |
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| 383 | var trainingHorizions = problemData.TrainingIndices.Select(r => Math.Min(trainingHorizon, problemData.TrainingPartition.End - r)).ToList();
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| 384 | IEnumerable<IEnumerable<double>> trainingTargetValues = problemData.TrainingIndices.Zip(trainingHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
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| 385 | IEnumerable<IEnumerable<double>> trainingEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
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| 386 | IEnumerable<IEnumerable<double>> trainingMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
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| 387 | IEnumerable<IEnumerable<double>> trainingAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
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| 388 |
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| 389 | IEnumerable<double> originalTrainingValues = trainingTargetValues.SelectMany(x => x).ToList();
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| 390 | IEnumerable<double> estimatedTrainingValues = trainingEstimatedValues.SelectMany(x => x).ToList();
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| 391 |
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| 392 | double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 393 | PrognosisTrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
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| 394 | double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 395 | PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
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| 396 | double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 397 | PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
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| 398 | double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 399 | PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
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| 400 | double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 401 | PrognosisTrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
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| 402 | double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 403 | PrognosisTrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
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| 404 |
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| 405 | PrognosisTrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
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| 406 | PrognosisTrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingDirectionalSymmetry : 0.0;
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| 407 | PrognosisTrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
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| 408 | PrognosisTrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingWeightedDirectionalSymmetry : 0.0;
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| 409 | PrognosisTrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
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| 410 | PrognosisTrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticAR1 : double.PositiveInfinity;
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| 411 | PrognosisTrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
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| 412 | PrognosisTrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticMean : double.PositiveInfinity;
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| 413 | }
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| 414 |
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| 415 | private void CalculateTestPrognosisResults() {
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| 416 | OnlineCalculatorError errorState;
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| 417 | var problemData = Solution.ProblemData;
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| 418 | if (!problemData.TestIndices.Any()) return;
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| 419 | var model = Solution.Model;
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| 420 | var testHorizions = problemData.TestIndices.Select(r => Math.Min(testHorizon, problemData.TestPartition.End - r)).ToList();
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| 421 | IEnumerable<IEnumerable<double>> testTargetValues = problemData.TestIndices.Zip(testHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
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| 422 | IEnumerable<IEnumerable<double>> testEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
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| 423 | IEnumerable<double> testStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
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| 424 |
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| 425 | IEnumerable<double> originalTestValues = testTargetValues.SelectMany(x => x).ToList();
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| 426 | IEnumerable<double> estimatedTestValues = testEstimatedValues.SelectMany(x => x).ToList();
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| 427 |
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| 428 | double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 429 | PrognosisTestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
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| 430 | double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 431 | PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
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| 432 | double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 433 | PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
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| 434 | double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 435 | PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
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| 436 | double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 437 | PrognosisTestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
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| 438 | double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 439 | PrognosisTestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
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| 440 |
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| 441 | PrognosisTestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
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| 442 | PrognosisTestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestDirectionalSymmetry : 0.0;
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| 443 | PrognosisTestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
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| 444 | PrognosisTestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestWeightedDirectionalSymmetry : 0.0;
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| 445 |
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| 446 |
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| 447 | if (problemData.TrainingIndices.Any()) {
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| 448 | //mean model
|
|---|
| 449 | double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
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| 450 | var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
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| 451 |
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| 452 | //AR1 model
|
|---|
| 453 | double alpha, beta;
|
|---|
| 454 | IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
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| 455 | OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
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| 456 | var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
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| 457 |
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| 458 | IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
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|---|
| 459 | IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
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| 460 |
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| 461 | PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
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| 462 | PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
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| 463 | PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
|
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| 464 | PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
|
|---|
| 465 | }
|
|---|
| 466 | }
|
|---|
| 467 | }
|
|---|
| 468 | }
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