| [6588] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| [11205] | 3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| [6588] | 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.Collections.Generic;
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| 23 | using HeuristicLab.Common;
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| 24 | using HeuristicLab.Data;
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| 25 | using HeuristicLab.Optimization;
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| 26 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 27 |
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| 28 | namespace HeuristicLab.Problems.DataAnalysis {
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| 29 | [StorableClass]
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| 30 | public abstract class RegressionSolutionBase : DataAnalysisSolution, IRegressionSolution {
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| [8798] | 31 | protected const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
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| 32 | protected const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
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| 33 | protected const string TrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
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| 34 | protected const string TestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
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| 35 | protected const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
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| 36 | protected const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
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| 37 | protected const string TrainingRelativeErrorResultName = "Average relative error (training)";
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| 38 | protected const string TestRelativeErrorResultName = "Average relative error (test)";
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| 39 | protected const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
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| 40 | protected const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
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| 41 | protected const string TrainingMeanErrorResultName = "Mean error (training)";
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| 42 | protected const string TestMeanErrorResultName = "Mean error (test)";
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| [6588] | 43 |
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| [8798] | 44 | protected const string TrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
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| 45 | protected const string TestMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the test partition";
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| 46 | protected const string TrainingMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the training partition";
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| 47 | protected const string TestMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the test partition";
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| 48 | protected const string TrainingSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition";
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| 49 | protected const string TestSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition";
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| 50 | protected const string TrainingRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the training partition";
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| 51 | protected const string TestRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the test partition";
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| 52 | protected const string TrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
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| 53 | protected const string TestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
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| 54 | protected const string TrainingMeanErrorResultDescription = "Mean of errors of the model on the training partition";
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| 55 | protected const string TestMeanErrorResultDescription = "Mean of errors of the model on the test partition";
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| 56 |
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| [6588] | 57 | public new IRegressionModel Model {
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| 58 | get { return (IRegressionModel)base.Model; }
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| 59 | protected set { base.Model = value; }
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| 60 | }
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| 61 |
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| 62 | public new IRegressionProblemData ProblemData {
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| 63 | get { return (IRegressionProblemData)base.ProblemData; }
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| [6653] | 64 | set { base.ProblemData = value; }
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| [6588] | 65 | }
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| 66 |
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| 67 | public abstract IEnumerable<double> EstimatedValues { get; }
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| 68 | public abstract IEnumerable<double> EstimatedTrainingValues { get; }
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| 69 | public abstract IEnumerable<double> EstimatedTestValues { get; }
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| 70 | public abstract IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows);
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| 71 |
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| 72 | #region Results
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| 73 | public double TrainingMeanSquaredError {
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| 74 | get { return ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value; }
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| 75 | private set { ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value = value; }
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| 76 | }
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| 77 | public double TestMeanSquaredError {
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| 78 | get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
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| 79 | private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
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| 80 | }
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| [6643] | 81 | public double TrainingMeanAbsoluteError {
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| 82 | get { return ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value; }
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| 83 | private set { ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value = value; }
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| 84 | }
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| 85 | public double TestMeanAbsoluteError {
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| 86 | get { return ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value; }
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| 87 | private set { ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value = value; }
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| 88 | }
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| [6588] | 89 | public double TrainingRSquared {
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| 90 | get { return ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value; }
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| 91 | private set { ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value = value; }
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| 92 | }
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| 93 | public double TestRSquared {
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| 94 | get { return ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value; }
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| 95 | private set { ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value = value; }
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| 96 | }
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| 97 | public double TrainingRelativeError {
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| 98 | get { return ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value; }
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| 99 | private set { ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value = value; }
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| 100 | }
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| 101 | public double TestRelativeError {
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| 102 | get { return ((DoubleValue)this[TestRelativeErrorResultName].Value).Value; }
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| 103 | private set { ((DoubleValue)this[TestRelativeErrorResultName].Value).Value = value; }
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| 104 | }
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| 105 | public double TrainingNormalizedMeanSquaredError {
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| 106 | get { return ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value; }
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| 107 | private set { ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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| 108 | }
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| 109 | public double TestNormalizedMeanSquaredError {
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| 110 | get { return ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value; }
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| 111 | private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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| 112 | }
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| [7272] | 113 | public double TrainingMeanError {
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| 114 | get { return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value; }
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| 115 | private set { ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value; }
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| 116 | }
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| 117 | public double TestMeanError {
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| 118 | get { return ((DoubleValue)this[TestMeanErrorResultName].Value).Value; }
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| 119 | private set { ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value; }
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| 120 | }
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| [6588] | 121 | #endregion
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| 122 |
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| 123 | [StorableConstructor]
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| 124 | protected RegressionSolutionBase(bool deserializing) : base(deserializing) { }
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| 125 | protected RegressionSolutionBase(RegressionSolutionBase original, Cloner cloner)
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| 126 | : base(original, cloner) {
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| 127 | }
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| 128 | protected RegressionSolutionBase(IRegressionModel model, IRegressionProblemData problemData)
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| 129 | : base(model, problemData) {
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| [8798] | 130 | Add(new Result(TrainingMeanSquaredErrorResultName, TrainingMeanSquaredErrorResultDescription, new DoubleValue()));
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| 131 | Add(new Result(TestMeanSquaredErrorResultName, TestMeanSquaredErrorResultDescription, new DoubleValue()));
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| 132 | Add(new Result(TrainingMeanAbsoluteErrorResultName, TrainingMeanAbsoluteErrorResultDescription, new DoubleValue()));
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| 133 | Add(new Result(TestMeanAbsoluteErrorResultName, TestMeanAbsoluteErrorResultDescription, new DoubleValue()));
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| 134 | Add(new Result(TrainingSquaredCorrelationResultName, TrainingSquaredCorrelationResultDescription, new DoubleValue()));
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| 135 | Add(new Result(TestSquaredCorrelationResultName, TestSquaredCorrelationResultDescription, new DoubleValue()));
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| 136 | Add(new Result(TrainingRelativeErrorResultName, TrainingRelativeErrorResultDescription, new PercentValue()));
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| 137 | Add(new Result(TestRelativeErrorResultName, TestRelativeErrorResultDescription, new PercentValue()));
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| 138 | Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, TrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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| 139 | Add(new Result(TestNormalizedMeanSquaredErrorResultName, TestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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| 140 | Add(new Result(TrainingMeanErrorResultName, TrainingMeanErrorResultDescription, new DoubleValue()));
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| 141 | Add(new Result(TestMeanErrorResultName, TestMeanErrorResultDescription, new DoubleValue()));
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| [6588] | 142 | }
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| 143 |
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| [6643] | 144 | [StorableHook(HookType.AfterDeserialization)]
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| 145 | private void AfterDeserialization() {
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| 146 | // BackwardsCompatibility3.4
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| 147 |
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| 148 | #region Backwards compatible code, remove with 3.5
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| 149 |
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| 150 | if (!ContainsKey(TrainingMeanAbsoluteErrorResultName)) {
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| 151 | OnlineCalculatorError errorState;
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| 152 | Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
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| [8139] | 153 | double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
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| [6643] | 154 | TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
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| 155 | }
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| 156 |
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| 157 | if (!ContainsKey(TestMeanAbsoluteErrorResultName)) {
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| 158 | OnlineCalculatorError errorState;
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| 159 | Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
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| [8139] | 160 | double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
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| [6643] | 161 | TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
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| 162 | }
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| [7272] | 163 |
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| 164 | if (!ContainsKey(TrainingMeanErrorResultName)) {
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| 165 | OnlineCalculatorError errorState;
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| 166 | Add(new Result(TrainingMeanErrorResultName, "Mean of errors of the model on the training partition", new DoubleValue()));
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| [8139] | 167 | double trainingME = OnlineMeanErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
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| [7272] | 168 | TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
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| 169 | }
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| 170 | if (!ContainsKey(TestMeanErrorResultName)) {
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| 171 | OnlineCalculatorError errorState;
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| 172 | Add(new Result(TestMeanErrorResultName, "Mean of errors of the model on the test partition", new DoubleValue()));
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| [8139] | 173 | double testME = OnlineMeanErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
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| [7272] | 174 | TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
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| 175 | }
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| [6643] | 176 | #endregion
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| 177 | }
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| 178 |
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| [8723] | 179 | protected override void RecalculateResults() {
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| 180 | CalculateRegressionResults();
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| 181 | }
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| 182 |
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| 183 | protected void CalculateRegressionResults() {
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| [7735] | 184 | IEnumerable<double> estimatedTrainingValues = EstimatedTrainingValues; // cache values
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| [8139] | 185 | IEnumerable<double> originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices);
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| [7735] | 186 | IEnumerable<double> estimatedTestValues = EstimatedTestValues; // cache values
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| [8139] | 187 | IEnumerable<double> originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices);
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| [6588] | 188 |
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| 189 | OnlineCalculatorError errorState;
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| [6961] | 190 | double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| [6588] | 191 | TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
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| [6961] | 192 | double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| [6588] | 193 | TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
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| 194 |
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| [6961] | 195 | double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| [6643] | 196 | TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
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| [6961] | 197 | double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| [6643] | 198 | TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
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| 199 |
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| [6961] | 200 | double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| [6588] | 201 | TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
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| [6961] | 202 | double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| [6588] | 203 | TestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
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| 204 |
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| [6961] | 205 | double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| [6588] | 206 | TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
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| [6961] | 207 | double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| [6588] | 208 | TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
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| 209 |
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| [6961] | 210 | double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| [6588] | 211 | TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
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| [6961] | 212 | double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| [6588] | 213 | TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
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| [7272] | 214 |
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| 215 | double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 216 | TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
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| 217 | double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 218 | TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
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| [6588] | 219 | }
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| 220 | }
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| 221 | }
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