| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2015 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 HeuristicLab.Common;
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| 25 | using HeuristicLab.Data;
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| 26 | using HeuristicLab.Optimization;
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| 27 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 28 |
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| 29 | namespace HeuristicLab.Problems.DataAnalysis {
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| 30 | [StorableType("DB55A1CE-5213-4C10-9ED6-56523523303E")]
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| 31 | public abstract class RegressionSolutionBase : DataAnalysisSolution, IRegressionSolution {
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| 32 | protected const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
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| 33 | protected const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
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| 34 | protected const string TrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
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| 35 | protected const string TestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
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| 36 | protected const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
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| 37 | protected const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
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| 38 | protected const string TrainingRelativeErrorResultName = "Average relative error (training)";
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| 39 | protected const string TestRelativeErrorResultName = "Average relative error (test)";
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| 40 | protected const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
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| 41 | protected const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
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| 42 | protected const string TrainingRootMeanSquaredErrorResultName = "Root mean squared error (training)";
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| 43 | protected const string TestRootMeanSquaredErrorResultName = "Root mean squared error (test)";
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| 44 |
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| 45 | // BackwardsCompatibility3.3
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| 46 | #region Backwards compatible code, remove with 3.5
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| 47 | private const string TrainingMeanErrorResultName = "Mean error (training)";
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| 48 | private const string TestMeanErrorResultName = "Mean error (test)";
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| 49 | #endregion
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| 50 |
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| 51 |
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| 52 | protected const string TrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
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| 53 | protected const string TestMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the test partition";
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| 54 | protected const string TrainingMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the training partition";
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| 55 | protected const string TestMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the test partition";
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| 56 | 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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| 57 | 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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| 58 | 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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| 59 | 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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| 60 | protected const string TrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
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| 61 | protected const string TestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
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| 62 | protected const string TrainingRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the training partition";
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| 63 | protected const string TestRootMeanSquaredErrorResultDescription = "Root mean of squared errors of the model on the test partition";
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| 64 |
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| 65 | public new IRegressionModel Model {
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| 66 | get { return (IRegressionModel)base.Model; }
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| 67 | protected set { base.Model = value; }
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| 68 | }
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| 69 |
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| 70 | public new IRegressionProblemData ProblemData {
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| 71 | get { return (IRegressionProblemData)base.ProblemData; }
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| 72 | set { base.ProblemData = value; }
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| 73 | }
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| 74 |
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| 75 | public abstract IEnumerable<double> EstimatedValues { get; }
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| 76 | public abstract IEnumerable<double> EstimatedTrainingValues { get; }
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| 77 | public abstract IEnumerable<double> EstimatedTestValues { get; }
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| 78 | public abstract IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows);
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| 79 |
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| 80 | #region Results
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| 81 | public double TrainingMeanSquaredError {
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| 82 | get { return ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value; }
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| 83 | private set { ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value = value; }
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| 84 | }
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| 85 | public double TestMeanSquaredError {
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| 86 | get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
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| 87 | private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
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| 88 | }
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| 89 | public double TrainingMeanAbsoluteError {
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| 90 | get { return ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value; }
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| 91 | private set { ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value = value; }
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| 92 | }
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| 93 | public double TestMeanAbsoluteError {
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| 94 | get { return ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value; }
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| 95 | private set { ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value = value; }
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| 96 | }
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| 97 | public double TrainingRSquared {
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| 98 | get { return ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value; }
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| 99 | private set { ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value = value; }
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| 100 | }
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| 101 | public double TestRSquared {
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| 102 | get { return ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value; }
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| 103 | private set { ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value = value; }
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| 104 | }
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| 105 | public double TrainingRelativeError {
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| 106 | get { return ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value; }
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| 107 | private set { ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value = value; }
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| 108 | }
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| 109 | public double TestRelativeError {
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| 110 | get { return ((DoubleValue)this[TestRelativeErrorResultName].Value).Value; }
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| 111 | private set { ((DoubleValue)this[TestRelativeErrorResultName].Value).Value = value; }
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| 112 | }
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| 113 | public double TrainingNormalizedMeanSquaredError {
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| 114 | get { return ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value; }
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| 115 | private set { ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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| 116 | }
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| 117 | public double TestNormalizedMeanSquaredError {
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| 118 | get { return ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value; }
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| 119 | private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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| 120 | }
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| 121 | public double TrainingRootMeanSquaredError {
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| 122 | get { return ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value; }
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| 123 | private set { ((DoubleValue)this[TrainingRootMeanSquaredErrorResultName].Value).Value = value; }
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| 124 | }
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| 125 | public double TestRootMeanSquaredError {
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| 126 | get { return ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value; }
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| 127 | private set { ((DoubleValue)this[TestRootMeanSquaredErrorResultName].Value).Value = value; }
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| 128 | }
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| 129 |
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| 130 | // BackwardsCompatibility3.3
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| 131 | #region Backwards compatible code, remove with 3.5
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| 132 | private double TrainingMeanError {
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| 133 | get {
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| 134 | if (!ContainsKey(TrainingMeanErrorResultName)) return double.NaN;
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| 135 | return ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value;
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| 136 | }
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| 137 | set {
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| 138 | if (ContainsKey(TrainingMeanErrorResultName))
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| 139 | ((DoubleValue)this[TrainingMeanErrorResultName].Value).Value = value;
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| 140 | }
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| 141 | }
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| 142 | private double TestMeanError {
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| 143 | get {
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| 144 | if (!ContainsKey(TestMeanErrorResultName)) return double.NaN;
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| 145 | return ((DoubleValue)this[TestMeanErrorResultName].Value).Value;
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| 146 | }
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| 147 | set {
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| 148 | if (ContainsKey(TestMeanErrorResultName))
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| 149 | ((DoubleValue)this[TestMeanErrorResultName].Value).Value = value;
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| 150 | }
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| 151 | }
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| 152 | #endregion
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| 153 | #endregion
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| 154 |
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| 155 | [StorableConstructor]
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| 156 | protected RegressionSolutionBase(bool deserializing) : base(deserializing) { }
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| 157 | protected RegressionSolutionBase(RegressionSolutionBase original, Cloner cloner)
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| 158 | : base(original, cloner) {
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| 159 | }
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| 160 | protected RegressionSolutionBase(IRegressionModel model, IRegressionProblemData problemData)
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| 161 | : base(model, problemData) {
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| 162 | Add(new Result(TrainingMeanSquaredErrorResultName, TrainingMeanSquaredErrorResultDescription, new DoubleValue()));
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| 163 | Add(new Result(TestMeanSquaredErrorResultName, TestMeanSquaredErrorResultDescription, new DoubleValue()));
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| 164 | Add(new Result(TrainingMeanAbsoluteErrorResultName, TrainingMeanAbsoluteErrorResultDescription, new DoubleValue()));
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| 165 | Add(new Result(TestMeanAbsoluteErrorResultName, TestMeanAbsoluteErrorResultDescription, new DoubleValue()));
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| 166 | Add(new Result(TrainingSquaredCorrelationResultName, TrainingSquaredCorrelationResultDescription, new DoubleValue()));
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| 167 | Add(new Result(TestSquaredCorrelationResultName, TestSquaredCorrelationResultDescription, new DoubleValue()));
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| 168 | Add(new Result(TrainingRelativeErrorResultName, TrainingRelativeErrorResultDescription, new PercentValue()));
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| 169 | Add(new Result(TestRelativeErrorResultName, TestRelativeErrorResultDescription, new PercentValue()));
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| 170 | Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, TrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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| 171 | Add(new Result(TestNormalizedMeanSquaredErrorResultName, TestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
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| 172 | Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
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| 173 | Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
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| 174 | }
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| 175 |
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| 176 | [StorableHook(HookType.AfterDeserialization)]
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| 177 | private void AfterDeserialization() {
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| 178 | // BackwardsCompatibility3.4
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| 179 | #region Backwards compatible code, remove with 3.5
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| 180 | if (!ContainsKey(TrainingMeanAbsoluteErrorResultName)) {
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| 181 | OnlineCalculatorError errorState;
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| 182 | Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
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| 183 | double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
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| 184 | TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
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| 185 | }
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| 186 |
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| 187 | if (!ContainsKey(TestMeanAbsoluteErrorResultName)) {
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| 188 | OnlineCalculatorError errorState;
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| 189 | Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
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| 190 | double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
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| 191 | TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
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| 192 | }
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| 193 |
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| 194 | if (!ContainsKey(TrainingRootMeanSquaredErrorResultName)) {
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| 195 | OnlineCalculatorError errorState;
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| 196 | Add(new Result(TrainingRootMeanSquaredErrorResultName, TrainingRootMeanSquaredErrorResultDescription, new DoubleValue()));
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| 197 | double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTrainingValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices), out errorState);
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| 198 | TrainingRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(trainingMSE) : double.NaN;
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| 199 | }
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| 200 |
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| 201 | if (!ContainsKey(TestRootMeanSquaredErrorResultName)) {
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| 202 | OnlineCalculatorError errorState;
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| 203 | Add(new Result(TestRootMeanSquaredErrorResultName, TestRootMeanSquaredErrorResultDescription, new DoubleValue()));
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| 204 | double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(EstimatedTestValues, ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices), out errorState);
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| 205 | TestRootMeanSquaredError = errorState == OnlineCalculatorError.None ? Math.Sqrt(testMSE) : double.NaN;
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| 206 | }
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| 207 | #endregion
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| 208 | }
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| 209 |
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| 210 | protected override void RecalculateResults() {
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| 211 | CalculateRegressionResults();
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| 212 | }
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| 213 |
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| 214 | protected void CalculateRegressionResults() {
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| 215 | IEnumerable<double> estimatedTrainingValues = EstimatedTrainingValues; // cache values
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| 216 | IEnumerable<double> originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices);
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| 217 | IEnumerable<double> estimatedTestValues = EstimatedTestValues; // cache values
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| 218 | IEnumerable<double> originalTestValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices);
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| 219 |
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| 220 | OnlineCalculatorError errorState;
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| 221 | double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 222 | TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
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| 223 | double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 224 | TestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
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| 225 |
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| 226 | double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 227 | TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
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| 228 | double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 229 | TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
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| 230 |
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| 231 | double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 232 | TrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
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| 233 | double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 234 | TestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
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| 235 |
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| 236 | double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 237 | TrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
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| 238 | double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 239 | TestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
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| 240 |
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| 241 | double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 242 | TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
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| 243 | double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 244 | TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
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| 245 |
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| 246 | TrainingRootMeanSquaredError = Math.Sqrt(TrainingMeanSquaredError);
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| 247 | TestRootMeanSquaredError = Math.Sqrt(TestMeanSquaredError);
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| 248 |
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| 249 | // BackwardsCompatibility3.3
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| 250 | #region Backwards compatible code, remove with 3.5
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| 251 | if (ContainsKey(TrainingMeanErrorResultName)) {
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| 252 | double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
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| 253 | TrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
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| 254 | }
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| 255 | if (ContainsKey(TestMeanErrorResultName)) {
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| 256 | double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
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| 257 | TestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
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| 258 | }
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| 259 | #endregion
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| 260 | }
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| 261 | }
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| 262 | }
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