[8323] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12009] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8323] | 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.Persistence.Default.CompositeSerializers.Storable;
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| 28 | using HeuristicLab.Problems.DataAnalysis;
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| 29 |
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[8371] | 30 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[8323] | 31 | /// <summary>
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| 32 | /// Represents a Gaussian process model.
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| 33 | /// </summary>
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| 34 | [StorableClass]
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| 35 | [Item("GaussianProcessModel", "Represents a Gaussian process posterior.")]
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[14027] | 36 | public sealed class GaussianProcessModel : RegressionModel, IGaussianProcessModel {
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| 37 | public override IEnumerable<string> VariablesUsedForPrediction {
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| 38 | get { return allowedInputVariables; }
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| 39 | }
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| 40 |
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[8323] | 41 | [Storable]
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| 42 | private double negativeLogLikelihood;
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| 43 | public double NegativeLogLikelihood {
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| 44 | get { return negativeLogLikelihood; }
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| 45 | }
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| 46 |
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| 47 | [Storable]
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[8484] | 48 | private double[] hyperparameterGradients;
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| 49 | public double[] HyperparameterGradients {
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| 50 | get {
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| 51 | var copy = new double[hyperparameterGradients.Length];
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| 52 | Array.Copy(hyperparameterGradients, copy, copy.Length);
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| 53 | return copy;
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| 54 | }
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| 55 | }
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| 56 |
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| 57 | [Storable]
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[8323] | 58 | private ICovarianceFunction covarianceFunction;
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| 59 | public ICovarianceFunction CovarianceFunction {
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| 60 | get { return covarianceFunction; }
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| 61 | }
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| 62 | [Storable]
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| 63 | private IMeanFunction meanFunction;
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| 64 | public IMeanFunction MeanFunction {
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| 65 | get { return meanFunction; }
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| 66 | }
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[14027] | 67 |
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[8323] | 68 | [Storable]
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| 69 | private string[] allowedInputVariables;
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| 70 | public string[] AllowedInputVariables {
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| 71 | get { return allowedInputVariables; }
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| 72 | }
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| 73 |
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| 74 | [Storable]
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| 75 | private double[] alpha;
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| 76 | [Storable]
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| 77 | private double sqrSigmaNoise;
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[8582] | 78 | public double SigmaNoise {
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| 79 | get { return Math.Sqrt(sqrSigmaNoise); }
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| 80 | }
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[8323] | 81 |
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| 82 | [Storable]
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[8982] | 83 | private double[] meanParameter;
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| 84 | [Storable]
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| 85 | private double[] covarianceParameter;
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| 86 |
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[13052] | 87 | private double[,] l; // used to be storable in previous versions (is calculated lazily now)
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| 88 | private double[,] x; // scaled training dataset, used to be storable in previous versions (is calculated lazily now)
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| 89 |
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| 90 | // BackwardsCompatibility3.4
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| 91 | #region Backwards compatible code, remove with 3.5
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| 92 | [Storable(Name = "l")] // restore if available but don't store anymore
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| 93 | private double[,] l_storable {
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| 94 | set { this.l = value; }
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| 95 | get {
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| 96 | if (trainingDataset == null) return l; // this model has been created with an old version
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| 97 | else return null; // if the training dataset is available l should not be serialized
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| 98 | }
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| 99 | }
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| 100 | [Storable(Name = "x")] // restore if available but don't store anymore
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| 101 | private double[,] x_storable {
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| 102 | set { this.x = value; }
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| 103 | get {
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| 104 | if (trainingDataset == null) return x; // this model has been created with an old version
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| 105 | else return null; // if the training dataset is available x should not be serialized
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| 106 | }
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| 107 | }
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| 108 | #endregion
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| 109 |
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| 110 |
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[8982] | 111 | [Storable]
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[13052] | 112 | private IDataset trainingDataset; // it is better to store the original training dataset completely because this is more efficient in persistence
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| 113 | [Storable]
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| 114 | private int[] trainingRows;
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[8323] | 115 |
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| 116 | [Storable]
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[8463] | 117 | private Scaling inputScaling;
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[8323] | 118 |
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| 119 |
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| 120 | [StorableConstructor]
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| 121 | private GaussianProcessModel(bool deserializing) : base(deserializing) { }
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| 122 | private GaussianProcessModel(GaussianProcessModel original, Cloner cloner)
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| 123 | : base(original, cloner) {
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| 124 | this.meanFunction = cloner.Clone(original.meanFunction);
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| 125 | this.covarianceFunction = cloner.Clone(original.covarianceFunction);
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[13147] | 126 | if (original.inputScaling != null)
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| 127 | this.inputScaling = cloner.Clone(original.inputScaling);
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[13052] | 128 | this.trainingDataset = cloner.Clone(original.trainingDataset);
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[8323] | 129 | this.negativeLogLikelihood = original.negativeLogLikelihood;
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[8416] | 130 | this.sqrSigmaNoise = original.sqrSigmaNoise;
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[8982] | 131 | if (original.meanParameter != null) {
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| 132 | this.meanParameter = (double[])original.meanParameter.Clone();
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| 133 | }
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| 134 | if (original.covarianceParameter != null) {
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| 135 | this.covarianceParameter = (double[])original.covarianceParameter.Clone();
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| 136 | }
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[8416] | 137 |
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| 138 | // shallow copies of arrays because they cannot be modified
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[13052] | 139 | this.trainingRows = original.trainingRows;
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[8323] | 140 | this.allowedInputVariables = original.allowedInputVariables;
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| 141 | this.alpha = original.alpha;
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| 142 | this.l = original.l;
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| 143 | this.x = original.x;
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| 144 | }
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[12702] | 145 | public GaussianProcessModel(IDataset ds, string targetVariable, IEnumerable<string> allowedInputVariables, IEnumerable<int> rows,
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[13147] | 146 | IEnumerable<double> hyp, IMeanFunction meanFunction, ICovarianceFunction covarianceFunction,
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| 147 | bool scaleInputs = true)
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[14027] | 148 | : base(targetVariable) {
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[8323] | 149 | this.name = ItemName;
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| 150 | this.description = ItemDescription;
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[8416] | 151 | this.meanFunction = (IMeanFunction)meanFunction.Clone();
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| 152 | this.covarianceFunction = (ICovarianceFunction)covarianceFunction.Clone();
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[8323] | 153 | this.allowedInputVariables = allowedInputVariables.ToArray();
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| 154 |
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| 155 |
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[8416] | 156 | int nVariables = this.allowedInputVariables.Length;
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[8982] | 157 | meanParameter = hyp
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[8416] | 158 | .Take(this.meanFunction.GetNumberOfParameters(nVariables))
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[8982] | 159 | .ToArray();
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| 160 |
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| 161 | covarianceParameter = hyp.Skip(this.meanFunction.GetNumberOfParameters(nVariables))
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| 162 | .Take(this.covarianceFunction.GetNumberOfParameters(nVariables))
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| 163 | .ToArray();
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[8473] | 164 | sqrSigmaNoise = Math.Exp(2.0 * hyp.Last());
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[13287] | 165 | try {
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| 166 | CalculateModel(ds, rows, scaleInputs);
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[13981] | 167 | }
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| 168 | catch (alglib.alglibexception ae) {
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[13287] | 169 | // wrap exception so that calling code doesn't have to know about alglib implementation
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| 170 | throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
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| 171 | }
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[8323] | 172 | }
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| 173 |
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[13147] | 174 | private void CalculateModel(IDataset ds, IEnumerable<int> rows, bool scaleInputs = true) {
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[13052] | 175 | this.trainingDataset = (IDataset)ds.Clone();
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| 176 | this.trainingRows = rows.ToArray();
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[13147] | 177 | this.inputScaling = scaleInputs ? new Scaling(ds, allowedInputVariables, rows) : null;
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[8323] | 178 |
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[13147] | 179 | x = GetData(ds, this.allowedInputVariables, this.trainingRows, this.inputScaling);
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| 180 |
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| 181 | IEnumerable<double> y;
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[14027] | 182 | y = ds.GetDoubleValues(TargetVariable, rows);
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[13147] | 183 |
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[8323] | 184 | int n = x.GetLength(0);
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| 185 |
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[13981] | 186 | var columns = Enumerable.Range(0, x.GetLength(1)).ToArray();
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[13052] | 187 | // calculate cholesky decomposed (lower triangular) covariance matrix
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[13981] | 188 | var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, columns);
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[13052] | 189 | this.l = CalculateL(x, cov, sqrSigmaNoise);
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| 190 |
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| 191 | // calculate mean
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[13981] | 192 | var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, columns);
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[8982] | 193 | double[] m = Enumerable.Range(0, x.GetLength(0))
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| 194 | .Select(r => mean.Mean(x, r))
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| 195 | .ToArray();
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| 196 |
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[8323] | 197 | // calculate sum of diagonal elements for likelihood
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| 198 | double diagSum = Enumerable.Range(0, n).Select(i => Math.Log(l[i, i])).Sum();
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| 199 |
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| 200 | // solve for alpha
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| 201 | double[] ym = y.Zip(m, (a, b) => a - b).ToArray();
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| 202 |
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[13052] | 203 | int info;
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| 204 | alglib.densesolverreport denseSolveRep;
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| 205 |
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[8323] | 206 | alglib.spdmatrixcholeskysolve(l, n, false, ym, out info, out denseSolveRep, out alpha);
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| 207 | for (int i = 0; i < alpha.Length; i++)
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| 208 | alpha[i] = alpha[i] / sqrSigmaNoise;
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| 209 | negativeLogLikelihood = 0.5 * Util.ScalarProd(ym, alpha) + diagSum + (n / 2.0) * Math.Log(2.0 * Math.PI * sqrSigmaNoise);
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| 210 |
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| 211 | // derivatives
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| 212 | int nAllowedVariables = x.GetLength(1);
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| 213 |
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[8463] | 214 | alglib.matinvreport matInvRep;
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[8475] | 215 | double[,] lCopy = new double[l.GetLength(0), l.GetLength(1)];
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| 216 | Array.Copy(l, lCopy, lCopy.Length);
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[8323] | 217 |
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[8475] | 218 | alglib.spdmatrixcholeskyinverse(ref lCopy, n, false, out info, out matInvRep);
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[8463] | 219 | if (info != 1) throw new ArgumentException("Can't invert matrix to calculate gradients.");
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[8323] | 220 | for (int i = 0; i < n; i++) {
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[8463] | 221 | for (int j = 0; j <= i; j++)
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[8475] | 222 | lCopy[i, j] = lCopy[i, j] / sqrSigmaNoise - alpha[i] * alpha[j];
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[8323] | 223 | }
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| 224 |
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[8475] | 225 | double noiseGradient = sqrSigmaNoise * Enumerable.Range(0, n).Select(i => lCopy[i, i]).Sum();
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[8323] | 226 |
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| 227 | double[] meanGradients = new double[meanFunction.GetNumberOfParameters(nAllowedVariables)];
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[8982] | 228 | for (int k = 0; k < meanGradients.Length; k++) {
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[13981] | 229 | var meanGrad = new double[alpha.Length];
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| 230 | for (int g = 0; g < meanGrad.Length; g++)
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| 231 | meanGrad[g] = mean.Gradient(x, g, k);
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[8982] | 232 | meanGradients[k] = -Util.ScalarProd(meanGrad, alpha);
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[8323] | 233 | }
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| 234 |
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| 235 | double[] covGradients = new double[covarianceFunction.GetNumberOfParameters(nAllowedVariables)];
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[8366] | 236 | if (covGradients.Length > 0) {
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| 237 | for (int i = 0; i < n; i++) {
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[8484] | 238 | for (int j = 0; j < i; j++) {
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[13981] | 239 | var g = cov.CovarianceGradient(x, i, j);
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[8484] | 240 | for (int k = 0; k < covGradients.Length; k++) {
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| 241 | covGradients[k] += lCopy[i, j] * g[k];
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[8366] | 242 | }
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[8323] | 243 | }
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[8484] | 244 |
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[13981] | 245 | var gDiag = cov.CovarianceGradient(x, i, i);
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[8484] | 246 | for (int k = 0; k < covGradients.Length; k++) {
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| 247 | // diag
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| 248 | covGradients[k] += 0.5 * lCopy[i, i] * gDiag[k];
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| 249 | }
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[8323] | 250 | }
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| 251 | }
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| 252 |
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[8484] | 253 | hyperparameterGradients =
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[8473] | 254 | meanGradients
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| 255 | .Concat(covGradients)
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| 256 | .Concat(new double[] { noiseGradient }).ToArray();
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[8484] | 257 |
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[8323] | 258 | }
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| 259 |
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[13147] | 260 | private static double[,] GetData(IDataset ds, IEnumerable<string> allowedInputs, IEnumerable<int> rows, Scaling scaling) {
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| 261 | if (scaling != null) {
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| 262 | return AlglibUtil.PrepareAndScaleInputMatrix(ds, allowedInputs, rows, scaling);
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| 263 | } else {
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| 264 | return AlglibUtil.PrepareInputMatrix(ds, allowedInputs, rows);
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| 265 | }
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[13052] | 266 | }
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[8323] | 267 |
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[13052] | 268 | private static double[,] CalculateL(double[,] x, ParameterizedCovarianceFunction cov, double sqrSigmaNoise) {
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| 269 | int n = x.GetLength(0);
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| 270 | var l = new double[n, n];
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| 271 |
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| 272 | // calculate covariances
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| 273 | for (int i = 0; i < n; i++) {
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| 274 | for (int j = i; j < n; j++) {
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| 275 | l[j, i] = cov.Covariance(x, i, j) / sqrSigmaNoise;
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| 276 | if (j == i) l[j, i] += 1.0;
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| 277 | }
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| 278 | }
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| 279 |
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| 280 | // cholesky decomposition
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| 281 | var res = alglib.trfac.spdmatrixcholesky(ref l, n, false);
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| 282 | if (!res) throw new ArgumentException("Matrix is not positive semidefinite");
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| 283 | return l;
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| 284 | }
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| 285 |
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| 286 |
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[8323] | 287 | public override IDeepCloneable Clone(Cloner cloner) {
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| 288 | return new GaussianProcessModel(this, cloner);
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| 289 | }
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| 290 |
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[8982] | 291 | // is called by the solution creator to set all parameter values of the covariance and mean function
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| 292 | // to the optimized values (necessary to make the values visible in the GUI)
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| 293 | public void FixParameters() {
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| 294 | covarianceFunction.SetParameter(covarianceParameter);
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| 295 | meanFunction.SetParameter(meanParameter);
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| 296 | covarianceParameter = new double[0];
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| 297 | meanParameter = new double[0];
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| 298 | }
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| 299 |
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[8323] | 300 | #region IRegressionModel Members
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[14027] | 301 | public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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[8323] | 302 | return GetEstimatedValuesHelper(dataset, rows);
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| 303 | }
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[14027] | 304 | public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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[8528] | 305 | return new GaussianProcessRegressionSolution(this, new RegressionProblemData(problemData));
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[8323] | 306 | }
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| 307 | #endregion
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| 308 |
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[8623] | 309 |
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[12702] | 310 | private IEnumerable<double> GetEstimatedValuesHelper(IDataset dataset, IEnumerable<int> rows) {
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[13287] | 311 | try {
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| 312 | if (x == null) {
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| 313 | x = GetData(trainingDataset, allowedInputVariables, trainingRows, inputScaling);
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| 314 | }
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| 315 | int n = x.GetLength(0);
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[13052] | 316 |
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[13287] | 317 | double[,] newX = GetData(dataset, allowedInputVariables, rows, inputScaling);
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| 318 | int newN = newX.GetLength(0);
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[13052] | 319 |
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[13981] | 320 | var Ks = new double[newN][];
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| 321 | var columns = Enumerable.Range(0, newX.GetLength(1)).ToArray();
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| 322 | var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, columns);
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[13287] | 323 | var ms = Enumerable.Range(0, newX.GetLength(0))
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| 324 | .Select(r => mean.Mean(newX, r))
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| 325 | .ToArray();
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[13981] | 326 | var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, columns);
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[13287] | 327 | for (int i = 0; i < newN; i++) {
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[13981] | 328 | Ks[i] = new double[n];
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[13287] | 329 | for (int j = 0; j < n; j++) {
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[13981] | 330 | Ks[i][j] = cov.CrossCovariance(x, newX, j, i);
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[13287] | 331 | }
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[8323] | 332 | }
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[13287] | 333 |
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| 334 | return Enumerable.Range(0, newN)
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[13981] | 335 | .Select(i => ms[i] + Util.ScalarProd(Ks[i], alpha));
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| 336 | }
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| 337 | catch (alglib.alglibexception ae) {
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[13287] | 338 | // wrap exception so that calling code doesn't have to know about alglib implementation
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| 339 | throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
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[8323] | 340 | }
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| 341 | }
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[8473] | 342 |
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[14166] | 343 | public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
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[13287] | 344 | try {
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| 345 | if (x == null) {
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| 346 | x = GetData(trainingDataset, allowedInputVariables, trainingRows, inputScaling);
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| 347 | }
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| 348 | int n = x.GetLength(0);
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[13052] | 349 |
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[13287] | 350 | var newX = GetData(dataset, allowedInputVariables, rows, inputScaling);
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| 351 | int newN = newX.GetLength(0);
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[8473] | 352 |
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[13287] | 353 | var kss = new double[newN];
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| 354 | double[,] sWKs = new double[n, newN];
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[13981] | 355 | var columns = Enumerable.Range(0, newX.GetLength(1)).ToArray();
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| 356 | var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, columns);
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[8473] | 357 |
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[13287] | 358 | if (l == null) {
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| 359 | l = CalculateL(x, cov, sqrSigmaNoise);
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| 360 | }
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[13052] | 361 |
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[13287] | 362 | // for stddev
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| 363 | for (int i = 0; i < newN; i++)
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| 364 | kss[i] = cov.Covariance(newX, i, i);
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[8473] | 365 |
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[13287] | 366 | for (int i = 0; i < newN; i++) {
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| 367 | for (int j = 0; j < n; j++) {
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| 368 | sWKs[j, i] = cov.CrossCovariance(x, newX, j, i) / Math.Sqrt(sqrSigmaNoise);
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| 369 | }
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[8473] | 370 | }
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| 371 |
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[13287] | 372 | // for stddev
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| 373 | alglib.ablas.rmatrixlefttrsm(n, newN, l, 0, 0, false, false, 0, ref sWKs, 0, 0);
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[8473] | 374 |
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[13287] | 375 | for (int i = 0; i < newN; i++) {
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[13981] | 376 | var col = Util.GetCol(sWKs, i).ToArray();
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| 377 | var sumV = Util.ScalarProd(col, col);
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[13287] | 378 | kss[i] += sqrSigmaNoise; // kss is V(f), add noise variance of predictive distibution to get V(y)
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| 379 | kss[i] -= sumV;
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| 380 | if (kss[i] < 0) kss[i] = 0;
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| 381 | }
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| 382 | return kss;
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[13981] | 383 | }
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| 384 | catch (alglib.alglibexception ae) {
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[13287] | 385 | // wrap exception so that calling code doesn't have to know about alglib implementation
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| 386 | throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
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[8473] | 387 | }
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| 388 | }
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[14027] | 389 |
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[8323] | 390 | }
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| 391 | }
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