[15064] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2016 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.Linq;
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| 24 | using HeuristicLab.Algorithms.DataAnalysis;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Operators;
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| 29 | using HeuristicLab.Optimization;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.Problems.DataAnalysis;
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| 33 |
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| 34 | namespace HeuristicLab.Algorithms.EGO {
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| 35 | /// <summary>
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| 36 | /// A base class for operators that manipulate real-valued vectors.
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| 37 | /// </summary>
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| 38 | [Item("ModelBuilder", "Builds a model from a dataset and a given RegressionAlgorithm")]
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| 39 | [StorableClass]
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| 40 | public class ModelBuilder : InstrumentedOperator, IStochasticOperator {
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| 41 | public override bool CanChangeName => true;
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| 42 |
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| 43 | public ILookupParameter<IDataAnalysisAlgorithm<IRegressionProblem>> RegressionAlgorithmParameter => (ILookupParameter<IDataAnalysisAlgorithm<IRegressionProblem>>)Parameters["RegressionAlgorithm"];
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| 44 | public ILookupParameter<IRegressionSolution> ModelParameter => (ILookupParameter<IRegressionSolution>)Parameters["Model"];
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| 45 | public ILookupParameter<ModifiableDataset> DatasetParameter => (ILookupParameter<ModifiableDataset>)Parameters["Dataset"];
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| 46 | public ILookupParameter<IRandom> RandomParameter => (ILookupParameter<IRandom>)Parameters["Random"];
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| 47 | public ILookupParameter<IntValue> MaxModelSizeParameter => (ILookupParameter<IntValue>)Parameters["Maximal Model Size"];
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| 48 | public ILookupParameter<DoubleMatrix> InfillBoundsParameter => (ILookupParameter<DoubleMatrix>)Parameters["InfillBounds"];
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| 49 |
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| 50 | [StorableConstructor]
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| 51 | protected ModelBuilder(bool deserializing) : base(deserializing) { }
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| 52 | protected ModelBuilder(ModelBuilder original, Cloner cloner) : base(original, cloner) { }
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| 53 | public ModelBuilder() {
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| 54 | Parameters.Add(new LookupParameter<IDataAnalysisAlgorithm<IRegressionProblem>>("RegressionAlgorithm", "The algorithm used to build a model") { Hidden = true });
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| 55 | Parameters.Add(new LookupParameter<IRegressionSolution>("Model", "The resulting model") { Hidden = true });
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| 56 | Parameters.Add(new LookupParameter<ModifiableDataset>("Dataset", "The Dataset from which the model is created") { Hidden = true });
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| 57 | Parameters.Add(new LookupParameter<IRandom>("Random", "A random number generator") { Hidden = true });
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| 58 | Parameters.Add(new LookupParameter<IntValue>("Maximal Model Size", "The maximum number of sample points used to build the model (Set -1 for infinite size") { Hidden = true });
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| 59 | Parameters.Add(new LookupParameter<DoubleMatrix>("InfillBounds", "The bounds applied for infill solving") { Hidden = true });
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| 60 | }
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| 61 |
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| 62 | public override IDeepCloneable Clone(Cloner cloner) {
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| 63 | return new ModelBuilder(this, cloner);
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| 64 | }
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| 65 |
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| 66 | public override IOperation InstrumentedApply() {
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| 67 | var regressionAlg = RegressionAlgorithmParameter.ActualValue;
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| 68 | IDataset data = DatasetParameter.ActualValue;
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| 69 | var random = RandomParameter.ActualValue;
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| 70 | var oldModel = ModelParameter.ActualValue;
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| 71 | var max = MaxModelSizeParameter.ActualValue.Value;
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| 72 | if (data.Rows > max && max > 0) {
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| 73 | data = SelectBestSamples(data, max);
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| 74 | InfillBoundsParameter.ActualValue = GetBounds(data);
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| 75 | }
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| 76 | ModelParameter.ActualValue = BuildModel(random, regressionAlg, data, oldModel);
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| 77 | return base.InstrumentedApply();
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| 78 | }
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| 79 |
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| 80 | private DoubleMatrix GetBounds(IDataset data) {
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| 81 | var res = new DoubleMatrix(data.Columns - 1, 2);
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| 82 | var names = data.DoubleVariables.ToArray();
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| 83 | for (var i = 0; i < names.Length - 1; i++) {
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| 84 | res[i, 0] = data.GetDoubleValues(names[i]).Min();
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| 85 | res[i, 1] = data.GetDoubleValues(names[i]).Max();
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| 86 | }
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| 87 | return res;
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| 88 | }
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| 89 |
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| 90 | private static Dataset SelectBestSamples(IDataset data, int max) {
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| 91 | var bestSampleIndices = data.GetDoubleValues("output").Select((d, i) => Tuple.Create(d, i)).OrderBy(x => x.Item1).Take(max).Select(x => x.Item2).ToArray();
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| 92 | return new Dataset(data.VariableNames, data.VariableNames.Select(v => data.GetDoubleValues(v, bestSampleIndices).ToList()));
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| 93 | }
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| 94 |
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| 95 | private static IRegressionSolution BuildModel(IRandom random, IDataAnalysisAlgorithm<IRegressionProblem> regressionAlgorithm, IDataset dataset, IRegressionSolution oldSolution) {
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| 96 | //var dataset = EgoUtilities.GetDataSet(dataSamples, RemoveDuplicates);
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| 97 | var problemdata = new RegressionProblemData(dataset, dataset.VariableNames.Where(x => !x.Equals("output")), "output");
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| 98 | problemdata.TrainingPartition.Start = 0;
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| 99 | problemdata.TrainingPartition.End = dataset.Rows;
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| 100 | problemdata.TestPartition.Start = dataset.Rows;
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| 101 | problemdata.TestPartition.End = dataset.Rows;
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| 102 |
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| 103 | //train
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| 104 | var problem = (RegressionProblem)regressionAlgorithm.Problem;
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| 105 | problem.ProblemDataParameter.Value = problemdata;
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| 106 | var i = 0;
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| 107 | IRegressionSolution solution = null;
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| 108 |
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| 109 | while (solution == null && i++ < 100) {
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| 110 | var results = EgoUtilities.SyncRunSubAlgorithm(regressionAlgorithm, random.Next(int.MaxValue));
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| 111 | solution = results.Select(x => x.Value).OfType<IRegressionSolution>().SingleOrDefault();
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| 112 | }
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| 113 |
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| 114 | //try creating a model with old hyperparameters and new dataset;
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| 115 | var gp = regressionAlgorithm as GaussianProcessRegression;
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| 116 | var oldmodel = oldSolution as GaussianProcessRegressionSolution;
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| 117 | if (gp != null && oldmodel != null) {
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| 118 | var mean = (IMeanFunction)oldmodel.Model.MeanFunction.Clone();
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| 119 | var cov = (ICovarianceFunction)oldmodel.Model.CovarianceFunction.Clone();
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| 120 | try {
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| 121 | var model = new GaussianProcessModel(problemdata.Dataset, problemdata.TargetVariable,
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| 122 | problemdata.AllowedInputVariables, problemdata.TrainingIndices, new[] { 0.0 }, mean, cov);
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| 123 | model.FixParameters();
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| 124 | var sol = new GaussianProcessRegressionSolution(model, problemdata);
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| 125 | if (solution == null || solution.TrainingMeanSquaredError > sol.TrainingMeanSquaredError) {
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| 126 | solution = sol;
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| 127 | }
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| 128 | }
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| 129 | catch (ArgumentException) { }
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| 130 | }
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| 131 |
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| 132 | regressionAlgorithm.Runs.Clear();
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| 133 | return solution;
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| 134 |
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| 135 | }
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| 136 | }
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| 137 | }
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