#region License Information /* HeuristicLab * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * and the BEACON Center for the Study of Evolution in Action. * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] // this class is used as a surrogate for persistence of an actual GBT model // since the actual GBT model would be very large when persisted we only store all necessary information to // recalculate the actual GBT model on demand [Item("Gradient boosted tree model", "")] public sealed class GradientBoostedTreesModelSurrogate : NamedItem, IRegressionModel { // don't store the actual model! private IRegressionModel actualModel; // the actual model is only recalculated when necessary [Storable] private readonly IRegressionProblemData trainingProblemData; [Storable] private readonly uint seed; [Storable] private ILossFunction lossFunction; [Storable] private double r; [Storable] private double m; [Storable] private double nu; [Storable] private int iterations; [Storable] private int maxSize; [StorableConstructor] private GradientBoostedTreesModelSurrogate(bool deserializing) : base(deserializing) { } private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner) : base(original, cloner) { if (original.actualModel != null) this.actualModel = cloner.Clone(original.actualModel); this.trainingProblemData = cloner.Clone(original.trainingProblemData); this.lossFunction = cloner.Clone(original.lossFunction); this.seed = original.seed; this.iterations = original.iterations; this.maxSize = original.maxSize; this.r = original.r; this.m = original.m; this.nu = original.nu; } // create only the surrogate model without an actual model public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed, ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu) : base("Gradient boosted tree model", string.Empty) { this.trainingProblemData = trainingProblemData; this.seed = seed; this.lossFunction = lossFunction; this.iterations = iterations; this.maxSize = maxSize; this.r = r; this.m = m; this.nu = nu; } // wrap an actual model in a surrograte public GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed, ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu, IRegressionModel model) : this(trainingProblemData, seed, lossFunction, iterations, maxSize, r, m, nu) { this.actualModel = model; } public override IDeepCloneable Clone(Cloner cloner) { return new GradientBoostedTreesModelSurrogate(this, cloner); } // forward message to actual model (recalculate model first if necessary) public IEnumerable GetEstimatedValues(IDataset dataset, IEnumerable rows) { if (actualModel == null) actualModel = RecalculateModel(); return actualModel.GetEstimatedValues(dataset, rows); } public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) { return new RegressionSolution(this, (IRegressionProblemData)problemData.Clone()); } private IRegressionModel RecalculateModel() { return GradientBoostedTreesAlgorithmStatic.TrainGbm(trainingProblemData, lossFunction, maxSize, nu, r, m, iterations, seed).Model; } } }