#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; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Algorithms.DataAnalysis { [StorableClass] [Item("Gradient boosted tree model", "")] // this is essentially a collection of weighted regression models public sealed class GradientBoostedTreesModel : RegressionModel, IGradientBoostedTreesModel { // BackwardsCompatibility3.4 for allowing deserialization & serialization of old models #region Backwards compatible code, remove with 3.5 private bool isCompatibilityLoaded = false; // only set to true if the model is deserialized from the old format, needed to make sure that information is serialized again if it was loaded from the old format [Storable(Name = "models")] private IList __persistedModels { set { this.isCompatibilityLoaded = true; this.models.Clear(); foreach (var m in value) this.models.Add(m); } get { if (this.isCompatibilityLoaded) return models; else return null; } } [Storable(Name = "weights")] private IList __persistedWeights { set { this.isCompatibilityLoaded = true; this.weights.Clear(); foreach (var w in value) this.weights.Add(w); } get { if (this.isCompatibilityLoaded) return weights; else return null; } } #endregion public override IEnumerable VariablesUsedForPrediction { get { return models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x); } } private readonly IList models; public IEnumerable Models { get { return models; } } private readonly IList weights; public IEnumerable Weights { get { return weights; } } [StorableConstructor] private GradientBoostedTreesModel(bool deserializing) : base(deserializing) { models = new List(); weights = new List(); } private GradientBoostedTreesModel(GradientBoostedTreesModel original, Cloner cloner) : base(original, cloner) { this.weights = new List(original.weights); this.models = new List(original.models.Select(m => cloner.Clone(m))); this.isCompatibilityLoaded = original.isCompatibilityLoaded; } [Obsolete("The constructor of GBTModel should not be used directly anymore (use GBTModelSurrogate instead)")] internal GradientBoostedTreesModel(IEnumerable models, IEnumerable weights) : base(string.Empty, "Gradient boosted tree model", string.Empty) { this.models = new List(models); this.weights = new List(weights); if (this.models.Count != this.weights.Count) throw new ArgumentException(); } public override IDeepCloneable Clone(Cloner cloner) { return new GradientBoostedTreesModel(this, cloner); } public override IEnumerable GetEstimatedValues(IDataset dataset, IEnumerable rows) { // allocate target array go over all models and add up weighted estimation for each row if (!rows.Any()) return Enumerable.Empty(); // return immediately if rows is empty. This prevents multiple iteration over lazy rows enumerable. // (which essentially looks up indexes in a dictionary) var res = new double[rows.Count()]; for (int i = 0; i < models.Count; i++) { var w = weights[i]; var m = models[i]; int r = 0; foreach (var est in m.GetEstimatedValues(dataset, rows)) { res[r++] += w * est; } } return res; } public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) { return new RegressionSolution(this, (IRegressionProblemData)problemData.Clone()); } } }