source: stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesModelSurrogate.cs @ 17156

Last change on this file since 17156 was 17156, checked in by gkronber, 2 months ago

#2883: merged r17030, r17032, r17033, r17043, r17044, r17137 from trunk to stable

File size: 5.7 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 * and the BEACON Center for the Study of Evolution in Action.
5 *
6 * This file is part of HeuristicLab.
7 *
8 * HeuristicLab is free software: you can redistribute it and/or modify
9 * it under the terms of the GNU General Public License as published by
10 * the Free Software Foundation, either version 3 of the License, or
11 * (at your option) any later version.
12 *
13 * HeuristicLab is distributed in the hope that it will be useful,
14 * but WITHOUT ANY WARRANTY; without even the implied warranty of
15 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
16 * GNU General Public License for more details.
17 *
18 * You should have received a copy of the GNU General Public License
19 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
20 */
21#endregion
22
23using System;
24using System.Collections.Generic;
25using System.Linq;
26using HeuristicLab.Common;
27using HeuristicLab.Core;
28using HEAL.Attic;
29using HeuristicLab.Problems.DataAnalysis;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableType("1BF7BEFB-6739-48AA-89BC-B632E72D148C")]
33  // this class is used as a surrogate for persistence of an actual GBT model
34  // since the actual GBT model would be very large when persisted we only store all necessary information to
35  // recalculate the actual GBT model on demand
36  [Item("Gradient boosted tree model", "")]
37  public sealed class GradientBoostedTreesModelSurrogate : RegressionModel, IGradientBoostedTreesModel {
38    // don't store the actual model!
39    // the actual model is only recalculated when necessary
40    private readonly Lazy<IGradientBoostedTreesModel> actualModel;
41    private IGradientBoostedTreesModel ActualModel {
42      get { return actualModel.Value; }
43    }
44
45    [Storable]
46    private readonly IRegressionProblemData trainingProblemData;
47    [Storable]
48    private readonly uint seed;
49    [Storable]
50    private ILossFunction lossFunction;
51    [Storable]
52    private double r;
53    [Storable]
54    private double m;
55    [Storable]
56    private double nu;
57    [Storable]
58    private int iterations;
59    [Storable]
60    private int maxSize;
61
62
63    public override IEnumerable<string> VariablesUsedForPrediction {
64      get {
65        return ActualModel.Models.SelectMany(x => x.VariablesUsedForPrediction).Distinct().OrderBy(x => x);
66      }
67    }
68
69    [StorableConstructor]
70    private GradientBoostedTreesModelSurrogate(StorableConstructorFlag _) : base(_) {
71      actualModel = new Lazy<IGradientBoostedTreesModel>(() => RecalculateModel());
72    }
73
74    private GradientBoostedTreesModelSurrogate(GradientBoostedTreesModelSurrogate original, Cloner cloner)
75      : base(original, cloner) {
76      IGradientBoostedTreesModel clonedModel = null;
77      if (original.actualModel.IsValueCreated) clonedModel = cloner.Clone(original.ActualModel);
78      actualModel = new Lazy<IGradientBoostedTreesModel>(CreateLazyInitFunc(clonedModel)); // only capture clonedModel in the closure
79
80      this.trainingProblemData = cloner.Clone(original.trainingProblemData);
81      this.lossFunction = cloner.Clone(original.lossFunction);
82      this.seed = original.seed;
83      this.iterations = original.iterations;
84      this.maxSize = original.maxSize;
85      this.r = original.r;
86      this.m = original.m;
87      this.nu = original.nu;
88    }
89
90    private Func<IGradientBoostedTreesModel> CreateLazyInitFunc(IGradientBoostedTreesModel clonedModel) {
91      return () => {
92        return clonedModel ?? RecalculateModel();
93      };
94    }
95
96    // create only the surrogate model without an actual model
97    private GradientBoostedTreesModelSurrogate(IRegressionProblemData trainingProblemData, uint seed,
98      ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu)
99      : base(trainingProblemData.TargetVariable, "Gradient boosted tree model", string.Empty) {
100      this.trainingProblemData = trainingProblemData;
101      this.seed = seed;
102      this.lossFunction = lossFunction;
103      this.iterations = iterations;
104      this.maxSize = maxSize;
105      this.r = r;
106      this.m = m;
107      this.nu = nu;
108
109      actualModel = new Lazy<IGradientBoostedTreesModel>(() => RecalculateModel());
110    }
111
112    // wrap an actual model in a surrograte
113    public GradientBoostedTreesModelSurrogate(IGradientBoostedTreesModel model, IRegressionProblemData trainingProblemData, uint seed,
114      ILossFunction lossFunction, int iterations, int maxSize, double r, double m, double nu)
115      : this(trainingProblemData, seed, lossFunction, iterations, maxSize, r, m, nu) {
116      actualModel = new Lazy<IGradientBoostedTreesModel>(() => model);
117    }
118
119    public override IDeepCloneable Clone(Cloner cloner) {
120      return new GradientBoostedTreesModelSurrogate(this, cloner);
121    }
122
123    // forward message to actual model (recalculate model first if necessary)
124    public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
125      return ActualModel.GetEstimatedValues(dataset, rows);
126    }
127
128    public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
129      return new RegressionSolution(this, (IRegressionProblemData)problemData.Clone());
130    }
131
132    private IGradientBoostedTreesModel RecalculateModel() {
133      return GradientBoostedTreesAlgorithmStatic.TrainGbm(trainingProblemData, lossFunction, maxSize, nu, r, m, iterations, seed).Model;
134    }
135
136    public IEnumerable<IRegressionModel> Models {
137      get {
138        return ActualModel.Models;
139      }
140    }
141
142    public IEnumerable<double> Weights {
143      get {
144        return ActualModel.Weights;
145      }
146    }
147  }
148}
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