source: branches/2847_M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/LeafTypes/ComplexLeaf.cs

Last change on this file was 16847, checked in by gkronber, 5 months ago

#2847: made some minor changes while reviewing

File size: 3.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2017 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Linq;
24using System.Threading;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Parameters;
28using HeuristicLab.Problems.DataAnalysis;
29using HEAL.Attic;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  [StorableType("F34A0ED9-2CF6-4DEE-850D-08790663B66D")]
33  [Item("ComplexLeaf", "A leaf type that uses an arbitrary RegressionAlgorithm to create leaf models")]
34  public class ComplexLeaf : LeafBase {
35    public const string RegressionParameterName = "Regression";
36    public IValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>> RegressionParameter {
37      get { return (IValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>>)Parameters[RegressionParameterName]; }
38    }
39    public IDataAnalysisAlgorithm<IRegressionProblem> Regression {
40      get { return RegressionParameter.Value; }
41      set { RegressionParameter.Value = value; }
42    }
43
44    #region Constructors & Cloning
45    [StorableConstructor]
46    private ComplexLeaf(StorableConstructorFlag _) : base(_) { }
47    private ComplexLeaf(ComplexLeaf original, Cloner cloner) : base(original, cloner) { }
48    public ComplexLeaf() {
49      var regression = new KernelRidgeRegression();
50      Parameters.Add(new ValueParameter<IDataAnalysisAlgorithm<IRegressionProblem>>(RegressionParameterName, "The algorithm creating RegressionModels", regression));
51    }
52    public override IDeepCloneable Clone(Cloner cloner) {
53      return new ComplexLeaf(this, cloner);
54    }
55    #endregion
56
57    #region IModelType
58    public override bool ProvidesConfidence {
59      get { return false; }
60    }
61    public override IRegressionModel Build(IRegressionProblemData pd, IRandom random, CancellationToken cancellationToken, out int noParameters) {
62      if (pd.Dataset.Rows < MinLeafSize(pd)) throw new ArgumentException("The number of training instances is too small to create a linear model");
63      noParameters = pd.Dataset.Rows + 1;
64      Regression.Problem = new RegressionProblem { ProblemData = pd };
65      var res = RegressionTreeUtilities.RunSubAlgorithm(Regression, random.Next(), cancellationToken);
66      var t = res.Select(x => x.Value).OfType<IRegressionSolution>().FirstOrDefault();
67      if (t == null) throw new ArgumentException("No RegressionSolution was provided by the algorithm");
68      return t.Model;
69    }
70    public override int MinLeafSize(IRegressionProblemData pd) {
71      return 3;
72    }
73    #endregion
74  }
75}
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