source: branches/2520_PersistenceReintegration/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs @ 16453

Last change on this file since 16453 was 16453, checked in by jkarder, 10 months ago

#2520: updated year of copyrights

File size: 9.5 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2019 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.Threading;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Optimization;
27using HeuristicLab.Parameters;
28using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
29using HeuristicLab.Problems.DataAnalysis;
30
31namespace HeuristicLab.Algorithms.DataAnalysis {
32  /// <summary>
33  /// Random forest regression data analysis algorithm.
34  /// </summary>
35  [Item("Random Forest Regression (RF)", "Random forest regression data analysis algorithm (wrapper for ALGLIB).")]
36  [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 120)]
37  [StorableClass]
38  public sealed class RandomForestRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
39    private const string RandomForestRegressionModelResultName = "Random forest regression solution";
40    private const string NumberOfTreesParameterName = "Number of trees";
41    private const string RParameterName = "R";
42    private const string MParameterName = "M";
43    private const string SeedParameterName = "Seed";
44    private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
45    private const string CreateSolutionParameterName = "CreateSolution";
46
47    #region parameter properties
48    public IFixedValueParameter<IntValue> NumberOfTreesParameter {
49      get { return (IFixedValueParameter<IntValue>)Parameters[NumberOfTreesParameterName]; }
50    }
51    public IFixedValueParameter<DoubleValue> RParameter {
52      get { return (IFixedValueParameter<DoubleValue>)Parameters[RParameterName]; }
53    }
54    public IFixedValueParameter<DoubleValue> MParameter {
55      get { return (IFixedValueParameter<DoubleValue>)Parameters[MParameterName]; }
56    }
57    public IFixedValueParameter<IntValue> SeedParameter {
58      get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
59    }
60    public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
61      get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
62    }
63    public IFixedValueParameter<BoolValue> CreateSolutionParameter {
64      get { return (IFixedValueParameter<BoolValue>)Parameters[CreateSolutionParameterName]; }
65    }
66    #endregion
67    #region properties
68    public int NumberOfTrees {
69      get { return NumberOfTreesParameter.Value.Value; }
70      set { NumberOfTreesParameter.Value.Value = value; }
71    }
72    public double R {
73      get { return RParameter.Value.Value; }
74      set { RParameter.Value.Value = value; }
75    }
76    public double M {
77      get { return MParameter.Value.Value; }
78      set { MParameter.Value.Value = value; }
79    }
80    public int Seed {
81      get { return SeedParameter.Value.Value; }
82      set { SeedParameter.Value.Value = value; }
83    }
84    public bool SetSeedRandomly {
85      get { return SetSeedRandomlyParameter.Value.Value; }
86      set { SetSeedRandomlyParameter.Value.Value = value; }
87    }
88    public bool CreateSolution {
89      get { return CreateSolutionParameter.Value.Value; }
90      set { CreateSolutionParameter.Value.Value = value; }
91    }
92    #endregion
93    [StorableConstructor]
94    private RandomForestRegression(bool deserializing) : base(deserializing) { }
95    private RandomForestRegression(RandomForestRegression original, Cloner cloner)
96      : base(original, cloner) {
97    }
98
99    public RandomForestRegression()
100      : base() {
101      Parameters.Add(new FixedValueParameter<IntValue>(NumberOfTreesParameterName, "The number of trees in the forest. Should be between 50 and 100", new IntValue(50)));
102      Parameters.Add(new FixedValueParameter<DoubleValue>(RParameterName, "The ratio of the training set that will be used in the construction of individual trees (0<r<=1). Should be adjusted depending on the noise level in the dataset in the range from 0.66 (low noise) to 0.05 (high noise). This parameter should be adjusted to achieve good generalization error.", new DoubleValue(0.3)));
103      Parameters.Add(new FixedValueParameter<DoubleValue>(MParameterName, "The ratio of features that will be used in the construction of individual trees (0<m<=1)", new DoubleValue(0.5)));
104      Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
105      Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
106      Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
107      Parameters[CreateSolutionParameterName].Hidden = true;
108
109      Problem = new RegressionProblem();
110    }
111
112    [StorableHook(HookType.AfterDeserialization)]
113    private void AfterDeserialization() {
114      // BackwardsCompatibility3.3
115      #region Backwards compatible code, remove with 3.4
116      if (!Parameters.ContainsKey(MParameterName))
117        Parameters.Add(new FixedValueParameter<DoubleValue>(MParameterName, "The ratio of features that will be used in the construction of individual trees (0<m<=1)", new DoubleValue(0.5)));
118      if (!Parameters.ContainsKey(SeedParameterName))
119        Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
120      if (!Parameters.ContainsKey((SetSeedRandomlyParameterName)))
121        Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
122      if (!Parameters.ContainsKey(CreateSolutionParameterName)) {
123        Parameters.Add(new FixedValueParameter<BoolValue>(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true)));
124        Parameters[CreateSolutionParameterName].Hidden = true;
125      }
126      #endregion
127    }
128
129    public override IDeepCloneable Clone(Cloner cloner) {
130      return new RandomForestRegression(this, cloner);
131    }
132
133    #region random forest
134    protected override void Run(CancellationToken cancellationToken) {
135      double rmsError, avgRelError, outOfBagRmsError, outOfBagAvgRelError;
136      if (SetSeedRandomly) Seed = Random.RandomSeedGenerator.GetSeed();
137      var model = CreateRandomForestRegressionModel(Problem.ProblemData, NumberOfTrees, R, M, Seed,
138        out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
139
140      Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the random forest regression solution on the training set.", new DoubleValue(rmsError)));
141      Results.Add(new Result("Average relative error", "The average of relative errors of the random forest regression solution on the training set.", new PercentValue(avgRelError)));
142      Results.Add(new Result("Root mean square error (out-of-bag)", "The out-of-bag root of the mean of squared errors of the random forest regression solution.", new DoubleValue(outOfBagRmsError)));
143      Results.Add(new Result("Average relative error (out-of-bag)", "The out-of-bag average of relative errors of the random forest regression solution.", new PercentValue(outOfBagAvgRelError)));
144
145      if (CreateSolution) {
146        var solution = new RandomForestRegressionSolution(model, (IRegressionProblemData)Problem.ProblemData.Clone());
147        Results.Add(new Result(RandomForestRegressionModelResultName, "The random forest regression solution.", solution));
148      }
149    }
150
151    // keep for compatibility with old API
152    public static RandomForestRegressionSolution CreateRandomForestRegressionSolution(IRegressionProblemData problemData, int nTrees, double r, double m, int seed,
153      out double rmsError, out double avgRelError, out double outOfBagRmsError, out double outOfBagAvgRelError) {
154      var model = CreateRandomForestRegressionModel(problemData, nTrees, r, m, seed,
155        out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
156      return new RandomForestRegressionSolution(model, (IRegressionProblemData)problemData.Clone());
157    }
158
159    public static RandomForestModel CreateRandomForestRegressionModel(IRegressionProblemData problemData, int nTrees,
160      double r, double m, int seed,
161      out double rmsError, out double avgRelError, out double outOfBagRmsError, out double outOfBagAvgRelError) {
162      return RandomForestModel.CreateRegressionModel(problemData, nTrees, r, m, seed,
163        rmsError: out rmsError, avgRelError: out avgRelError, outOfBagRmsError: out outOfBagRmsError, outOfBagAvgRelError: out outOfBagAvgRelError);
164    }
165
166    #endregion
167  }
168}
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