source: trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegression.cs @ 17154

Last change on this file since 17154 was 17154, checked in by gkronber, 2 months ago

#2952: merged relevant revisions from branch to trunk

Merged revision(s) 17045-17153 from branches/2952_RF-ModelStorage/HeuristicLab.Algorithms.DataAnalysis:
#2952: Intermediate commit of refactoring RF models that is not yet finished.

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#2952: Corrected evaluation in RF models.

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#2952: Finished implementation of different RF models.

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#2952 Fixed triggering model recalculation when cloning.
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#2952: merged r17137 from trunk to branch
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#2952: re-added backwards compatibility code for very old versions of GBT and RF
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#2952: hide parameter in backwards compatibility hook
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17045-17153

File size: 11.8 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.Collections.Generic;
23using System.Linq;
24using System.Threading;
25using HEAL.Attic;
26using HeuristicLab.Algorithms.DataAnalysis.RandomForest;
27using HeuristicLab.Common;
28using HeuristicLab.Core;
29using HeuristicLab.Data;
30using HeuristicLab.Optimization;
31using HeuristicLab.Parameters;
32using HeuristicLab.Problems.DataAnalysis;
33
34namespace HeuristicLab.Algorithms.DataAnalysis {
35  /// <summary>
36  /// Random forest regression data analysis algorithm.
37  /// </summary>
38  [Item("Random Forest Regression (RF)", "Random forest regression data analysis algorithm (wrapper for ALGLIB).")]
39  [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 120)]
40  [StorableType("721CE0EB-82AF-4E49-9900-48E1C67B5E53")]
41  public sealed class RandomForestRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {
42    private const string RandomForestRegressionModelResultName = "Random forest regression solution";
43    private const string NumberOfTreesParameterName = "Number of trees";
44    private const string RParameterName = "R";
45    private const string MParameterName = "M";
46    private const string SeedParameterName = "Seed";
47    private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
48    private const string ModelCreationParameterName = "ModelCreation";
49
50    #region parameter properties
51    public IFixedValueParameter<IntValue> NumberOfTreesParameter {
52      get { return (IFixedValueParameter<IntValue>)Parameters[NumberOfTreesParameterName]; }
53    }
54    public IFixedValueParameter<DoubleValue> RParameter {
55      get { return (IFixedValueParameter<DoubleValue>)Parameters[RParameterName]; }
56    }
57    public IFixedValueParameter<DoubleValue> MParameter {
58      get { return (IFixedValueParameter<DoubleValue>)Parameters[MParameterName]; }
59    }
60    public IFixedValueParameter<IntValue> SeedParameter {
61      get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
62    }
63    public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
64      get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
65    }
66    private IFixedValueParameter<EnumValue<ModelCreation>> ModelCreationParameter {
67      get { return (IFixedValueParameter<EnumValue<ModelCreation>>)Parameters[ModelCreationParameterName]; }
68    }
69    #endregion
70    #region properties
71    public int NumberOfTrees {
72      get { return NumberOfTreesParameter.Value.Value; }
73      set { NumberOfTreesParameter.Value.Value = value; }
74    }
75    public double R {
76      get { return RParameter.Value.Value; }
77      set { RParameter.Value.Value = value; }
78    }
79    public double M {
80      get { return MParameter.Value.Value; }
81      set { MParameter.Value.Value = value; }
82    }
83    public int Seed {
84      get { return SeedParameter.Value.Value; }
85      set { SeedParameter.Value.Value = value; }
86    }
87    public bool SetSeedRandomly {
88      get { return SetSeedRandomlyParameter.Value.Value; }
89      set { SetSeedRandomlyParameter.Value.Value = value; }
90    }
91    public ModelCreation ModelCreation {
92      get { return ModelCreationParameter.Value.Value; }
93      set { ModelCreationParameter.Value.Value = value; }
94    }
95    #endregion
96    [StorableConstructor]
97    private RandomForestRegression(StorableConstructorFlag _) : base(_) { }
98    private RandomForestRegression(RandomForestRegression original, Cloner cloner)
99      : base(original, cloner) {
100    }
101
102    public RandomForestRegression()
103      : base() {
104      Parameters.Add(new FixedValueParameter<IntValue>(NumberOfTreesParameterName, "The number of trees in the forest. Should be between 50 and 100", new IntValue(50)));
105      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)));
106      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)));
107      Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
108      Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
109      Parameters.Add(new FixedValueParameter<EnumValue<ModelCreation>>(ModelCreationParameterName, "Defines the results produced at the end of the run (Surrogate => Less disk space, lazy recalculation of model)", new EnumValue<ModelCreation>(ModelCreation.Model)));
110      Parameters[ModelCreationParameterName].Hidden = true;
111
112      Problem = new RegressionProblem();
113    }
114
115    [StorableHook(HookType.AfterDeserialization)]
116    private void AfterDeserialization() {
117      // BackwardsCompatibility3.3
118      #region Backwards compatible code, remove with 3.4
119      if (!Parameters.ContainsKey(MParameterName))
120        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)));
121      if (!Parameters.ContainsKey(SeedParameterName))
122        Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
123      if (!Parameters.ContainsKey((SetSeedRandomlyParameterName)))
124        Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
125
126      // parameter type has been changed
127      if (Parameters.ContainsKey("CreateSolution")) {
128        var createSolutionParam = Parameters["CreateSolution"] as FixedValueParameter<BoolValue>;
129        Parameters.Remove(createSolutionParam);
130
131        ModelCreation value = createSolutionParam.Value.Value ? ModelCreation.Model : ModelCreation.QualityOnly;
132        Parameters.Add(new FixedValueParameter<EnumValue<ModelCreation>>(ModelCreationParameterName, "Defines the results produced at the end of the run (Surrogate => Less disk space, lazy recalculation of model)", new EnumValue<ModelCreation>(value)));
133        Parameters[ModelCreationParameterName].Hidden = true;
134      } else if (!Parameters.ContainsKey(ModelCreationParameterName)) {
135        // very old version contains neither ModelCreationParameter nor CreateSolutionParameter
136        Parameters.Add(new FixedValueParameter<EnumValue<ModelCreation>>(ModelCreationParameterName, "Defines the results produced at the end of the run (Surrogate => Less disk space, lazy recalculation of model)", new EnumValue<ModelCreation>(ModelCreation.Model)));
137        Parameters[ModelCreationParameterName].Hidden = true;
138      }
139      #endregion
140    }
141
142    public override IDeepCloneable Clone(Cloner cloner) {
143      return new RandomForestRegression(this, cloner);
144    }
145
146    #region random forest
147    protected override void Run(CancellationToken cancellationToken) {
148      double rmsError, avgRelError, outOfBagRmsError, outOfBagAvgRelError;
149      if (SetSeedRandomly) Seed = Random.RandomSeedGenerator.GetSeed();
150      var model = CreateRandomForestRegressionModel(Problem.ProblemData, NumberOfTrees, R, M, Seed,
151        out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
152
153      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)));
154      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)));
155      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)));
156      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)));
157
158      IRegressionSolution solution = null;
159      if (ModelCreation == ModelCreation.Model) {
160        solution = model.CreateRegressionSolution(Problem.ProblemData);
161      } else if (ModelCreation == ModelCreation.SurrogateModel) {
162        var problemData = Problem.ProblemData;
163        var surrogateModel = new RandomForestModelSurrogate(model, problemData.TargetVariable, problemData, Seed, NumberOfTrees, R, M);
164        solution = surrogateModel.CreateRegressionSolution(problemData);
165      }
166
167      if (solution != null) {
168        Results.Add(new Result(RandomForestRegressionModelResultName, "The random forest regression solution.", solution));
169      }
170    }
171
172
173    // keep for compatibility with old API
174    public static RandomForestRegressionSolution CreateRandomForestRegressionSolution(IRegressionProblemData problemData, int nTrees, double r, double m, int seed,
175      out double rmsError, out double avgRelError, out double outOfBagRmsError, out double outOfBagAvgRelError) {
176      var model = CreateRandomForestRegressionModel(problemData, nTrees, r, m, seed,
177        out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
178      return new RandomForestRegressionSolution(model, (IRegressionProblemData)problemData.Clone());
179    }
180
181    public static RandomForestModelFull CreateRandomForestRegressionModel(IRegressionProblemData problemData, int nTrees,
182     double r, double m, int seed,
183     out double rmsError, out double avgRelError, out double outOfBagRmsError, out double outOfBagAvgRelError) {
184      var model = CreateRandomForestRegressionModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed, out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
185      return model;
186    }
187
188    public static RandomForestModelFull CreateRandomForestRegressionModel(IRegressionProblemData problemData, IEnumerable<int> trainingIndices, int nTrees, double r, double m, int seed,
189    out double rmsError, out double avgRelError, out double outOfBagRmsError, out double outOfBagAvgRelError) {
190
191      var variables = problemData.AllowedInputVariables.Concat(new string[] { problemData.TargetVariable });
192      double[,] inputMatrix = problemData.Dataset.ToArray(variables, trainingIndices);
193
194      alglib.dfreport rep;
195      var dForest = RandomForestUtil.CreateRandomForestModel(seed, inputMatrix, nTrees, r, m, 1, out rep);
196
197      rmsError = rep.rmserror;
198      outOfBagRmsError = rep.oobrmserror;
199      avgRelError = rep.avgrelerror;
200      outOfBagAvgRelError = rep.oobavgrelerror;
201
202      return new RandomForestModelFull(dForest, problemData.TargetVariable, problemData.AllowedInputVariables);
203    }
204
205    #endregion
206  }
207}
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