[6240] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[14186] | 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[6240] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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[15127] | 27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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[6240] | 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 | using HeuristicLab.Problems.DataAnalysis;
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[15127] | 30 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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[6240] | 31 |
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| 32 | namespace HeuristicLab.Algorithms.DataAnalysis {
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| 33 | /// <summary>
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[6241] | 34 | /// Represents a random forest model for regression and classification
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[6240] | 35 | /// </summary>
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| 36 | [StorableClass]
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[6241] | 37 | [Item("RandomForestModel", "Represents a random forest for regression and classification.")]
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[14027] | 38 | public sealed class RandomForestModel : ClassificationModel, IRandomForestModel {
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[11006] | 39 | // not persisted
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[6240] | 40 | private alglib.decisionforest randomForest;
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[11006] | 41 | private alglib.decisionforest RandomForest {
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| 42 | get {
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| 43 | // recalculate lazily
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| 44 | if (randomForest.innerobj.trees == null || randomForest.innerobj.trees.Length == 0) RecalculateModel();
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| 45 | return randomForest;
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[6240] | 46 | }
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| 47 | }
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| 48 |
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[14027] | 49 | public override IEnumerable<string> VariablesUsedForPrediction {
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| 50 | get { return originalTrainingData.AllowedInputVariables; }
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| 51 | }
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| 52 |
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[15127] | 53 | public int NumberOfTrees {
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| 54 | get { return nTrees; }
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| 55 | }
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[14027] | 56 |
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[11006] | 57 | // instead of storing the data of the model itself
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| 58 | // we instead only store data necessary to recalculate the same model lazily on demand
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[6240] | 59 | [Storable]
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[11006] | 60 | private int seed;
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[6240] | 61 | [Storable]
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[11006] | 62 | private IDataAnalysisProblemData originalTrainingData;
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[6241] | 63 | [Storable]
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| 64 | private double[] classValues;
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[11006] | 65 | [Storable]
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| 66 | private int nTrees;
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| 67 | [Storable]
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| 68 | private double r;
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| 69 | [Storable]
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| 70 | private double m;
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| 71 |
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[6240] | 72 | [StorableConstructor]
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[6241] | 73 | private RandomForestModel(bool deserializing)
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[6240] | 74 | : base(deserializing) {
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[11006] | 75 | // for backwards compatibility (loading old solutions)
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| 76 | randomForest = new alglib.decisionforest();
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[6240] | 77 | }
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[6241] | 78 | private RandomForestModel(RandomForestModel original, Cloner cloner)
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[6240] | 79 | : base(original, cloner) {
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| 80 | randomForest = new alglib.decisionforest();
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| 81 | randomForest.innerobj.bufsize = original.randomForest.innerobj.bufsize;
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| 82 | randomForest.innerobj.nclasses = original.randomForest.innerobj.nclasses;
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| 83 | randomForest.innerobj.ntrees = original.randomForest.innerobj.ntrees;
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| 84 | randomForest.innerobj.nvars = original.randomForest.innerobj.nvars;
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[11006] | 85 | // we assume that the trees array (double[]) is immutable in alglib
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| 86 | randomForest.innerobj.trees = original.randomForest.innerobj.trees;
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[11901] | 87 |
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[11006] | 88 | // allowedInputVariables is immutable so we don't need to clone
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| 89 | allowedInputVariables = original.allowedInputVariables;
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| 90 |
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| 91 | // clone data which is necessary to rebuild the model
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| 92 | this.seed = original.seed;
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| 93 | this.originalTrainingData = cloner.Clone(original.originalTrainingData);
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| 94 | // classvalues is immutable so we don't need to clone
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| 95 | this.classValues = original.classValues;
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| 96 | this.nTrees = original.nTrees;
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| 97 | this.r = original.r;
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| 98 | this.m = original.m;
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[6240] | 99 | }
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[11006] | 100 |
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| 101 | // random forest models can only be created through the static factory methods CreateRegressionModel and CreateClassificationModel
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[14027] | 102 | private RandomForestModel(string targetVariable, alglib.decisionforest randomForest,
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[11006] | 103 | int seed, IDataAnalysisProblemData originalTrainingData,
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| 104 | int nTrees, double r, double m, double[] classValues = null)
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[14027] | 105 | : base(targetVariable) {
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[6240] | 106 | this.name = ItemName;
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| 107 | this.description = ItemDescription;
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[11006] | 108 | // the model itself
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[6240] | 109 | this.randomForest = randomForest;
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[11006] | 110 | // data which is necessary for recalculation of the model
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| 111 | this.seed = seed;
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| 112 | this.originalTrainingData = (IDataAnalysisProblemData)originalTrainingData.Clone();
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| 113 | this.classValues = classValues;
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| 114 | this.nTrees = nTrees;
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| 115 | this.r = r;
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| 116 | this.m = m;
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[6240] | 117 | }
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| 118 |
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| 119 | public override IDeepCloneable Clone(Cloner cloner) {
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[6241] | 120 | return new RandomForestModel(this, cloner);
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[6240] | 121 | }
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| 122 |
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[11006] | 123 | private void RecalculateModel() {
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| 124 | double rmsError, oobRmsError, relClassError, oobRelClassError;
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| 125 | var regressionProblemData = originalTrainingData as IRegressionProblemData;
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| 126 | var classificationProblemData = originalTrainingData as IClassificationProblemData;
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| 127 | if (regressionProblemData != null) {
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| 128 | var model = CreateRegressionModel(regressionProblemData,
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| 129 | nTrees, r, m, seed, out rmsError, out oobRmsError,
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| 130 | out relClassError, out oobRelClassError);
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| 131 | randomForest = model.randomForest;
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| 132 | } else if (classificationProblemData != null) {
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| 133 | var model = CreateClassificationModel(classificationProblemData,
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| 134 | nTrees, r, m, seed, out rmsError, out oobRmsError,
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| 135 | out relClassError, out oobRelClassError);
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| 136 | randomForest = model.randomForest;
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| 137 | }
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| 138 | }
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| 139 |
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[12702] | 140 | public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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[11006] | 141 | double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, AllowedInputVariables, rows);
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| 142 | AssertInputMatrix(inputData);
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[6240] | 143 |
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| 144 | int n = inputData.GetLength(0);
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| 145 | int columns = inputData.GetLength(1);
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| 146 | double[] x = new double[columns];
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| 147 | double[] y = new double[1];
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| 148 |
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| 149 | for (int row = 0; row < n; row++) {
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| 150 | for (int column = 0; column < columns; column++) {
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| 151 | x[column] = inputData[row, column];
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| 152 | }
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[11006] | 153 | alglib.dfprocess(RandomForest, x, ref y);
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[6240] | 154 | yield return y[0];
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| 155 | }
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| 156 | }
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| 157 |
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[14822] | 158 | public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
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| 159 | double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, AllowedInputVariables, rows);
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| 160 | AssertInputMatrix(inputData);
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| 161 |
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| 162 | int n = inputData.GetLength(0);
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| 163 | int columns = inputData.GetLength(1);
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| 164 | double[] x = new double[columns];
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| 165 | double[] ys = new double[this.RandomForest.innerobj.ntrees];
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| 166 |
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| 167 | for (int row = 0; row < n; row++) {
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| 168 | for (int column = 0; column < columns; column++) {
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| 169 | x[column] = inputData[row, column];
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| 170 | }
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| 171 | alglib.dforest.dfprocessraw(RandomForest.innerobj, x, ref ys);
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| 172 | yield return ys.VariancePop();
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| 173 | }
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| 174 | }
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| 175 |
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[14027] | 176 | public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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[11006] | 177 | double[,] inputData = AlglibUtil.PrepareInputMatrix(dataset, AllowedInputVariables, rows);
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| 178 | AssertInputMatrix(inputData);
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[6241] | 179 |
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| 180 | int n = inputData.GetLength(0);
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| 181 | int columns = inputData.GetLength(1);
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| 182 | double[] x = new double[columns];
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[11006] | 183 | double[] y = new double[RandomForest.innerobj.nclasses];
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[6241] | 184 |
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| 185 | for (int row = 0; row < n; row++) {
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| 186 | for (int column = 0; column < columns; column++) {
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| 187 | x[column] = inputData[row, column];
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| 188 | }
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| 189 | alglib.dfprocess(randomForest, x, ref y);
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| 190 | // find class for with the largest probability value
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| 191 | int maxProbClassIndex = 0;
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| 192 | double maxProb = y[0];
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| 193 | for (int i = 1; i < y.Length; i++) {
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| 194 | if (maxProb < y[i]) {
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| 195 | maxProb = y[i];
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| 196 | maxProbClassIndex = i;
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| 197 | }
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| 198 | }
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| 199 | yield return classValues[maxProbClassIndex];
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| 200 | }
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| 201 | }
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| 202 |
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[15127] | 203 | public ISymbolicExpressionTree ExtractTree(int treeIdx) {
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| 204 | var rf = RandomForest;
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| 205 | // hoping that the internal representation of alglib is stable
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[14027] | 206 |
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[15127] | 207 | // TREE FORMAT
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| 208 | // W[Offs] - size of sub-array (for the tree)
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| 209 | // node info:
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| 210 | // W[K+0] - variable number (-1 for leaf mode)
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| 211 | // W[K+1] - threshold (class/value for leaf node)
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| 212 | // W[K+2] - ">=" branch index (absent for leaf node)
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| 213 |
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| 214 | // skip irrelevant trees
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| 215 | int offset = 0;
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| 216 | for (int i = 0; i < treeIdx - 1; i++) {
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| 217 | offset = offset + (int)Math.Round(rf.innerobj.trees[offset]);
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| 218 | }
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| 219 |
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| 220 | var constSy = new Constant();
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| 221 | var varCondSy = new VariableCondition() { IgnoreSlope = true };
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| 222 |
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| 223 | var node = CreateRegressionTreeRec(rf.innerobj.trees, offset, offset + 1, constSy, varCondSy);
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| 224 |
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| 225 | var startNode = new StartSymbol().CreateTreeNode();
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| 226 | startNode.AddSubtree(node);
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| 227 | var root = new ProgramRootSymbol().CreateTreeNode();
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| 228 | root.AddSubtree(startNode);
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| 229 | return new SymbolicExpressionTree(root);
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| 230 | }
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| 231 |
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| 232 | private ISymbolicExpressionTreeNode CreateRegressionTreeRec(double[] trees, int offset, int k, Constant constSy, VariableCondition varCondSy) {
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| 233 |
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| 234 | // alglib source for evaluation of one tree (dfprocessinternal)
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| 235 | // offs = 0
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| 236 | //
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| 237 | // Set pointer to the root
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| 238 | //
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| 239 | // k = offs + 1;
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| 240 | //
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| 241 | // //
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| 242 | // // Navigate through the tree
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| 243 | // //
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| 244 | // while (true) {
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| 245 | // if ((double)(df.trees[k]) == (double)(-1)) {
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| 246 | // if (df.nclasses == 1) {
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| 247 | // y[0] = y[0] + df.trees[k + 1];
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| 248 | // } else {
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| 249 | // idx = (int)Math.Round(df.trees[k + 1]);
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| 250 | // y[idx] = y[idx] + 1;
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| 251 | // }
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| 252 | // break;
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| 253 | // }
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| 254 | // if ((double)(x[(int)Math.Round(df.trees[k])]) < (double)(df.trees[k + 1])) {
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| 255 | // k = k + innernodewidth;
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| 256 | // } else {
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| 257 | // k = offs + (int)Math.Round(df.trees[k + 2]);
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| 258 | // }
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| 259 | // }
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| 260 |
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| 261 | if ((double)(trees[k]) == (double)(-1)) {
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| 262 | var constNode = (ConstantTreeNode)constSy.CreateTreeNode();
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| 263 | constNode.Value = trees[k + 1];
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| 264 | return constNode;
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| 265 | } else {
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| 266 | var condNode = (VariableConditionTreeNode)varCondSy.CreateTreeNode();
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| 267 | condNode.VariableName = AllowedInputVariables[(int)Math.Round(trees[k])];
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| 268 | condNode.Threshold = trees[k + 1];
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| 269 | condNode.Slope = double.PositiveInfinity;
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| 270 |
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| 271 | var left = CreateRegressionTreeRec(trees, offset, k + 3, constSy, varCondSy);
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| 272 | var right = CreateRegressionTreeRec(trees, offset, offset + (int)Math.Round(trees[k + 2]), constSy, varCondSy);
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| 273 |
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| 274 | condNode.AddSubtree(left); // not 100% correct because interpreter uses: if(x <= thres) left() else right() and RF uses if(x < thres) left() else right() (see above)
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| 275 | condNode.AddSubtree(right);
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| 276 | return condNode;
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| 277 | }
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| 278 | }
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| 279 |
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| 280 |
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[14027] | 281 | public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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| 282 | return new RandomForestRegressionSolution(this, new RegressionProblemData(problemData));
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[6603] | 283 | }
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[14027] | 284 | public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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| 285 | return new RandomForestClassificationSolution(this, new ClassificationProblemData(problemData));
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[6603] | 286 | }
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| 287 |
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[11006] | 288 | public static RandomForestModel CreateRegressionModel(IRegressionProblemData problemData, int nTrees, double r, double m, int seed,
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[11901] | 289 | out double rmsError, out double outOfBagRmsError, out double avgRelError, out double outOfBagAvgRelError) {
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| 290 | return CreateRegressionModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed, out rmsError, out avgRelError, out outOfBagAvgRelError, out outOfBagRmsError);
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| 291 | }
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[11006] | 292 |
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[11901] | 293 | public static RandomForestModel CreateRegressionModel(IRegressionProblemData problemData, IEnumerable<int> trainingIndices, int nTrees, double r, double m, int seed,
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| 294 | out double rmsError, out double outOfBagRmsError, out double avgRelError, out double outOfBagAvgRelError) {
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[11006] | 295 | var variables = problemData.AllowedInputVariables.Concat(new string[] { problemData.TargetVariable });
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[11901] | 296 | double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(problemData.Dataset, variables, trainingIndices);
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[11006] | 297 |
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| 298 | alglib.dfreport rep;
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| 299 | var dForest = CreateRandomForestModel(seed, inputMatrix, nTrees, r, m, 1, out rep);
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| 300 |
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| 301 | rmsError = rep.rmserror;
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| 302 | avgRelError = rep.avgrelerror;
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| 303 | outOfBagAvgRelError = rep.oobavgrelerror;
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| 304 | outOfBagRmsError = rep.oobrmserror;
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| 305 |
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[14027] | 306 | return new RandomForestModel(problemData.TargetVariable, dForest, seed, problemData, nTrees, r, m);
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[6240] | 307 | }
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| 308 |
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[11006] | 309 | public static RandomForestModel CreateClassificationModel(IClassificationProblemData problemData, int nTrees, double r, double m, int seed,
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| 310 | out double rmsError, out double outOfBagRmsError, out double relClassificationError, out double outOfBagRelClassificationError) {
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[11901] | 311 | return CreateClassificationModel(problemData, problemData.TrainingIndices, nTrees, r, m, seed, out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
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| 312 | }
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[11006] | 313 |
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[11901] | 314 | public static RandomForestModel CreateClassificationModel(IClassificationProblemData problemData, IEnumerable<int> trainingIndices, int nTrees, double r, double m, int seed,
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| 315 | out double rmsError, out double outOfBagRmsError, out double relClassificationError, out double outOfBagRelClassificationError) {
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| 316 |
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[11006] | 317 | var variables = problemData.AllowedInputVariables.Concat(new string[] { problemData.TargetVariable });
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[11901] | 318 | double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(problemData.Dataset, variables, trainingIndices);
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[11006] | 319 |
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| 320 | var classValues = problemData.ClassValues.ToArray();
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| 321 | int nClasses = classValues.Length;
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| 322 |
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| 323 | // map original class values to values [0..nClasses-1]
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| 324 | var classIndices = new Dictionary<double, double>();
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| 325 | for (int i = 0; i < nClasses; i++) {
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| 326 | classIndices[classValues[i]] = i;
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| 327 | }
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| 328 |
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| 329 | int nRows = inputMatrix.GetLength(0);
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| 330 | int nColumns = inputMatrix.GetLength(1);
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| 331 | for (int row = 0; row < nRows; row++) {
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| 332 | inputMatrix[row, nColumns - 1] = classIndices[inputMatrix[row, nColumns - 1]];
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| 333 | }
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| 334 |
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| 335 | alglib.dfreport rep;
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| 336 | var dForest = CreateRandomForestModel(seed, inputMatrix, nTrees, r, m, nClasses, out rep);
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| 337 |
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| 338 | rmsError = rep.rmserror;
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| 339 | outOfBagRmsError = rep.oobrmserror;
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| 340 | relClassificationError = rep.relclserror;
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| 341 | outOfBagRelClassificationError = rep.oobrelclserror;
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| 342 |
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[14027] | 343 | return new RandomForestModel(problemData.TargetVariable, dForest, seed, problemData, nTrees, r, m, classValues);
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[11006] | 344 | }
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| 345 |
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| 346 | private static alglib.decisionforest CreateRandomForestModel(int seed, double[,] inputMatrix, int nTrees, double r, double m, int nClasses, out alglib.dfreport rep) {
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| 347 | AssertParameters(r, m);
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| 348 | AssertInputMatrix(inputMatrix);
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| 349 |
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| 350 | int info = 0;
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| 351 | alglib.math.rndobject = new System.Random(seed);
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| 352 | var dForest = new alglib.decisionforest();
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| 353 | rep = new alglib.dfreport();
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| 354 | int nRows = inputMatrix.GetLength(0);
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| 355 | int nColumns = inputMatrix.GetLength(1);
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| 356 | int sampleSize = Math.Max((int)Math.Round(r * nRows), 1);
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| 357 | int nFeatures = Math.Max((int)Math.Round(m * (nColumns - 1)), 1);
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| 358 |
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| 359 | alglib.dforest.dfbuildinternal(inputMatrix, nRows, nColumns - 1, nClasses, nTrees, sampleSize, nFeatures, alglib.dforest.dfusestrongsplits + alglib.dforest.dfuseevs, ref info, dForest.innerobj, rep.innerobj);
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| 360 | if (info != 1) throw new ArgumentException("Error in calculation of random forest model");
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| 361 | return dForest;
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| 362 | }
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| 363 |
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| 364 | private static void AssertParameters(double r, double m) {
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| 365 | if (r <= 0 || r > 1) throw new ArgumentException("The R parameter for random forest modeling must be between 0 and 1.");
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| 366 | if (m <= 0 || m > 1) throw new ArgumentException("The M parameter for random forest modeling must be between 0 and 1.");
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| 367 | }
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| 368 |
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| 369 | private static void AssertInputMatrix(double[,] inputMatrix) {
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[11901] | 370 | if (inputMatrix.Cast<double>().Any(x => Double.IsNaN(x) || Double.IsInfinity(x)))
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[11006] | 371 | throw new NotSupportedException("Random forest modeling does not support NaN or infinity values in the input dataset.");
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| 372 | }
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| 373 |
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| 374 | #region persistence for backwards compatibility
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| 375 | // when the originalTrainingData is null this means the model was loaded from an old file
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| 376 | // therefore, we cannot use the new persistence mechanism because the original data is not available anymore
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| 377 | // in such cases we still store the compete model
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| 378 | private bool IsCompatibilityLoaded { get { return originalTrainingData == null; } }
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| 379 |
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| 380 | private string[] allowedInputVariables;
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| 381 | [Storable(Name = "allowedInputVariables")]
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| 382 | private string[] AllowedInputVariables {
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| 383 | get {
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| 384 | if (IsCompatibilityLoaded) return allowedInputVariables;
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| 385 | else return originalTrainingData.AllowedInputVariables.ToArray();
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| 386 | }
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| 387 | set { allowedInputVariables = value; }
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| 388 | }
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[6240] | 389 | [Storable]
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| 390 | private int RandomForestBufSize {
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| 391 | get {
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[11006] | 392 | if (IsCompatibilityLoaded) return randomForest.innerobj.bufsize;
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| 393 | else return 0;
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[6240] | 394 | }
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| 395 | set {
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| 396 | randomForest.innerobj.bufsize = value;
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| 397 | }
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| 398 | }
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| 399 | [Storable]
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| 400 | private int RandomForestNClasses {
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| 401 | get {
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[11006] | 402 | if (IsCompatibilityLoaded) return randomForest.innerobj.nclasses;
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| 403 | else return 0;
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[6240] | 404 | }
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| 405 | set {
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| 406 | randomForest.innerobj.nclasses = value;
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| 407 | }
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| 408 | }
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| 409 | [Storable]
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| 410 | private int RandomForestNTrees {
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| 411 | get {
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[11006] | 412 | if (IsCompatibilityLoaded) return randomForest.innerobj.ntrees;
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| 413 | else return 0;
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[6240] | 414 | }
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| 415 | set {
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| 416 | randomForest.innerobj.ntrees = value;
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| 417 | }
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| 418 | }
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| 419 | [Storable]
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| 420 | private int RandomForestNVars {
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| 421 | get {
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[11006] | 422 | if (IsCompatibilityLoaded) return randomForest.innerobj.nvars;
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| 423 | else return 0;
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[6240] | 424 | }
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| 425 | set {
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| 426 | randomForest.innerobj.nvars = value;
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| 427 | }
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| 428 | }
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| 429 | [Storable]
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| 430 | private double[] RandomForestTrees {
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| 431 | get {
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[11006] | 432 | if (IsCompatibilityLoaded) return randomForest.innerobj.trees;
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| 433 | else return new double[] { };
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[6240] | 434 | }
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| 435 | set {
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| 436 | randomForest.innerobj.trees = value;
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| 437 | }
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| 438 | }
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| 439 | #endregion
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| 440 | }
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| 441 | }
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