[11315] | 1 | #region License Information
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| 2 |
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| 3 | /* HeuristicLab
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[17097] | 4 | * Copyright (C) 2002-2019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[11315] | 5 | *
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| 6 | * This file is part of HeuristicLab.
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| 7 | *
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| 8 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 9 | * it under the terms of the GNU General Public License as published by
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| 10 | * the Free Software Foundation, either version 3 of the License, or
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| 11 | * (at your option) any later version.
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| 12 | *
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| 13 | * HeuristicLab is distributed in the hope that it will be useful,
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| 14 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 15 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 16 | * GNU General Public License for more details.
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| 17 | *
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| 18 | * You should have received a copy of the GNU General Public License
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| 19 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 20 | */
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| 21 |
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| 22 | #endregion
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| 23 |
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| 24 | using System;
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| 25 | using System.Collections.Generic;
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| 26 | using System.Linq;
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| 27 | using System.Linq.Expressions;
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| 28 | using System.Threading.Tasks;
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| 29 | using HeuristicLab.Common;
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[11901] | 30 | using HeuristicLab.Core;
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[11315] | 31 | using HeuristicLab.Data;
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[11901] | 32 | using HeuristicLab.Parameters;
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[17097] | 33 | using HEAL.Attic;
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[11315] | 34 | using HeuristicLab.Problems.DataAnalysis;
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[11901] | 35 | using HeuristicLab.Random;
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[11315] | 36 |
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| 37 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[11901] | 38 | [Item("RFParameter", "A random forest parameter collection")]
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[17097] | 39 | [StorableType("40E482DA-63C5-4D39-97C7-63701CF1D021")]
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[11901] | 40 | public class RFParameter : ParameterCollection {
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| 41 | public RFParameter() {
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| 42 | base.Add(new FixedValueParameter<IntValue>("N", "The number of random forest trees", new IntValue(50)));
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| 43 | base.Add(new FixedValueParameter<DoubleValue>("M", "The ratio of features that will be used in the construction of individual trees (0<m<=1)", new DoubleValue(0.1)));
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| 44 | base.Add(new FixedValueParameter<DoubleValue>("R", "The ratio of the training set that will be used in the construction of individual trees (0<r<=1)", new DoubleValue(0.1)));
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| 45 | }
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[11315] | 46 |
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[11901] | 47 | [StorableConstructor]
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[17097] | 48 | protected RFParameter(StorableConstructorFlag _) : base(_) {
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[11901] | 49 | }
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[11315] | 50 |
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[11901] | 51 | protected RFParameter(RFParameter original, Cloner cloner)
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| 52 | : base(original, cloner) {
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| 53 | this.N = original.N;
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| 54 | this.R = original.R;
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| 55 | this.M = original.M;
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| 56 | }
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[11315] | 57 |
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[11901] | 58 | public override IDeepCloneable Clone(Cloner cloner) {
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| 59 | return new RFParameter(this, cloner);
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[11315] | 60 | }
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| 61 |
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[11901] | 62 | private IFixedValueParameter<IntValue> NParameter {
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| 63 | get { return (IFixedValueParameter<IntValue>)base["N"]; }
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| 64 | }
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[11315] | 65 |
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[11901] | 66 | private IFixedValueParameter<DoubleValue> RParameter {
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| 67 | get { return (IFixedValueParameter<DoubleValue>)base["R"]; }
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| 68 | }
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[11315] | 69 |
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[11901] | 70 | private IFixedValueParameter<DoubleValue> MParameter {
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| 71 | get { return (IFixedValueParameter<DoubleValue>)base["M"]; }
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[11315] | 72 | }
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| 73 |
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[11901] | 74 | public int N {
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| 75 | get { return NParameter.Value.Value; }
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| 76 | set { NParameter.Value.Value = value; }
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[11315] | 77 | }
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| 78 |
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[11901] | 79 | public double R {
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| 80 | get { return RParameter.Value.Value; }
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| 81 | set { RParameter.Value.Value = value; }
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| 82 | }
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[11315] | 83 |
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[11901] | 84 | public double M {
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| 85 | get { return MParameter.Value.Value; }
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| 86 | set { MParameter.Value.Value = value; }
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| 87 | }
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| 88 | }
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[11315] | 89 |
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[11901] | 90 | public static class RandomForestUtil {
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| 91 | private static void CrossValidate(IRegressionProblemData problemData, Tuple<IEnumerable<int>, IEnumerable<int>>[] partitions, int nTrees, double r, double m, int seed, out double avgTestMse) {
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| 92 | avgTestMse = 0;
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| 93 | var ds = problemData.Dataset;
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| 94 | var targetVariable = GetTargetVariableName(problemData);
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| 95 | foreach (var tuple in partitions) {
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[11315] | 96 | double rmsError, avgRelError, outOfBagAvgRelError, outOfBagRmsError;
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[11901] | 97 | var trainingRandomForestPartition = tuple.Item1;
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| 98 | var testRandomForestPartition = tuple.Item2;
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| 99 | var model = RandomForestModel.CreateRegressionModel(problemData, trainingRandomForestPartition, nTrees, r, m, seed, out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
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| 100 | var estimatedValues = model.GetEstimatedValues(ds, testRandomForestPartition);
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| 101 | var targetValues = ds.GetDoubleValues(targetVariable, testRandomForestPartition);
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[11315] | 102 | OnlineCalculatorError calculatorError;
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[11901] | 103 | double mse = OnlineMeanSquaredErrorCalculator.Calculate(estimatedValues, targetValues, out calculatorError);
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[11315] | 104 | if (calculatorError != OnlineCalculatorError.None)
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| 105 | mse = double.NaN;
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[11901] | 106 | avgTestMse += mse;
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[11315] | 107 | }
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[11901] | 108 | avgTestMse /= partitions.Length;
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| 109 | }
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[11315] | 110 |
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[11901] | 111 | private static void CrossValidate(IClassificationProblemData problemData, Tuple<IEnumerable<int>, IEnumerable<int>>[] partitions, int nTrees, double r, double m, int seed, out double avgTestAccuracy) {
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| 112 | avgTestAccuracy = 0;
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| 113 | var ds = problemData.Dataset;
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| 114 | var targetVariable = GetTargetVariableName(problemData);
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| 115 | foreach (var tuple in partitions) {
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| 116 | double rmsError, avgRelError, outOfBagAvgRelError, outOfBagRmsError;
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| 117 | var trainingRandomForestPartition = tuple.Item1;
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| 118 | var testRandomForestPartition = tuple.Item2;
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| 119 | var model = RandomForestModel.CreateClassificationModel(problemData, trainingRandomForestPartition, nTrees, r, m, seed, out rmsError, out avgRelError, out outOfBagRmsError, out outOfBagAvgRelError);
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| 120 | var estimatedValues = model.GetEstimatedClassValues(ds, testRandomForestPartition);
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| 121 | var targetValues = ds.GetDoubleValues(targetVariable, testRandomForestPartition);
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| 122 | OnlineCalculatorError calculatorError;
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| 123 | double accuracy = OnlineAccuracyCalculator.Calculate(estimatedValues, targetValues, out calculatorError);
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| 124 | if (calculatorError != OnlineCalculatorError.None)
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| 125 | accuracy = double.NaN;
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| 126 | avgTestAccuracy += accuracy;
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| 127 | }
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| 128 | avgTestAccuracy /= partitions.Length;
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[11315] | 129 | }
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| 130 |
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[12702] | 131 | /// <summary>
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| 132 | /// Grid search without crossvalidation (since for random forests the out-of-bag estimate is unbiased)
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| 133 | /// </summary>
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| 134 | /// <param name="problemData">The regression problem data</param>
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| 135 | /// <param name="parameterRanges">The ranges for each parameter in the grid search</param>
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| 136 | /// <param name="seed">The random seed (required by the random forest model)</param>
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| 137 | /// <param name="maxDegreeOfParallelism">The maximum allowed number of threads (to parallelize the grid search)</param>
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[11901] | 138 | public static RFParameter GridSearch(IRegressionProblemData problemData, Dictionary<string, IEnumerable<double>> parameterRanges, int seed = 12345, int maxDegreeOfParallelism = 1) {
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| 139 | var setters = parameterRanges.Keys.Select(GenerateSetter).ToList();
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| 140 | var crossProduct = parameterRanges.Values.CartesianProduct();
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| 141 | double bestOutOfBagRmsError = double.MaxValue;
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| 142 | RFParameter bestParameters = new RFParameter();
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| 143 |
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[12702] | 144 | var locker = new object();
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[11901] | 145 | Parallel.ForEach(crossProduct, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism }, parameterCombination => {
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| 146 | var parameterValues = parameterCombination.ToList();
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| 147 | var parameters = new RFParameter();
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| 148 | for (int i = 0; i < setters.Count; ++i) { setters[i](parameters, parameterValues[i]); }
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| 149 | double rmsError, outOfBagRmsError, avgRelError, outOfBagAvgRelError;
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| 150 | RandomForestModel.CreateRegressionModel(problemData, problemData.TrainingIndices, parameters.N, parameters.R, parameters.M, seed, out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
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| 151 |
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| 152 | lock (locker) {
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| 153 | if (bestOutOfBagRmsError > outOfBagRmsError) {
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| 154 | bestOutOfBagRmsError = outOfBagRmsError;
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| 155 | bestParameters = (RFParameter)parameters.Clone();
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| 156 | }
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| 157 | }
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| 158 | });
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| 159 | return bestParameters;
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| 160 | }
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| 161 |
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[12702] | 162 | /// <summary>
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| 163 | /// Grid search without crossvalidation (since for random forests the out-of-bag estimate is unbiased)
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| 164 | /// </summary>
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| 165 | /// <param name="problemData">The classification problem data</param>
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| 166 | /// <param name="parameterRanges">The ranges for each parameter in the grid search</param>
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| 167 | /// <param name="seed">The random seed (required by the random forest model)</param>
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| 168 | /// <param name="maxDegreeOfParallelism">The maximum allowed number of threads (to parallelize the grid search)</param>
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[11901] | 169 | public static RFParameter GridSearch(IClassificationProblemData problemData, Dictionary<string, IEnumerable<double>> parameterRanges, int seed = 12345, int maxDegreeOfParallelism = 1) {
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| 170 | var setters = parameterRanges.Keys.Select(GenerateSetter).ToList();
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| 171 | var crossProduct = parameterRanges.Values.CartesianProduct();
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| 172 |
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| 173 | double bestOutOfBagRmsError = double.MaxValue;
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| 174 | RFParameter bestParameters = new RFParameter();
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| 175 |
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[12702] | 176 | var locker = new object();
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[11901] | 177 | Parallel.ForEach(crossProduct, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism }, parameterCombination => {
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| 178 | var parameterValues = parameterCombination.ToList();
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| 179 | var parameters = new RFParameter();
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| 180 | for (int i = 0; i < setters.Count; ++i) { setters[i](parameters, parameterValues[i]); }
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| 181 | double rmsError, outOfBagRmsError, avgRelError, outOfBagAvgRelError;
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| 182 | RandomForestModel.CreateClassificationModel(problemData, problemData.TrainingIndices, parameters.N, parameters.R, parameters.M, seed,
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| 183 | out rmsError, out outOfBagRmsError, out avgRelError, out outOfBagAvgRelError);
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| 184 |
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| 185 | lock (locker) {
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| 186 | if (bestOutOfBagRmsError > outOfBagRmsError) {
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| 187 | bestOutOfBagRmsError = outOfBagRmsError;
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| 188 | bestParameters = (RFParameter)parameters.Clone();
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| 189 | }
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| 190 | }
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| 191 | });
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| 192 | return bestParameters;
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| 193 | }
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| 194 |
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[12702] | 195 | /// <summary>
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| 196 | /// Grid search with crossvalidation
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| 197 | /// </summary>
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| 198 | /// <param name="problemData">The regression problem data</param>
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| 199 | /// <param name="numberOfFolds">The number of folds for crossvalidation</param>
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| 200 | /// <param name="shuffleFolds">Specifies whether the folds should be shuffled</param>
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| 201 | /// <param name="parameterRanges">The ranges for each parameter in the grid search</param>
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| 202 | /// <param name="seed">The random seed (required by the random forest model)</param>
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| 203 | /// <param name="maxDegreeOfParallelism">The maximum allowed number of threads (to parallelize the grid search)</param>
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| 204 | /// <returns>The best parameter values found by the grid search</returns>
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[11901] | 205 | public static RFParameter GridSearch(IRegressionProblemData problemData, int numberOfFolds, bool shuffleFolds, Dictionary<string, IEnumerable<double>> parameterRanges, int seed = 12345, int maxDegreeOfParallelism = 1) {
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[11315] | 206 | DoubleValue mse = new DoubleValue(Double.MaxValue);
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[11901] | 207 | RFParameter bestParameter = new RFParameter();
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[11315] | 208 |
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[11901] | 209 | var setters = parameterRanges.Keys.Select(GenerateSetter).ToList();
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| 210 | var partitions = GenerateRandomForestPartitions(problemData, numberOfFolds);
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| 211 | var crossProduct = parameterRanges.Values.CartesianProduct();
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[11315] | 212 |
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[12702] | 213 | var locker = new object();
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[11901] | 214 | Parallel.ForEach(crossProduct, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism }, parameterCombination => {
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| 215 | var parameterValues = parameterCombination.ToList();
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[11315] | 216 | double testMSE;
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| 217 | var parameters = new RFParameter();
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[11901] | 218 | for (int i = 0; i < setters.Count; ++i) {
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| 219 | setters[i](parameters, parameterValues[i]);
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[11315] | 220 | }
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[11901] | 221 | CrossValidate(problemData, partitions, parameters.N, parameters.R, parameters.M, seed, out testMSE);
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| 222 |
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| 223 | lock (locker) {
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| 224 | if (testMSE < mse.Value) {
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[11315] | 225 | mse.Value = testMSE;
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[11901] | 226 | bestParameter = (RFParameter)parameters.Clone();
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[11315] | 227 | }
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[11901] | 228 | }
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| 229 | });
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| 230 | return bestParameter;
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| 231 | }
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| 232 |
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[12702] | 233 | /// <summary>
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| 234 | /// Grid search with crossvalidation
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| 235 | /// </summary>
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| 236 | /// <param name="problemData">The classification problem data</param>
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| 237 | /// <param name="numberOfFolds">The number of folds for crossvalidation</param>
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| 238 | /// <param name="shuffleFolds">Specifies whether the folds should be shuffled</param>
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| 239 | /// <param name="parameterRanges">The ranges for each parameter in the grid search</param>
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| 240 | /// <param name="seed">The random seed (for shuffling)</param>
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| 241 | /// <param name="maxDegreeOfParallelism">The maximum allowed number of threads (to parallelize the grid search)</param>
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[11901] | 242 | public static RFParameter GridSearch(IClassificationProblemData problemData, int numberOfFolds, bool shuffleFolds, Dictionary<string, IEnumerable<double>> parameterRanges, int seed = 12345, int maxDegreeOfParallelism = 1) {
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| 243 | DoubleValue accuracy = new DoubleValue(0);
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| 244 | RFParameter bestParameter = new RFParameter();
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| 245 |
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| 246 | var setters = parameterRanges.Keys.Select(GenerateSetter).ToList();
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| 247 | var crossProduct = parameterRanges.Values.CartesianProduct();
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| 248 | var partitions = GenerateRandomForestPartitions(problemData, numberOfFolds, shuffleFolds);
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| 249 |
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[12702] | 250 | var locker = new object();
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[11901] | 251 | Parallel.ForEach(crossProduct, new ParallelOptions { MaxDegreeOfParallelism = maxDegreeOfParallelism }, parameterCombination => {
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| 252 | var parameterValues = parameterCombination.ToList();
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| 253 | double testAccuracy;
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| 254 | var parameters = new RFParameter();
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| 255 | for (int i = 0; i < setters.Count; ++i) {
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| 256 | setters[i](parameters, parameterValues[i]);
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| 257 | }
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| 258 | CrossValidate(problemData, partitions, parameters.N, parameters.R, parameters.M, seed, out testAccuracy);
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| 259 |
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| 260 | lock (locker) {
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| 261 | if (testAccuracy > accuracy.Value) {
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| 262 | accuracy.Value = testAccuracy;
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[11315] | 263 | bestParameter = (RFParameter)parameters.Clone();
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| 264 | }
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| 265 | }
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| 266 | });
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| 267 | return bestParameter;
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| 268 | }
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[11901] | 269 |
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| 270 | private static Tuple<IEnumerable<int>, IEnumerable<int>>[] GenerateRandomForestPartitions(IDataAnalysisProblemData problemData, int numberOfFolds, bool shuffleFolds = false) {
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| 271 | var folds = GenerateFolds(problemData, numberOfFolds, shuffleFolds).ToList();
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| 272 | var partitions = new Tuple<IEnumerable<int>, IEnumerable<int>>[numberOfFolds];
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| 273 |
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| 274 | for (int i = 0; i < numberOfFolds; ++i) {
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| 275 | int p = i; // avoid "access to modified closure" warning
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| 276 | var trainingRows = folds.SelectMany((par, j) => j != p ? par : Enumerable.Empty<int>());
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| 277 | var testRows = folds[i];
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| 278 | partitions[i] = new Tuple<IEnumerable<int>, IEnumerable<int>>(trainingRows, testRows);
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| 279 | }
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| 280 | return partitions;
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| 281 | }
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| 282 |
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| 283 | public static IEnumerable<IEnumerable<int>> GenerateFolds(IDataAnalysisProblemData problemData, int numberOfFolds, bool shuffleFolds = false) {
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| 284 | var random = new MersenneTwister((uint)Environment.TickCount);
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| 285 | if (problemData is IRegressionProblemData) {
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| 286 | var trainingIndices = shuffleFolds ? problemData.TrainingIndices.OrderBy(x => random.Next()) : problemData.TrainingIndices;
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| 287 | return GenerateFolds(trainingIndices, problemData.TrainingPartition.Size, numberOfFolds);
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| 288 | }
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| 289 | if (problemData is IClassificationProblemData) {
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| 290 | // when shuffle is enabled do stratified folds generation, some folds may have zero elements
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| 291 | // otherwise, generate folds normally
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| 292 | return shuffleFolds ? GenerateFoldsStratified(problemData as IClassificationProblemData, numberOfFolds, random) : GenerateFolds(problemData.TrainingIndices, problemData.TrainingPartition.Size, numberOfFolds);
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| 293 | }
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| 294 | throw new ArgumentException("Problem data is neither regression or classification problem data.");
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| 295 | }
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| 296 |
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| 297 | /// <summary>
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| 298 | /// Stratified fold generation from classification data. Stratification means that we ensure the same distribution of class labels for each fold.
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| 299 | /// The samples are grouped by class label and each group is split into @numberOfFolds parts. The final folds are formed from the joining of
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| 300 | /// the corresponding parts from each class label.
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| 301 | /// </summary>
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| 302 | /// <param name="problemData">The classification problem data.</param>
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| 303 | /// <param name="numberOfFolds">The number of folds in which to split the data.</param>
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| 304 | /// <param name="random">The random generator used to shuffle the folds.</param>
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| 305 | /// <returns>An enumerable sequece of folds, where a fold is represented by a sequence of row indices.</returns>
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| 306 | private static IEnumerable<IEnumerable<int>> GenerateFoldsStratified(IClassificationProblemData problemData, int numberOfFolds, IRandom random) {
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| 307 | var values = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices);
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| 308 | var valuesIndices = problemData.TrainingIndices.Zip(values, (i, v) => new { Index = i, Value = v }).ToList();
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| 309 | IEnumerable<IEnumerable<IEnumerable<int>>> foldsByClass = valuesIndices.GroupBy(x => x.Value, x => x.Index).Select(g => GenerateFolds(g, g.Count(), numberOfFolds));
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| 310 | var enumerators = foldsByClass.Select(f => f.GetEnumerator()).ToList();
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| 311 | while (enumerators.All(e => e.MoveNext())) {
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| 312 | yield return enumerators.SelectMany(e => e.Current).OrderBy(x => random.Next()).ToList();
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| 313 | }
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| 314 | }
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| 315 |
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| 316 | private static IEnumerable<IEnumerable<T>> GenerateFolds<T>(IEnumerable<T> values, int valuesCount, int numberOfFolds) {
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| 317 | // if number of folds is greater than the number of values, some empty folds will be returned
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| 318 | if (valuesCount < numberOfFolds) {
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| 319 | for (int i = 0; i < numberOfFolds; ++i)
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| 320 | yield return i < valuesCount ? values.Skip(i).Take(1) : Enumerable.Empty<T>();
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| 321 | } else {
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| 322 | int f = valuesCount / numberOfFolds, r = valuesCount % numberOfFolds; // number of folds rounded to integer and remainder
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| 323 | int start = 0, end = f;
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| 324 | for (int i = 0; i < numberOfFolds; ++i) {
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| 325 | if (r > 0) {
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| 326 | ++end;
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| 327 | --r;
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| 328 | }
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| 329 | yield return values.Skip(start).Take(end - start);
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| 330 | start = end;
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| 331 | end += f;
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| 332 | }
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| 333 | }
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| 334 | }
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| 335 |
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| 336 | private static Action<RFParameter, double> GenerateSetter(string field) {
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| 337 | var targetExp = Expression.Parameter(typeof(RFParameter));
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| 338 | var valueExp = Expression.Parameter(typeof(double));
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| 339 | var fieldExp = Expression.Property(targetExp, field);
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| 340 | var assignExp = Expression.Assign(fieldExp, Expression.Convert(valueExp, fieldExp.Type));
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| 341 | var setter = Expression.Lambda<Action<RFParameter, double>>(assignExp, targetExp, valueExp).Compile();
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| 342 | return setter;
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| 343 | }
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| 344 |
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| 345 | private static string GetTargetVariableName(IDataAnalysisProblemData problemData) {
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| 346 | var regressionProblemData = problemData as IRegressionProblemData;
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| 347 | var classificationProblemData = problemData as IClassificationProblemData;
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| 348 |
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| 349 | if (regressionProblemData != null)
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| 350 | return regressionProblemData.TargetVariable;
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| 351 | if (classificationProblemData != null)
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| 352 | return classificationProblemData.TargetVariable;
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| 353 |
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| 354 | throw new ArgumentException("Problem data is neither regression or classification problem data.");
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| 355 | }
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[11315] | 356 | }
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| 357 | }
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