[17154] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17180] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[17154] | 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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[17931] | 22 | extern alias alglib_3_7;
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[17154] | 23 | using System;
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| 24 | using System.Collections.Generic;
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| 25 | using System.Linq;
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| 26 | using HEAL.Attic;
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| 27 | using HeuristicLab.Common;
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| 28 | using HeuristicLab.Core;
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| 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 30 | using HeuristicLab.Problems.DataAnalysis;
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| 31 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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| 32 |
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| 33 | namespace HeuristicLab.Algorithms.DataAnalysis {
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[17931] | 34 |
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[17154] | 35 | [StorableType("9C797DF0-1169-4381-A732-6DAB90802839")]
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[17931] | 36 | [Item("RandomForestModel (alglib 3.7)", "Represents a random forest for regression and classification.")]
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| 37 | [Obsolete("This version uses alglib version 3.7. Use RandomForestModelFull instead.")]
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| 38 | public sealed class RandomForestModelAlglib_3_7 : ClassificationModel, IRandomForestModel {
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[17154] | 39 |
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| 40 | public override IEnumerable<string> VariablesUsedForPrediction {
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| 41 | get { return inputVariables; }
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| 42 | }
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| 43 |
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| 44 | [Storable]
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| 45 | private double[] classValues;
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| 46 |
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| 47 | [Storable]
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| 48 | private string[] inputVariables;
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| 49 |
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| 50 | public int NumberOfTrees {
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| 51 | get { return RandomForestNTrees; }
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| 52 | }
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| 53 |
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| 54 | // not persisted
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[17931] | 55 | private alglib_3_7.alglib.decisionforest randomForest;
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[17154] | 56 |
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| 57 | [Storable]
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| 58 | private int RandomForestBufSize {
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| 59 | get { return randomForest.innerobj.bufsize; }
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| 60 | set { randomForest.innerobj.bufsize = value; }
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| 61 | }
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| 62 | [Storable]
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| 63 | private int RandomForestNClasses {
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| 64 | get { return randomForest.innerobj.nclasses; }
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| 65 | set { randomForest.innerobj.nclasses = value; }
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| 66 | }
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| 67 | [Storable]
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| 68 | private int RandomForestNTrees {
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| 69 | get { return randomForest.innerobj.ntrees; }
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| 70 | set { randomForest.innerobj.ntrees = value; }
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| 71 | }
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| 72 | [Storable]
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| 73 | private int RandomForestNVars {
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| 74 | get { return randomForest.innerobj.nvars; }
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| 75 | set { randomForest.innerobj.nvars = value; }
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| 76 | }
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| 77 | [Storable]
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| 78 | private double[] RandomForestTrees {
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| 79 | get { return randomForest.innerobj.trees; }
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| 80 | set { randomForest.innerobj.trees = value; }
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| 81 | }
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| 82 |
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| 83 | [StorableConstructor]
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[17931] | 84 | private RandomForestModelAlglib_3_7(StorableConstructorFlag _) : base(_) {
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| 85 | randomForest = new alglib_3_7.alglib.decisionforest();
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[17154] | 86 | }
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| 87 |
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[17931] | 88 | private RandomForestModelAlglib_3_7(RandomForestModelAlglib_3_7 original, Cloner cloner) : base(original, cloner) {
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| 89 | randomForest = new alglib_3_7.alglib.decisionforest();
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[17154] | 90 | randomForest.innerobj.bufsize = original.randomForest.innerobj.bufsize;
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| 91 | randomForest.innerobj.nclasses = original.randomForest.innerobj.nclasses;
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| 92 | randomForest.innerobj.ntrees = original.randomForest.innerobj.ntrees;
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| 93 | randomForest.innerobj.nvars = original.randomForest.innerobj.nvars;
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| 94 | randomForest.innerobj.trees = (double[])original.randomForest.innerobj.trees.Clone();
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| 95 |
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| 96 | // following fields are immutable so we don't need to clone them
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| 97 | inputVariables = original.inputVariables;
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| 98 | classValues = original.classValues;
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| 99 | }
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| 100 | public override IDeepCloneable Clone(Cloner cloner) {
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[17931] | 101 | return new RandomForestModelAlglib_3_7(this, cloner);
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[17154] | 102 | }
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| 103 |
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[17931] | 104 | public RandomForestModelAlglib_3_7(alglib_3_7.alglib.decisionforest decisionForest, string targetVariable, IEnumerable<string> inputVariables, IEnumerable<double> classValues = null) : base(targetVariable) {
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[17154] | 105 | this.name = ItemName;
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| 106 | this.description = ItemDescription;
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| 107 |
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| 108 | randomForest = decisionForest;
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| 109 |
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| 110 | this.inputVariables = inputVariables.ToArray();
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| 111 |
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| 112 | //classValues are only use for classification models
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| 113 | if (classValues == null) this.classValues = new double[0];
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| 114 | else this.classValues = classValues.ToArray();
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| 115 | }
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| 116 |
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| 117 |
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| 118 | public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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| 119 | return new RandomForestRegressionSolution(this, new RegressionProblemData(problemData));
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| 120 | }
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| 121 | public override IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
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| 122 | return new RandomForestClassificationSolution(this, new ClassificationProblemData(problemData));
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| 123 | }
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| 124 |
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| 125 | public bool IsProblemDataCompatible(IRegressionProblemData problemData, out string errorMessage) {
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| 126 | return RegressionModel.IsProblemDataCompatible(this, problemData, out errorMessage);
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| 127 | }
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| 128 |
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| 129 | public override bool IsProblemDataCompatible(IDataAnalysisProblemData problemData, out string errorMessage) {
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| 130 | if (problemData == null) throw new ArgumentNullException("problemData", "The provided problemData is null.");
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| 131 |
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| 132 | var regressionProblemData = problemData as IRegressionProblemData;
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| 133 | if (regressionProblemData != null)
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| 134 | return IsProblemDataCompatible(regressionProblemData, out errorMessage);
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| 135 |
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| 136 | var classificationProblemData = problemData as IClassificationProblemData;
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| 137 | if (classificationProblemData != null)
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| 138 | return IsProblemDataCompatible(classificationProblemData, out errorMessage);
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| 139 |
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| 140 | throw new ArgumentException("The problem data is not compatible with this random forest. Instead a " + problemData.GetType().GetPrettyName() + " was provided.", "problemData");
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| 141 | }
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| 142 |
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| 143 | public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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| 144 | double[,] inputData = dataset.ToArray(inputVariables, rows);
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| 145 | RandomForestUtil.AssertInputMatrix(inputData);
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| 146 |
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| 147 | int n = inputData.GetLength(0);
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| 148 | int columns = inputData.GetLength(1);
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| 149 | double[] x = new double[columns];
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| 150 | double[] y = new double[1];
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| 151 |
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| 152 | for (int row = 0; row < n; row++) {
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| 153 | for (int column = 0; column < columns; column++) {
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| 154 | x[column] = inputData[row, column];
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| 155 | }
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[17931] | 156 | alglib_3_7.alglib.dfprocess(randomForest, x, ref y);
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[17154] | 157 | yield return y[0];
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| 158 | }
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| 159 | }
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| 160 | public IEnumerable<double> GetEstimatedVariances(IDataset dataset, IEnumerable<int> rows) {
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| 161 | double[,] inputData = dataset.ToArray(inputVariables, rows);
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| 162 | RandomForestUtil.AssertInputMatrix(inputData);
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| 163 |
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| 164 | int n = inputData.GetLength(0);
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| 165 | int columns = inputData.GetLength(1);
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| 166 | double[] x = new double[columns];
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| 167 | double[] ys = new double[this.randomForest.innerobj.ntrees];
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| 168 |
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| 169 | for (int row = 0; row < n; row++) {
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| 170 | for (int column = 0; column < columns; column++) {
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| 171 | x[column] = inputData[row, column];
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| 172 | }
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[17931] | 173 | alglib_3_7.alglib.dforest.dfprocessraw(randomForest.innerobj, x, ref ys);
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[17154] | 174 | yield return ys.VariancePop();
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| 175 | }
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| 176 | }
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| 177 |
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| 178 | public override IEnumerable<double> GetEstimatedClassValues(IDataset dataset, IEnumerable<int> rows) {
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| 179 | double[,] inputData = dataset.ToArray(inputVariables, rows);
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| 180 | RandomForestUtil.AssertInputMatrix(inputData);
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| 181 |
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| 182 | int n = inputData.GetLength(0);
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| 183 | int columns = inputData.GetLength(1);
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| 184 | double[] x = new double[columns];
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| 185 | double[] y = new double[randomForest.innerobj.nclasses];
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| 186 |
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| 187 | for (int row = 0; row < n; row++) {
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| 188 | for (int column = 0; column < columns; column++) {
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| 189 | x[column] = inputData[row, column];
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| 190 | }
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[17931] | 191 | alglib_3_7::alglib.dfprocess(randomForest, x, ref y);
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[17154] | 192 | // find class for with the largest probability value
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| 193 | int maxProbClassIndex = 0;
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| 194 | double maxProb = y[0];
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| 195 | for (int i = 1; i < y.Length; i++) {
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| 196 | if (maxProb < y[i]) {
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| 197 | maxProb = y[i];
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| 198 | maxProbClassIndex = i;
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| 199 | }
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| 200 | }
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| 201 | yield return classValues[maxProbClassIndex];
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| 202 | }
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| 203 | }
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| 204 |
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| 205 | public ISymbolicExpressionTree ExtractTree(int treeIdx) {
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| 206 | var rf = randomForest;
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| 207 | // hoping that the internal representation of alglib is stable
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| 208 |
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| 209 | // TREE FORMAT
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| 210 | // W[Offs] - size of sub-array (for the tree)
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| 211 | // node info:
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| 212 | // W[K+0] - variable number (-1 for leaf mode)
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| 213 | // W[K+1] - threshold (class/value for leaf node)
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| 214 | // W[K+2] - ">=" branch index (absent for leaf node)
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| 215 |
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| 216 | // skip irrelevant trees
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| 217 | int offset = 0;
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| 218 | for (int i = 0; i < treeIdx - 1; i++) {
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| 219 | offset = offset + (int)Math.Round(rf.innerobj.trees[offset]);
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| 220 | }
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| 221 |
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[18132] | 222 | var numSy = new Number();
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[17154] | 223 | var varCondSy = new VariableCondition() { IgnoreSlope = true };
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| 224 |
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[18132] | 225 | var node = CreateRegressionTreeRec(rf.innerobj.trees, offset, offset + 1, numSy, varCondSy);
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[17154] | 226 |
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| 227 | var startNode = new StartSymbol().CreateTreeNode();
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| 228 | startNode.AddSubtree(node);
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| 229 | var root = new ProgramRootSymbol().CreateTreeNode();
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| 230 | root.AddSubtree(startNode);
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| 231 | return new SymbolicExpressionTree(root);
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| 232 | }
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| 233 |
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[18132] | 234 | private ISymbolicExpressionTreeNode CreateRegressionTreeRec(double[] trees, int offset, int k, Number numSy, VariableCondition varCondSy) {
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[17154] | 235 |
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| 236 | // alglib source for evaluation of one tree (dfprocessinternal)
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| 237 | // offs = 0
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| 238 | //
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| 239 | // Set pointer to the root
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| 240 | //
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| 241 | // k = offs + 1;
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| 242 | //
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| 243 | // //
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| 244 | // // Navigate through the tree
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| 245 | // //
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| 246 | // while (true) {
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| 247 | // if ((double)(df.trees[k]) == (double)(-1)) {
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| 248 | // if (df.nclasses == 1) {
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| 249 | // y[0] = y[0] + df.trees[k + 1];
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| 250 | // } else {
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| 251 | // idx = (int)Math.Round(df.trees[k + 1]);
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| 252 | // y[idx] = y[idx] + 1;
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| 253 | // }
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| 254 | // break;
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| 255 | // }
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| 256 | // if ((double)(x[(int)Math.Round(df.trees[k])]) < (double)(df.trees[k + 1])) {
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| 257 | // k = k + innernodewidth;
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| 258 | // } else {
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| 259 | // k = offs + (int)Math.Round(df.trees[k + 2]);
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| 260 | // }
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| 261 | // }
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| 262 |
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[18132] | 263 | if (trees[k] == -1) {
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| 264 | var numNode = (NumberTreeNode)numSy.CreateTreeNode();
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| 265 | numNode.Value = trees[k + 1];
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| 266 | return numNode;
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[17154] | 267 | } else {
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| 268 | var condNode = (VariableConditionTreeNode)varCondSy.CreateTreeNode();
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| 269 | condNode.VariableName = inputVariables[(int)Math.Round(trees[k])];
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| 270 | condNode.Threshold = trees[k + 1];
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| 271 | condNode.Slope = double.PositiveInfinity;
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| 272 |
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[18132] | 273 | var left = CreateRegressionTreeRec(trees, offset, k + 3, numSy, varCondSy);
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| 274 | var right = CreateRegressionTreeRec(trees, offset, offset + (int)Math.Round(trees[k + 2]), numSy, varCondSy);
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[17154] | 275 |
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| 276 | 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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| 277 | condNode.AddSubtree(right);
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| 278 | return condNode;
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| 279 | }
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| 280 | }
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| 281 | }
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| 282 | } |
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