[5617] | 1 | #region License Information |
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| 2 | /* HeuristicLab |
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[14185] | 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL) |
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[5617] | 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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[5777] | 23 | using System.Collections.Generic; |
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[5617] | 24 | using System.Linq; |
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[14542] | 25 | using System.Threading; |
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[5617] | 26 | using HeuristicLab.Common; |
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| 27 | using HeuristicLab.Core; |
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| 28 | using HeuristicLab.Data; |
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[5777] | 29 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; |
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[5617] | 30 | using HeuristicLab.Optimization; |
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; |
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| 32 | using HeuristicLab.Problems.DataAnalysis; |
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| 33 | using HeuristicLab.Problems.DataAnalysis.Symbolic; |
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[5624] | 34 | using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression; |
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[5617] | 35 | |
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| 36 | namespace HeuristicLab.Algorithms.DataAnalysis { |
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| 37 | /// <summary> |
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| 38 | /// Linear regression data analysis algorithm. |
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| 39 | /// </summary> |
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[13238] | 40 | [Item("Linear Regression (LR)", "Linear regression data analysis algorithm (wrapper for ALGLIB).")] |
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[12504] | 41 | [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 100)] |
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[5617] | 42 | [StorableClass] |
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| 43 | public sealed class LinearRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> { |
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[5649] | 44 | private const string LinearRegressionModelResultName = "Linear regression solution"; |
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[5617] | 45 | |
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| 46 | [StorableConstructor] |
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| 47 | private LinearRegression(bool deserializing) : base(deserializing) { } |
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| 48 | private LinearRegression(LinearRegression original, Cloner cloner) |
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| 49 | : base(original, cloner) { |
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| 50 | } |
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| 51 | public LinearRegression() |
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| 52 | : base() { |
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[5649] | 53 | Problem = new RegressionProblem(); |
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[5617] | 54 | } |
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| 55 | [StorableHook(HookType.AfterDeserialization)] |
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| 56 | private void AfterDeserialization() { } |
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| 57 | |
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| 58 | public override IDeepCloneable Clone(Cloner cloner) { |
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| 59 | return new LinearRegression(this, cloner); |
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| 60 | } |
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| 61 | |
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| 62 | #region linear regression |
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[14542] | 63 | protected override void Run(CancellationToken cancellationToken) { |
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[5617] | 64 | double rmsError, cvRmsError; |
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[5624] | 65 | var solution = CreateLinearRegressionSolution(Problem.ProblemData, out rmsError, out cvRmsError); |
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[5649] | 66 | Results.Add(new Result(LinearRegressionModelResultName, "The linear regression solution.", solution)); |
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| 67 | Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the linear regression solution on the training set.", new DoubleValue(rmsError))); |
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| 68 | Results.Add(new Result("Estimated root mean square error (cross-validation)", "The estimated root of the mean of squared errors of the linear regression solution via cross validation.", new DoubleValue(cvRmsError))); |
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[5617] | 69 | } |
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| 70 | |
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[5624] | 71 | public static ISymbolicRegressionSolution CreateLinearRegressionSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError) { |
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[12509] | 72 | var dataset = problemData.Dataset; |
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[5624] | 73 | string targetVariable = problemData.TargetVariable; |
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[5649] | 74 | IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables; |
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[8139] | 75 | IEnumerable<int> rows = problemData.TrainingIndices; |
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[14237] | 76 | var doubleVariables = allowedInputVariables.Where(dataset.VariableHasType<double>); |
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| 77 | var factorVariableNames = allowedInputVariables.Where(dataset.VariableHasType<string>); |
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[14240] | 78 | var factorVariables = AlglibUtil.GetFactorVariableValues(dataset, factorVariableNames, rows); |
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[14237] | 79 | double[,] binaryMatrix = AlglibUtil.PrepareInputMatrix(dataset, factorVariables, rows); |
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| 80 | double[,] doubleVarMatrix = AlglibUtil.PrepareInputMatrix(dataset, doubleVariables.Concat(new string[] { targetVariable }), rows); |
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| 81 | var inputMatrix = binaryMatrix.VertCat(doubleVarMatrix); |
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| 82 | |
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[6002] | 83 | if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x))) |
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| 84 | throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset."); |
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[5617] | 85 | |
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[12817] | 86 | alglib.linearmodel lm = new alglib.linearmodel(); |
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| 87 | alglib.lrreport ar = new alglib.lrreport(); |
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[5617] | 88 | int nRows = inputMatrix.GetLength(0); |
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| 89 | int nFeatures = inputMatrix.GetLength(1) - 1; |
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[12817] | 90 | double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant |
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[5617] | 91 | |
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| 92 | int retVal = 1; |
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| 93 | alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar); |
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[5649] | 94 | if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression solution"); |
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[5617] | 95 | rmsError = ar.rmserror; |
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| 96 | cvRmsError = ar.cvrmserror; |
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| 97 | |
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| 98 | alglib.lrunpack(lm, out coefficients, out nFeatures); |
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| 99 | |
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| 100 | ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode()); |
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| 101 | ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode(); |
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[5733] | 102 | tree.Root.AddSubtree(startNode); |
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[5617] | 103 | ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode(); |
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[5733] | 104 | startNode.AddSubtree(addition); |
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[5617] | 105 | |
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| 106 | int col = 0; |
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[14237] | 107 | foreach (var kvp in factorVariables) { |
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| 108 | var varName = kvp.Key; |
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| 109 | foreach (var cat in kvp.Value) { |
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[14243] | 110 | BinaryFactorVariableTreeNode vNode = |
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| 111 | (BinaryFactorVariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.BinaryFactorVariable().CreateTreeNode(); |
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[14237] | 112 | vNode.VariableName = varName; |
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| 113 | vNode.VariableValue = cat; |
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| 114 | vNode.Weight = coefficients[col]; |
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| 115 | addition.AddSubtree(vNode); |
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| 116 | col++; |
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| 117 | } |
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| 118 | } |
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| 119 | foreach (string column in doubleVariables) { |
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[5617] | 120 | VariableTreeNode vNode = (VariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode(); |
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| 121 | vNode.VariableName = column; |
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| 122 | vNode.Weight = coefficients[col]; |
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[5733] | 123 | addition.AddSubtree(vNode); |
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[5617] | 124 | col++; |
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| 125 | } |
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| 126 | |
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| 127 | ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode(); |
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| 128 | cNode.Value = coefficients[coefficients.Length - 1]; |
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[5733] | 129 | addition.AddSubtree(cNode); |
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[5617] | 130 | |
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[13941] | 131 | SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(problemData.TargetVariable, tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), (IRegressionProblemData)problemData.Clone()); |
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[6555] | 132 | solution.Model.Name = "Linear Regression Model"; |
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[7588] | 133 | solution.Name = "Linear Regression Solution"; |
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[5624] | 134 | return solution; |
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[5617] | 135 | } |
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| 136 | #endregion |
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| 137 | } |
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| 138 | } |
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