1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System;


23  using System.Linq;


24  using HeuristicLab.Common;


25  using HeuristicLab.Core;


26  using HeuristicLab.Data;


27  using HeuristicLab.Optimization;


28  using HeuristicLab.Parameters;


29  using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;


30  using HeuristicLab.Problems.DataAnalysis;


31  using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;


32  using System.Collections.Generic;


33  using HeuristicLab.Problems.DataAnalysis.Symbolic;


34  using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;


35 


36  namespace HeuristicLab.Algorithms.DataAnalysis {


37  /// <summary>


38  /// Linear regression data analysis algorithm.


39  /// </summary>


40  [Item("Linear Regression", "Linear regression data analysis algorithm.")]


41  [Creatable("Data Analysis")]


42  [StorableClass]


43  public sealed class LinearRegression : FixedDataAnalysisAlgorithm<IRegressionProblem> {


44  private const string LinearRegressionModelResultName = "Linear regression solution";


45 


46  [StorableConstructor]


47  private LinearRegression(bool deserializing) : base(deserializing) { }


48  private LinearRegression(LinearRegression original, Cloner cloner)


49  : base(original, cloner) {


50  }


51  public LinearRegression()


52  : base() {


53  Problem = new RegressionProblem();


54  }


55  [StorableHook(HookType.AfterDeserialization)]


56  private void AfterDeserialization() { }


57 


58  public override IDeepCloneable Clone(Cloner cloner) {


59  return new LinearRegression(this, cloner);


60  }


61 


62  #region linear regression


63  protected override void Run() {


64  double rmsError, cvRmsError;


65  var solution = CreateLinearRegressionSolution(Problem.ProblemData, out rmsError, out cvRmsError);


66  Results.Add(new Result(LinearRegressionModelResultName, "The linear regression solution.", solution));


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)));


68  Results.Add(new Result("Estimated root mean square error (crossvalidation)", "The estimated root of the mean of squared errors of the linear regression solution via cross validation.", new DoubleValue(cvRmsError)));


69  }


70 


71  public static ISymbolicRegressionSolution CreateLinearRegressionSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError) {


72  Dataset dataset = problemData.Dataset;


73  string targetVariable = problemData.TargetVariable;


74  IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;


75  int samplesStart = problemData.TrainingPartitionStart.Value;


76  int samplesEnd = problemData.TrainingPartitionEnd.Value;


77  IEnumerable<int> rows = Enumerable.Range(samplesStart, samplesEnd  samplesStart);


78  double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);


79 


80  alglib.linearmodel lm = new alglib.linearmodel();


81  alglib.lrreport ar = new alglib.lrreport();


82  int nRows = inputMatrix.GetLength(0);


83  int nFeatures = inputMatrix.GetLength(1)  1;


84  double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant


85 


86  int retVal = 1;


87  alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);


88  if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression solution");


89  rmsError = ar.rmserror;


90  cvRmsError = ar.cvrmserror;


91 


92  alglib.lrunpack(lm, out coefficients, out nFeatures);


93 


94  ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());


95  ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();


96  tree.Root.AddSubTree(startNode);


97  ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();


98  startNode.AddSubTree(addition);


99 


100  int col = 0;


101  foreach (string column in allowedInputVariables) {


102  VariableTreeNode vNode = (VariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();


103  vNode.VariableName = column;


104  vNode.Weight = coefficients[col];


105  addition.AddSubTree(vNode);


106  col++;


107  }


108 


109  ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode();


110  cNode.Value = coefficients[coefficients.Length  1];


111  addition.AddSubTree(cNode);


112 


113  SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), problemData);


114  return solution;


115  }


116  #endregion


117  }


118  }

