#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Linear regression data analysis algorithm.
///
[Item("Linear Regression", "Linear regression data analysis algorithm (wrapper for ALGLIB).")]
[Creatable("Data Analysis")]
[StorableClass]
public sealed class LinearRegression : FixedDataAnalysisAlgorithm {
private const string LinearRegressionModelResultName = "Linear regression solution";
[StorableConstructor]
private LinearRegression(bool deserializing) : base(deserializing) { }
private LinearRegression(LinearRegression original, Cloner cloner)
: base(original, cloner) {
}
public LinearRegression()
: base() {
Problem = new RegressionProblem();
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() { }
public override IDeepCloneable Clone(Cloner cloner) {
return new LinearRegression(this, cloner);
}
#region linear regression
protected override void Run() {
double rmsError, cvRmsError;
var solution = CreateLinearRegressionSolution(Problem.ProblemData, out rmsError, out cvRmsError);
Results.Add(new Result(LinearRegressionModelResultName, "The linear regression solution.", solution));
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)));
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)));
}
public static ISymbolicRegressionSolution CreateLinearRegressionSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError) {
Dataset dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
IEnumerable allowedInputVariables = problemData.AllowedInputVariables;
IEnumerable rows = problemData.TrainingIndices;
double[,] inputMatrix = AlglibUtil.PrepareInputMatrix(dataset, allowedInputVariables.Concat(new string[] { targetVariable }), rows);
if (inputMatrix.Cast().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
alglib.linearmodel lm = new alglib.linearmodel();
alglib.lrreport ar = new alglib.lrreport();
int nRows = inputMatrix.GetLength(0);
int nFeatures = inputMatrix.GetLength(1) - 1;
double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant
int retVal = 1;
alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression solution");
rmsError = ar.rmserror;
cvRmsError = ar.cvrmserror;
alglib.lrunpack(lm, out coefficients, out nFeatures);
ISymbolicExpressionTree tree = new SymbolicExpressionTree(new ProgramRootSymbol().CreateTreeNode());
ISymbolicExpressionTreeNode startNode = new StartSymbol().CreateTreeNode();
tree.Root.AddSubtree(startNode);
ISymbolicExpressionTreeNode addition = new Addition().CreateTreeNode();
startNode.AddSubtree(addition);
int col = 0;
foreach (string column in allowedInputVariables) {
VariableTreeNode vNode = (VariableTreeNode)new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
vNode.VariableName = column;
vNode.Weight = coefficients[col];
addition.AddSubtree(vNode);
col++;
}
ConstantTreeNode cNode = (ConstantTreeNode)new Constant().CreateTreeNode();
cNode.Value = coefficients[coefficients.Length - 1];
addition.AddSubtree(cNode);
SymbolicRegressionSolution solution = new SymbolicRegressionSolution(new SymbolicRegressionModel(tree, new SymbolicDataAnalysisExpressionTreeInterpreter()), (IRegressionProblemData)problemData.Clone());
solution.Model.Name = "Linear Regression Model";
solution.Name = "Linear Regression Solution";
return solution;
}
#endregion
}
}