#region License Information
/* HeuristicLab
* Copyright (C) 2002-2016 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.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
using HeuristicLab.Problems.DataAnalysis.Symbolic;
using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
using HeuristicLab.Random;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Nonlinear regression data analysis algorithm.
///
[Item("Nonlinear Regression (NLR)", "Nonlinear regression (curve fitting) data analysis algorithm (wrapper for ALGLIB).")]
[Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 120)]
[StorableClass]
public sealed class NonlinearRegression : FixedDataAnalysisAlgorithm {
private const string RegressionSolutionResultName = "Regression solution";
private const string ModelStructureParameterName = "Model structure";
private const string IterationsParameterName = "Iterations";
private const string RestartsParameterName = "Restarts";
private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
private const string SeedParameterName = "Seed";
public IFixedValueParameter ModelStructureParameter {
get { return (IFixedValueParameter)Parameters[ModelStructureParameterName]; }
}
public IFixedValueParameter IterationsParameter {
get { return (IFixedValueParameter)Parameters[IterationsParameterName]; }
}
public IFixedValueParameter SetSeedRandomlyParameter {
get { return (IFixedValueParameter)Parameters[SetSeedRandomlyParameterName]; }
}
public IFixedValueParameter SeedParameter {
get { return (IFixedValueParameter)Parameters[SeedParameterName]; }
}
public IFixedValueParameter RestartsParameter {
get { return (IFixedValueParameter)Parameters[RestartsParameterName]; }
}
public string ModelStructure {
get { return ModelStructureParameter.Value.Value; }
set { ModelStructureParameter.Value.Value = value; }
}
public int Iterations {
get { return IterationsParameter.Value.Value; }
set { IterationsParameter.Value.Value = value; }
}
public int Restarts {
get { return RestartsParameter.Value.Value; }
set { RestartsParameter.Value.Value = value; }
}
public int Seed {
get { return SeedParameter.Value.Value; }
set { SeedParameter.Value.Value = value; }
}
public bool SetSeedRandomly {
get { return SetSeedRandomlyParameter.Value.Value; }
set { SetSeedRandomlyParameter.Value.Value = value; }
}
[StorableConstructor]
private NonlinearRegression(bool deserializing) : base(deserializing) { }
private NonlinearRegression(NonlinearRegression original, Cloner cloner)
: base(original, cloner) {
}
public NonlinearRegression()
: base() {
Problem = new RegressionProblem();
Parameters.Add(new FixedValueParameter(ModelStructureParameterName, "The function for which the parameters must be fit (only numeric constants are tuned).", new StringValue("1.0 * x*x + 0.0")));
Parameters.Add(new FixedValueParameter(IterationsParameterName, "The maximum number of iterations for constants optimization.", new IntValue(200)));
Parameters.Add(new FixedValueParameter(RestartsParameterName, "The number of independent random restarts", new IntValue(10)));
Parameters.Add(new FixedValueParameter(SeedParameterName, "The PRNG seed value.", new IntValue()));
Parameters.Add(new FixedValueParameter(SetSeedRandomlyParameterName, "Switch to determine if the random number seed should be initialized randomly.", new BoolValue(true)));
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
// BackwardsCompatibility3.3
#region Backwards compatible code, remove with 3.4
if (!Parameters.ContainsKey(RestartsParameterName))
Parameters.Add(new FixedValueParameter(RestartsParameterName, "The number of independent random restarts", new IntValue(1)));
if (!Parameters.ContainsKey(SeedParameterName))
Parameters.Add(new FixedValueParameter(SeedParameterName, "The PRNG seed value.", new IntValue()));
if (!Parameters.ContainsKey(SetSeedRandomlyParameterName))
Parameters.Add(new FixedValueParameter(SetSeedRandomlyParameterName, "Switch to determine if the random number seed should be initialized randomly.", new BoolValue(true)));
#endregion
}
public override IDeepCloneable Clone(Cloner cloner) {
return new NonlinearRegression(this, cloner);
}
#region nonlinear regression
protected override void Run() {
if (SetSeedRandomly) Seed = (new System.Random()).Next();
var rand = new MersenneTwister((uint)Seed);
var bestSolution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, rand);
for (int r = 0; r < Restarts; r++) {
var solution = CreateRegressionSolution(Problem.ProblemData, ModelStructure, Iterations, rand);
if (solution.TrainingRootMeanSquaredError < bestSolution.TrainingRootMeanSquaredError) {
bestSolution = solution;
}
}
Results.Add(new Result(RegressionSolutionResultName, "The nonlinear regression solution.", bestSolution));
Results.Add(new Result("Root mean square error (train)", "The root of the mean of squared errors of the regression solution on the training set.", new DoubleValue(bestSolution.TrainingRootMeanSquaredError)));
Results.Add(new Result("Root mean square error (test)", "The root of the mean of squared errors of the regression solution on the test set.", new DoubleValue(bestSolution.TestRootMeanSquaredError)));
}
///
/// Fits a model to the data by optimizing the numeric constants.
/// Model is specified as infix expression containing variable names and numbers.
/// The starting point for the numeric constants is initialized randomly if a random number generator is specified (~N(0,1)). Otherwise the user specified constants are
/// used as a starting point.
/// -
/// Training and test data
/// The function as infix expression
/// Number of constant optimization iterations (using Levenberg-Marquardt algorithm)
/// Optional random number generator for random initialization of numeric constants.
///
public static ISymbolicRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData, string modelStructure, int maxIterations, IRandom random = null) {
var parser = new InfixExpressionParser();
var tree = parser.Parse(modelStructure);
if (!SymbolicRegressionConstantOptimizationEvaluator.CanOptimizeConstants(tree)) throw new ArgumentException("The optimizer does not support the specified model structure.");
// initialize constants randomly
if (random != null) {
foreach (var node in tree.IterateNodesPrefix().OfType()) {
node.Value = NormalDistributedRandom.NextDouble(random, 0, 1);
}
}
var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter();
SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, tree, problemData, problemData.TrainingIndices,
applyLinearScaling: false, maxIterations: maxIterations,
updateVariableWeights: false, updateConstantsInTree: true);
var scaledModel = new SymbolicRegressionModel(problemData.TargetVariable, tree, (ISymbolicDataAnalysisExpressionTreeInterpreter)interpreter.Clone());
scaledModel.Scale(problemData);
SymbolicRegressionSolution solution = new SymbolicRegressionSolution(scaledModel, (IRegressionProblemData)problemData.Clone());
solution.Model.Name = "Regression Model";
solution.Name = "Regression Solution";
return solution;
}
#endregion
}
}