#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 HeuristicLab.Algorithms.GradientDescent;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;
namespace HeuristicLab.Algorithms.DataAnalysis {
///
/// Base class for Gaussian process data analysis algorithms (regression and classification).
///
[StorableClass]
public abstract class GaussianProcessBase : EngineAlgorithm {
protected const string MeanFunctionParameterName = "MeanFunction";
protected const string CovarianceFunctionParameterName = "CovarianceFunction";
protected const string MinimizationIterationsParameterName = "Iterations";
protected const string ApproximateGradientsParameterName = "ApproximateGradients";
protected const string SeedParameterName = "Seed";
protected const string SetSeedRandomlyParameterName = "SetSeedRandomly";
protected const string ModelCreatorParameterName = "GaussianProcessModelCreator";
protected const string NegativeLogLikelihoodParameterName = "NegativeLogLikelihood";
protected const string HyperparameterParameterName = "Hyperparameter";
protected const string HyperparameterGradientsParameterName = "HyperparameterGradients";
protected const string SolutionCreatorParameterName = "GaussianProcessSolutionCreator";
protected const string ScaleInputValuesParameterName = "ScaleInputValues";
public new IDataAnalysisProblem Problem {
get { return (IDataAnalysisProblem)base.Problem; }
set { base.Problem = value; }
}
#region parameter properties
public IValueParameter MeanFunctionParameter {
get { return (IValueParameter)Parameters[MeanFunctionParameterName]; }
}
public IValueParameter CovarianceFunctionParameter {
get { return (IValueParameter)Parameters[CovarianceFunctionParameterName]; }
}
public IValueParameter MinimizationIterationsParameter {
get { return (IValueParameter)Parameters[MinimizationIterationsParameterName]; }
}
public IValueParameter SeedParameter {
get { return (IValueParameter)Parameters[SeedParameterName]; }
}
public IValueParameter SetSeedRandomlyParameter {
get { return (IValueParameter)Parameters[SetSeedRandomlyParameterName]; }
}
public IFixedValueParameter ScaleInputValuesParameter {
get { return (IFixedValueParameter)Parameters[ScaleInputValuesParameterName]; }
}
#endregion
#region properties
public IMeanFunction MeanFunction {
set { MeanFunctionParameter.Value = value; }
get { return MeanFunctionParameter.Value; }
}
public ICovarianceFunction CovarianceFunction {
set { CovarianceFunctionParameter.Value = value; }
get { return CovarianceFunctionParameter.Value; }
}
public int MinimizationIterations {
set { MinimizationIterationsParameter.Value.Value = value; }
get { return MinimizationIterationsParameter.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; } }
public bool ScaleInputValues {
get { return ScaleInputValuesParameter.Value.Value; }
set { ScaleInputValuesParameter.Value.Value = value; }
}
#endregion
[StorableConstructor]
protected GaussianProcessBase(bool deserializing) : base(deserializing) { }
protected GaussianProcessBase(GaussianProcessBase original, Cloner cloner)
: base(original, cloner) {
}
protected GaussianProcessBase(IDataAnalysisProblem problem)
: base() {
Problem = problem;
Parameters.Add(new ValueParameter(MeanFunctionParameterName, "The mean function to use.", new MeanConst()));
Parameters.Add(new ValueParameter(CovarianceFunctionParameterName, "The covariance function to use.", new CovarianceSquaredExponentialIso()));
Parameters.Add(new ValueParameter(MinimizationIterationsParameterName, "The number of iterations for likelihood optimization with LM-BFGS.", new IntValue(20)));
Parameters.Add(new ValueParameter(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
Parameters.Add(new ValueParameter(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
Parameters.Add(new ValueParameter(ApproximateGradientsParameterName, "Indicates that gradients should not be approximated (necessary for LM-BFGS).", new BoolValue(false)));
Parameters[ApproximateGradientsParameterName].Hidden = true; // should not be changed
Parameters.Add(new FixedValueParameter(ScaleInputValuesParameterName,
"Determines if the input variable values are scaled to the range [0..1] for training.", new BoolValue(true)));
Parameters[ScaleInputValuesParameterName].Hidden = true;
// necessary for BFGS
Parameters.Add(new ValueParameter("Maximization", new BoolValue(false)));
Parameters["Maximization"].Hidden = true;
var randomCreator = new HeuristicLab.Random.RandomCreator();
var gpInitializer = new GaussianProcessHyperparameterInitializer();
var bfgsInitializer = new LbfgsInitializer();
var makeStep = new LbfgsMakeStep();
var branch = new ConditionalBranch();
var modelCreator = new Placeholder();
var updateResults = new LbfgsUpdateResults();
var analyzer = new LbfgsAnalyzer();
var finalModelCreator = new Placeholder();
var finalAnalyzer = new LbfgsAnalyzer();
var solutionCreator = new Placeholder();
OperatorGraph.InitialOperator = randomCreator;
randomCreator.SeedParameter.ActualName = SeedParameterName;
randomCreator.SeedParameter.Value = null;
randomCreator.SetSeedRandomlyParameter.ActualName = SetSeedRandomlyParameterName;
randomCreator.SetSeedRandomlyParameter.Value = null;
randomCreator.Successor = gpInitializer;
gpInitializer.CovarianceFunctionParameter.ActualName = CovarianceFunctionParameterName;
gpInitializer.MeanFunctionParameter.ActualName = MeanFunctionParameterName;
gpInitializer.ProblemDataParameter.ActualName = Problem.ProblemDataParameter.Name;
gpInitializer.HyperparameterParameter.ActualName = HyperparameterParameterName;
gpInitializer.RandomParameter.ActualName = randomCreator.RandomParameter.Name;
gpInitializer.Successor = bfgsInitializer;
bfgsInitializer.IterationsParameter.ActualName = MinimizationIterationsParameterName;
bfgsInitializer.PointParameter.ActualName = HyperparameterParameterName;
bfgsInitializer.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
bfgsInitializer.Successor = makeStep;
makeStep.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
makeStep.PointParameter.ActualName = HyperparameterParameterName;
makeStep.Successor = branch;
branch.ConditionParameter.ActualName = makeStep.TerminationCriterionParameter.Name;
branch.FalseBranch = modelCreator;
branch.TrueBranch = finalModelCreator;
modelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
modelCreator.Successor = updateResults;
updateResults.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
updateResults.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
updateResults.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
updateResults.ApproximateGradientsParameter.ActualName = ApproximateGradientsParameterName;
updateResults.Successor = analyzer;
analyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
analyzer.PointParameter.ActualName = HyperparameterParameterName;
analyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
analyzer.StateParameter.ActualName = bfgsInitializer.StateParameter.Name;
analyzer.PointsTableParameter.ActualName = "Hyperparameter table";
analyzer.QualityGradientsTableParameter.ActualName = "Gradients table";
analyzer.QualitiesTableParameter.ActualName = "Negative log likelihood table";
analyzer.Successor = makeStep;
finalModelCreator.OperatorParameter.ActualName = ModelCreatorParameterName;
finalModelCreator.Successor = finalAnalyzer;
finalAnalyzer.QualityParameter.ActualName = NegativeLogLikelihoodParameterName;
finalAnalyzer.PointParameter.ActualName = HyperparameterParameterName;
finalAnalyzer.QualityGradientsParameter.ActualName = HyperparameterGradientsParameterName;
finalAnalyzer.PointsTableParameter.ActualName = analyzer.PointsTableParameter.ActualName;
finalAnalyzer.QualityGradientsTableParameter.ActualName = analyzer.QualityGradientsTableParameter.ActualName;
finalAnalyzer.QualitiesTableParameter.ActualName = analyzer.QualitiesTableParameter.ActualName;
finalAnalyzer.Successor = solutionCreator;
solutionCreator.OperatorParameter.ActualName = SolutionCreatorParameterName;
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
// BackwardsCompatibility3.4
#region Backwards compatible code, remove with 3.5
if (!Parameters.ContainsKey("Maximization")) {
Parameters.Add(new ValueParameter("Maximization", new BoolValue(false)));
Parameters["Maximization"].Hidden = true;
}
if (!Parameters.ContainsKey(ScaleInputValuesParameterName)) {
Parameters.Add(new FixedValueParameter(ScaleInputValuesParameterName,
"Determines if the input variable values are scaled to the range [0..1] for training.", new BoolValue(true)));
Parameters[ScaleInputValuesParameterName].Hidden = true;
}
#endregion
}
}
}