#region License Information /* HeuristicLab * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * and the BEACON Center for the Study of Evolution in Action. * * 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 System.Threading; using HeuristicLab.Analysis; using HeuristicLab.Algorithms.OffspringSelectionGeneticAlgorithm; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; 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; using HeuristicLab.Selection; namespace HeuristicLab.Algorithms.DataAnalysis.MctsSymbolicRegression { [Item("Gradient Boosting Machine Regression (GBM)", "Gradient boosting for any regression base learner (e.g. MCTS symbolic regression)")] [StorableClass] [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 350)] public class GradientBoostingRegressionAlgorithm : BasicAlgorithm { public override Type ProblemType { get { return typeof(IRegressionProblem); } } public new IRegressionProblem Problem { get { return (IRegressionProblem)base.Problem; } set { base.Problem = value; } } #region ParameterNames private const string IterationsParameterName = "Iterations"; private const string NuParameterName = "Nu"; private const string MParameterName = "M"; private const string RParameterName = "R"; private const string RegressionAlgorithmParameterName = "RegressionAlgorithm"; private const string SeedParameterName = "Seed"; private const string SetSeedRandomlyParameterName = "SetSeedRandomly"; private const string CreateSolutionParameterName = "CreateSolution"; private const string RegressionAlgorithmSolutionResultParameterName = "RegressionAlgorithmResult"; #endregion #region ParameterProperties public IFixedValueParameter IterationsParameter { get { return (IFixedValueParameter)Parameters[IterationsParameterName]; } } public IFixedValueParameter NuParameter { get { return (IFixedValueParameter)Parameters[NuParameterName]; } } public IFixedValueParameter RParameter { get { return (IFixedValueParameter)Parameters[RParameterName]; } } public IFixedValueParameter MParameter { get { return (IFixedValueParameter)Parameters[MParameterName]; } } // regression algorithms are currently: DataAnalysisAlgorithms, BasicAlgorithms and engine algorithms with no common interface public IConstrainedValueParameter RegressionAlgorithmParameter { get { return (IConstrainedValueParameter)Parameters[RegressionAlgorithmParameterName]; } } public IFixedValueParameter RegressionAlgorithmSolutionResultParameter { get { return (IFixedValueParameter)Parameters[RegressionAlgorithmSolutionResultParameterName]; } } public IFixedValueParameter SeedParameter { get { return (IFixedValueParameter)Parameters[SeedParameterName]; } } public FixedValueParameter SetSeedRandomlyParameter { get { return (FixedValueParameter)Parameters[SetSeedRandomlyParameterName]; } } public IFixedValueParameter CreateSolutionParameter { get { return (IFixedValueParameter)Parameters[CreateSolutionParameterName]; } } #endregion #region Properties public int Iterations { get { return IterationsParameter.Value.Value; } set { IterationsParameter.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; } } public double Nu { get { return NuParameter.Value.Value; } set { NuParameter.Value.Value = value; } } public double R { get { return RParameter.Value.Value; } set { RParameter.Value.Value = value; } } public double M { get { return MParameter.Value.Value; } set { MParameter.Value.Value = value; } } public bool CreateSolution { get { return CreateSolutionParameter.Value.Value; } set { CreateSolutionParameter.Value.Value = value; } } public IAlgorithm RegressionAlgorithm { get { return RegressionAlgorithmParameter.Value; } } public string RegressionAlgorithmResult { get { return RegressionAlgorithmSolutionResultParameter.Value.Value; } set { RegressionAlgorithmSolutionResultParameter.Value.Value = value; } } #endregion [StorableConstructor] protected GradientBoostingRegressionAlgorithm(bool deserializing) : base(deserializing) { } protected GradientBoostingRegressionAlgorithm(GradientBoostingRegressionAlgorithm original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new GradientBoostingRegressionAlgorithm(this, cloner); } public GradientBoostingRegressionAlgorithm() { Problem = new RegressionProblem(); // default problem var mctsSymbReg = new MctsSymbolicRegressionAlgorithm(); mctsSymbReg.Iterations = 10000; mctsSymbReg.StoreAlgorithmInEachRun = false; var sgp = CreateOSGP(); var regressionAlgs = new ItemSet(new IAlgorithm[] { new RandomForestRegression(), sgp, mctsSymbReg }); foreach (var alg in regressionAlgs) alg.Prepare(); Parameters.Add(new FixedValueParameter(IterationsParameterName, "Number of iterations", new IntValue(100))); Parameters.Add(new FixedValueParameter(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0))); Parameters.Add(new FixedValueParameter(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true))); Parameters.Add(new FixedValueParameter(NuParameterName, "The learning rate nu when updating predictions in GBM (0 < nu <= 1)", new DoubleValue(0.5))); Parameters.Add(new FixedValueParameter(RParameterName, "The fraction of rows that are sampled randomly for the base learner in each iteration (0 < r <= 1)", new DoubleValue(1))); Parameters.Add(new FixedValueParameter(MParameterName, "The fraction of variables that are sampled randomly for the base learner in each iteration (0 < m <= 1)", new DoubleValue(0.5))); Parameters.Add(new ConstrainedValueParameter(RegressionAlgorithmParameterName, "The regression algorithm to use as a base learner", regressionAlgs, mctsSymbReg)); Parameters.Add(new FixedValueParameter(RegressionAlgorithmSolutionResultParameterName, "The name of the solution produced by the regression algorithm", new StringValue("Solution"))); Parameters[RegressionAlgorithmSolutionResultParameterName].Hidden = true; Parameters.Add(new FixedValueParameter(CreateSolutionParameterName, "Flag that indicates if a solution should be produced at the end of the run", new BoolValue(true))); Parameters[CreateSolutionParameterName].Hidden = true; } protected override void Run(CancellationToken cancellationToken) { // Set up the algorithm if (SetSeedRandomly) Seed = new System.Random().Next(); var rand = new MersenneTwister((uint)Seed); // Set up the results display var iterations = new IntValue(0); Results.Add(new Result("Iterations", iterations)); var table = new DataTable("Qualities"); table.Rows.Add(new DataRow("Loss (train)")); table.Rows.Add(new DataRow("Loss (test)")); Results.Add(new Result("Qualities", table)); var curLoss = new DoubleValue(); var curTestLoss = new DoubleValue(); Results.Add(new Result("Loss (train)", curLoss)); Results.Add(new Result("Loss (test)", curTestLoss)); var runCollection = new RunCollection(); Results.Add(new Result("Runs", runCollection)); // init var problemData = Problem.ProblemData; var targetVarName = Problem.ProblemData.TargetVariable; var modifiableDataset = new ModifiableDataset( problemData.Dataset.VariableNames, problemData.Dataset.VariableNames.Select(v => problemData.Dataset.GetDoubleValues(v).ToList())); var trainingRows = problemData.TrainingIndices; var testRows = problemData.TestIndices; var yPred = new double[trainingRows.Count()]; var yPredTest = new double[testRows.Count()]; var y = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).ToArray(); var curY = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).ToArray(); var yTest = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices).ToArray(); var curYTest = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices).ToArray(); var nu = Nu; var mVars = (int)Math.Ceiling(M * problemData.AllowedInputVariables.Count()); var rRows = (int)Math.Ceiling(R * problemData.TrainingIndices.Count()); var alg = RegressionAlgorithm; List models = new List(); try { // Loop until iteration limit reached or canceled. for (int i = 0; i < Iterations; i++) { cancellationToken.ThrowIfCancellationRequested(); modifiableDataset.RemoveVariable(targetVarName); modifiableDataset.AddVariable(targetVarName, curY.Concat(curYTest)); SampleTrainingData(rand, modifiableDataset, rRows, problemData.Dataset, curY, problemData.TargetVariable, problemData.TrainingIndices); // all training indices from the original problem data are allowed var modifiableProblemData = new RegressionProblemData(modifiableDataset, problemData.AllowedInputVariables.SampleRandomWithoutRepetition(rand, mVars), problemData.TargetVariable); modifiableProblemData.TrainingPartition.Start = 0; modifiableProblemData.TrainingPartition.End = rRows; modifiableProblemData.TestPartition.Start = problemData.TestPartition.Start; modifiableProblemData.TestPartition.End = problemData.TestPartition.End; if (!TrySetProblemData(alg, modifiableProblemData)) throw new NotSupportedException("The algorithm cannot be used with GBM."); IRegressionModel model; IRun run; // try to find a model. The algorithm might fail to produce a model. In this case we just retry until the iterations are exhausted if (TryExecute(alg, RegressionAlgorithmResult, out model, out run)) { int row = 0; // update predictions for training and test // update new targets (in the case of squared error loss we simply use negative residuals) foreach (var pred in model.GetEstimatedValues(problemData.Dataset, trainingRows)) { yPred[row] = yPred[row] + nu * pred; curY[row] = y[row] - yPred[row]; row++; } row = 0; foreach (var pred in model.GetEstimatedValues(problemData.Dataset, testRows)) { yPredTest[row] = yPredTest[row] + nu * pred; curYTest[row] = yTest[row] - yPredTest[row]; row++; } // determine quality OnlineCalculatorError error; var trainR = OnlinePearsonsRCalculator.Calculate(yPred, y, out error); var testR = OnlinePearsonsRCalculator.Calculate(yPredTest, yTest, out error); // iteration results curLoss.Value = error == OnlineCalculatorError.None ? trainR * trainR : 0.0; curTestLoss.Value = error == OnlineCalculatorError.None ? testR * testR : 0.0; models.Add(model); } runCollection.Add(run); table.Rows["Loss (train)"].Values.Add(curLoss.Value); table.Rows["Loss (test)"].Values.Add(curTestLoss.Value); iterations.Value = i + 1; } // produce solution if (CreateSolution) { // when all our models are symbolic models we can easily combine them to a single model if (models.All(m => m is ISymbolicRegressionModel)) { Results.Add(new Result("Solution", CreateSymbolicSolution(models, Nu, (IRegressionProblemData)problemData.Clone()))); } // just produce an ensemble solution for now (TODO: correct scaling or linear regression for ensemble model weights) Results.Add(new Result("EnsembleSolution", new RegressionEnsembleSolution(models, (IRegressionProblemData)problemData.Clone()))); } } finally { // reset everything alg.Prepare(true); } } private IAlgorithm CreateOSGP() { // configure strict osgp var alg = new OffspringSelectionGeneticAlgorithm.OffspringSelectionGeneticAlgorithm(); var prob = new SymbolicRegressionSingleObjectiveProblem(); prob.MaximumSymbolicExpressionTreeDepth.Value = 7; prob.MaximumSymbolicExpressionTreeLength.Value = 15; alg.Problem = prob; alg.SuccessRatio.Value = 1.0; alg.ComparisonFactorLowerBound.Value = 1.0; alg.ComparisonFactorUpperBound.Value = 1.0; alg.MutationProbability.Value = 0.15; alg.PopulationSize.Value = 200; alg.MaximumSelectionPressure.Value = 100; alg.MaximumEvaluatedSolutions.Value = 20000; alg.SelectorParameter.Value = alg.SelectorParameter.ValidValues.OfType().First(); alg.MutatorParameter.Value = alg.MutatorParameter.ValidValues.OfType().First(); alg.StoreAlgorithmInEachRun = false; return alg; } private void SampleTrainingData(MersenneTwister rand, ModifiableDataset ds, int rRows, IDataset sourceDs, double[] curTarget, string targetVarName, IEnumerable trainingIndices) { var selectedRows = trainingIndices.SampleRandomWithoutRepetition(rand, rRows).ToArray(); int t = 0; object[] srcRow = new object[ds.Columns]; var varNames = ds.DoubleVariables.ToArray(); foreach (var r in selectedRows) { // take all values from the original dataset for (int c = 0; c < srcRow.Length; c++) { var col = sourceDs.GetReadOnlyDoubleValues(varNames[c]); srcRow[c] = col[r]; } ds.ReplaceRow(t, srcRow); // but use the updated target values ds.SetVariableValue(curTarget[r], targetVarName, t); t++; } } private static ISymbolicRegressionSolution CreateSymbolicSolution(List models, double nu, IRegressionProblemData problemData) { var symbModels = models.OfType(); var lowerLimit = symbModels.Min(m => m.LowerEstimationLimit); var upperLimit = symbModels.Max(m => m.UpperEstimationLimit); var interpreter = new SymbolicDataAnalysisExpressionTreeLinearInterpreter(); var progRootNode = new ProgramRootSymbol().CreateTreeNode(); var startNode = new StartSymbol().CreateTreeNode(); var addNode = new Addition().CreateTreeNode(); var mulNode = new Multiplication().CreateTreeNode(); var scaleNode = (ConstantTreeNode)new Constant().CreateTreeNode(); // all models are scaled using the same nu scaleNode.Value = nu; foreach (var m in symbModels) { var relevantPart = m.SymbolicExpressionTree.Root.GetSubtree(0).GetSubtree(0); // skip root and start addNode.AddSubtree((ISymbolicExpressionTreeNode)relevantPart.Clone()); } mulNode.AddSubtree(addNode); mulNode.AddSubtree(scaleNode); startNode.AddSubtree(mulNode); progRootNode.AddSubtree(startNode); var t = new SymbolicExpressionTree(progRootNode); var combinedModel = new SymbolicRegressionModel(t, interpreter, lowerLimit, upperLimit); var sol = new SymbolicRegressionSolution(combinedModel, problemData); return sol; } private static bool TrySetProblemData(IAlgorithm alg, IRegressionProblemData problemData) { var prob = alg.Problem as IRegressionProblem; // there is already a problem and it is compatible -> just set problem data if (prob != null) { prob.ProblemDataParameter.Value = problemData; return true; } else return false; } private static bool TryExecute(IAlgorithm alg, string regressionAlgorithmResultName, out IRegressionModel model, out IRun run) { model = null; using (var wh = new AutoResetEvent(false)) { EventHandler> handler = (sender, args) => wh.Set(); EventHandler handler2 = (sender, args) => wh.Set(); alg.ExceptionOccurred += handler; alg.Stopped += handler2; try { alg.Prepare(); alg.Start(); wh.WaitOne(); run = alg.Runs.Last(); var sols = alg.Results.Select(r => r.Value).OfType(); if (!sols.Any()) return false; var sol = sols.First(); if (sols.Skip(1).Any()) { // more than one solution => use regressionAlgorithmResult if (alg.Results.ContainsKey(regressionAlgorithmResultName)) { sol = (IRegressionSolution)alg.Results[regressionAlgorithmResultName].Value; } } var symbRegSol = sol as SymbolicRegressionSolution; // only accept symb reg solutions that do not hit the estimation limits // NaN evaluations would not be critical but are problematic if we want to combine all symbolic models into a single symbolic model if (symbRegSol == null || (symbRegSol.TrainingLowerEstimationLimitHits == 0 && symbRegSol.TrainingUpperEstimationLimitHits == 0 && symbRegSol.TestLowerEstimationLimitHits == 0 && symbRegSol.TestUpperEstimationLimitHits == 0) && symbRegSol.TrainingNaNEvaluations == 0 && symbRegSol.TestNaNEvaluations == 0) { model = sol.Model; } } finally { alg.ExceptionOccurred -= handler; alg.Stopped -= handler2; } } return model != null; } } }