private static IScope InitializeScope(IRandom random, IRegressionProblemData problemData, IPruning pruning, int minLeafSize, ILeafModel leafModel, ISplitter splitter, bool generateRules, bool useHoldout, double holdoutSize) { var stateScope = new Scope("RegressionTreeStateScope"); //reduce RegressionProblemData to AllowedInput & Target column wise and to TrainingSet row wise var doubleVars = new HashSet <string>(problemData.Dataset.DoubleVariables); var vars = problemData.AllowedInputVariables.Concat(new[] { problemData.TargetVariable }).ToArray(); if (vars.Any(v => !doubleVars.Contains(v))) { throw new NotSupportedException("Decision tree regression supports only double valued input or output features."); } var doubles = vars.Select(v => problemData.Dataset.GetDoubleValues(v, problemData.TrainingIndices).ToArray()).ToArray(); if (doubles.Any(v => v.Any(x => double.IsNaN(x) || double.IsInfinity(x)))) { throw new NotSupportedException("Decision tree regression does not support NaN or infinity values in the input dataset."); } var trainingData = new Dataset(vars, doubles); var pd = new RegressionProblemData(trainingData, problemData.AllowedInputVariables, problemData.TargetVariable); pd.TrainingPartition.End = pd.TestPartition.Start = pd.TestPartition.End = pd.Dataset.Rows; pd.TrainingPartition.Start = 0; //store regression tree parameters var regressionTreeParams = new RegressionTreeParameters(pruning, minLeafSize, leafModel, pd, random, splitter); stateScope.Variables.Add(new Variable(RegressionTreeParameterVariableName, regressionTreeParams)); //initialize tree operators pruning.Initialize(stateScope); splitter.Initialize(stateScope); leafModel.Initialize(stateScope); //store unbuilt model IItem model; if (generateRules) { model = RegressionRuleSetModel.CreateRuleModel(problemData.TargetVariable, regressionTreeParams); RegressionRuleSetModel.Initialize(stateScope); } else { model = RegressionNodeTreeModel.CreateTreeModel(problemData.TargetVariable, regressionTreeParams); } stateScope.Variables.Add(new Variable(ModelVariableName, model)); //store training & pruning indices IReadOnlyList <int> trainingSet, pruningSet; GeneratePruningSet(pd.TrainingIndices.ToArray(), random, useHoldout, holdoutSize, out trainingSet, out pruningSet); stateScope.Variables.Add(new Variable(TrainingSetVariableName, new IntArray(trainingSet.ToArray()))); stateScope.Variables.Add(new Variable(PruningSetVariableName, new IntArray(pruningSet.ToArray()))); return(stateScope); }
public static void UpdateModel(IDecisionTreeModel model, IRegressionProblemData problemData, IRandom random, ILeafModel leafModel, CancellationToken?cancellationToken = null) { if (cancellationToken == null) { cancellationToken = CancellationToken.None; } var regressionTreeParameters = new RegressionTreeParameters(leafModel, problemData, random); var scope = new Scope(); scope.Variables.Add(new Variable(RegressionTreeParameterVariableName, regressionTreeParameters)); leafModel.Initialize(scope); model.Update(problemData.TrainingIndices.ToList(), scope, cancellationToken.Value); }