public void Build(IReadOnlyList <int> trainingRows, IReadOnlyList <int> pruningRows, IScope statescope, ResultCollection results, CancellationToken cancellationToken)
{
var regressionTreeParams = (RegressionTreeParameters)statescope.Variables[DecisionTreeRegression.RegressionTreeParameterVariableName].Value;
variables = regressionTreeParams.AllowedInputVariables.ToList();
//build tree and select node with maximum coverage
var tree = RegressionNodeTreeModel.CreateTreeModel(regressionTreeParams.TargetVariable, regressionTreeParams);
tree.BuildModel(trainingRows, pruningRows, statescope, results, cancellationToken);
var nodeModel = tree.Root.EnumerateNodes().Where(x => x.IsLeaf).MaxItems(x => x.NumSamples).First();
var satts = new List <string>();
var svals = new List <double>();
var reops = new List <Comparison>();
//extract splits
for (var temp = nodeModel; temp.Parent != null; temp = temp.Parent)
{
satts.Add(temp.Parent.SplitAttribute);
svals.Add(temp.Parent.SplitValue);
reops.Add(temp.Parent.Left == temp ? Comparison.LessEqual : Comparison.Greater);
}
Comparisons = reops.ToArray();
SplitAttributes = satts.ToArray();
SplitValues = svals.ToArray();
int np;
RuleModel = regressionTreeParams.LeafModel.BuildModel(trainingRows.Union(pruningRows).Where(r => Covers(regressionTreeParams.Data, r)).ToArray(), regressionTreeParams, cancellationToken, out np);
}
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);
}
